Laser-Induced Breakdown Spectroscopy for Contaminated Water Screening: Principles, Applications, and Analytical Advancements

Connor Hughes Nov 27, 2025 144

This article provides a comprehensive overview of Laser-Induced Breakdown Spectroscopy (LIBS) as a rapid, versatile tool for contaminated water screening.

Laser-Induced Breakdown Spectroscopy for Contaminated Water Screening: Principles, Applications, and Analytical Advancements

Abstract

This article provides a comprehensive overview of Laser-Induced Breakdown Spectroscopy (LIBS) as a rapid, versatile tool for contaminated water screening. It covers the fundamental principles of LIBS technology, from plasma generation to spectral analysis, and details its methodological applications for detecting heavy metals, nutrients, and other pollutants in various water bodies. The content explores advanced optimization strategies to enhance sensitivity and repeatability, including signal processing and matrix effect mitigation. Furthermore, it presents a critical validation of LIBS performance through comparative studies with established techniques like ICP-MS, underscoring its suitability for real-time, on-site environmental monitoring. Aimed at researchers and environmental professionals, this review synthesizes recent advancements to highlight LIBS's transformative potential in water quality assessment and environmental protection.

Understanding LIBS Technology: Core Principles and Advantages for Water Analysis

The Basic Components and Setup of a LIBS System

Laser-Induced Breakdown Spectroscopy (LIBS) is a rapid analytical technique used to determine the elemental composition of materials. The method involves using a high-powered, pulsed laser to excite a small amount of the sample material, creating a transient plasma. As this plasma cools, the excited atoms and ions within it emit light at characteristic wavelengths. By analyzing this emitted light, the elemental composition of the sample can be identified and quantified [1] [2]. LIBS is particularly valuable for its capability for real-time, in-situ analysis with minimal sample preparation, making it applicable across various fields including environmental monitoring, industrial process control, and material science [3] [4]. In the specific context of contaminated water screening, LIBS offers the potential for rapid on-site detection of heavy metals and other pollutants, though it presents distinct challenges compared to solid sample analysis.

Fundamental Physics of LIBS

The LIBS process encompasses two main physical phenomena: ablation and plasma formation and evolution.

Ablation and Plasma Formation

A high-energy laser pulse is focused onto a sample, leading to the vaporization and ionization of a microscopic amount of material, thus forming a high-temperature plasma plume (often exceeding 50,000 K initially) [1]. For nanosecond-class lasers, this process is thermally dominated, causing melting and explosive boiling of the sample. The laser pulse's trailing energy is absorbed by this ejected material, sustaining and heating the plasma above the sample surface [1].

Plasma Evolution and Light Emission

The generated plasma is initially in a state of severe disequilibrium, emitting intense, featureless Bremsstrahlung continuum radiation. As the plasma rapidly expands and cools (within microseconds), it approaches Local Thermodynamic Equilibrium (LTE). At this stage, atoms and ions de-excite, emitting light at specific wavelengths characteristic of the elements present [1]. The detection system is typically time-gated to collect light after the initial continuum background has diminished, significantly improving the signal-to-noise ratio for the elemental emission lines [1] [4].

Core Components of a LIBS System

A typical LIBS setup consists of several key hardware components that work in synchrony, as illustrated in Figure 1.

Pulsed Laser Source

The laser serves as the excitation source and is a critical determinant of system performance.

  • Laser Type: Q-switched Nd:YAG lasers are the most common, typically operating at their fundamental wavelength of 1064 nm or at harmonics (532 nm, 355 nm, or 266 nm) [1].
  • Pulse Duration: Generally in the nanosecond (ns) regime, though femtosecond lasers offer different ablation characteristics [1].
  • Key Parameters: Pulse energy (often up to hundreds of mJ), repetition rate (e.g., 1-100 Hz), wavelength, and beam quality are crucial design considerations [1] [4].
Optics and Sample Chamber

Optical components guide and focus the laser light and collect the emitted plasma light.

  • Focusing Lens: A lens focuses the laser beam onto the sample surface to achieve the high power density (> GW/cm²) required for plasma generation [1] [4].
  • Collection Optics: A lens or telescope system collects the plasma emission light and couples it into an optical fiber or directly into the spectrometer [4].
  • Sample Chamber/Stage: A chamber may be used to control the atmospheric conditions around the sample, which can significantly influence the plasma [3]. For inline monitoring or liquid analysis, specialized flow cells or open-path configurations are used [5] [6].
Spectrometer and Detector

This subsystem is responsible for dispersing the collected light and measuring its intensity as a function of wavelength.

  • Spectrometer: Disperses the collected plasma light. Echelle spectrographs are highly effective for LIBS as they provide a wide spectral range (e.g., 200-975 nm) simultaneously while maintaining high resolution [7] [4].
  • Detector: Converts the optical signal into an electrical one. Intensified CCD (ICCD) cameras are often preferred because they can be electronically gated with nanosecond precision. This allows data acquisition to be delayed until after the initial continuum emission has decayed, vastly improving analytical performance [1] [4]. Other detectors like standard CCDs and EMCCDs are also used.
Synchronization and Data System

A pulse generator or digital delay generator is essential for synchronizing the laser firing, detector gating, and any other time-critical components [4]. A computer system controls the hardware, acquires the spectra, and runs software for data processing and quantitative analysis.

Table 1: Core Components of a Typical LIBS System

Component Category Specific Examples & Key Parameters Primary Function
Pulsed Laser Q-switched Nd:YAG; Wavelength (1064, 532, 355, 266 nm), Pulse Energy (mJ to 100s of mJ), Pulse Duration (ns) Generates a high-power pulse to ablate sample and form plasma
Focusing Optics Plano-convex lens (e.g., f=75 mm) Focuses laser to a small spot for high power density
Light Collection & Delivery Collection lens, fiber optic cable Collects plasma emission light and delivers it to the spectrometer
Spectrometer Echelle spectrograph (e.g., 200-975 nm range), Czerny-Turner Disperses the collected light into its constituent wavelengths
Detector Gated ICCD, CCD, EMCCD; Gate width/delay (ns to µs) Measures intensity of light at different wavelengths; gating rejects early continuum
Synchronization Pulse/Delay Generator (e.g., Stanford DG535) Precisely synchronizes laser firing and detector gating

LIBS_Setup Computer Computer & Software (Control, Data Acquisition, Analysis) Laser Pulsed Laser Source (e.g., Nd:YAG) Computer->Laser Spectrometer Spectrometer Computer->Spectrometer Detector Gated Detector (e.g., ICCD) Computer->Detector Sync Pulse Generator (Synchronization) Computer->Sync FocusOptics Focusing Optics (Lens) Laser->FocusOptics Sample Sample FocusOptics->Sample Plasma Laser-Induced Plasma Sample->Plasma CollectOptics Collection Optics & Fiber Plasma->CollectOptics CollectOptics->Spectrometer Spectrometer->Detector Sync->Laser Sync->Detector

Figure 1: A schematic diagram illustrating the logical relationships and data flow between the core components of a typical LIBS system. The pulse generator ensures precise synchronization between the laser and the detector.

Special Considerations for Contaminated Water Screening

Applying LIBS directly to water analysis presents specific challenges, primarily due to the rapid dissipation of laser energy in the liquid, splashing, and the reduced plasma temperature and lifetime compared to solids. This often results in poorer signal intensity and higher limits of detection [6].

IEC-LIBS: An Advanced Method for Trace Metal Detection in Water

A novel method combining an Ion Enrichment Chip (IEC) with LIBS has been developed to overcome these limitations for detecting trace heavy metals like chromium in water [6].

  • Principle: The IEC chip features a millimetric channel used to separate and enrich total chromium and the highly toxic Cr(VI) from the water sample onto a solid substrate. This pre-concentration step is crucial for detecting trace concentrations.
  • Analysis: The enriched substrate is then analyzed using a standard LIBS system. This approach combines the separation and enrichment capabilities of IEC with the rapid, elemental analysis power of LIBS.
  • Performance: This method has demonstrated detection limits as low as 10 μg/L for total Cr and 4 μg/L for Cr(VI) in water, making it a sensitive, rapid, and environmentally friendly technique compliant with environmental quality standards [6].
Alternative Sample Presentation Methods

Other common strategies for analyzing liquids with LIBS include:

  • Liquid Jet: Creating a stable jet of the liquid sample for analysis.
  • Flow Cell: Containing the water in a cell with a transparent window for the laser.
  • Solid Substrate Deposition: Depositing and drying the water sample onto a filter or substrate, thus converting the analysis to a solid matrix.

Experimental Protocol for Water Screening via IEC-LIBS

The following provides a detailed methodology for detecting chromium in water using the IEC-LIBS technique, adaptable for other metal contaminants.

Reagents and Materials

Table 2: Research Reagent Solutions and Essential Materials

Reagent/Material Specification/Purpose Function in the Protocol
Water Samples Environmental water (e.g., pool, river, wastewater); spiked samples for validation The analyte of interest, containing trace levels of chromium.
Ion Enrichment Chip (IEC) Lab-on-a-chip device with millimetric channel To separate and pre-concentrate chromium from the liquid sample onto a solid phase.
Nitric Acid (HNO₃) High purity, for sample preservation and digestion Used in sample pre-treatment to digest organics and maintain metal ions in solution.
Calibration Standards Certified reference materials with known Cr concentrations For constructing the calibration model to quantify Cr in unknown samples.
Solid Substrate Appropriate filter material compatible with the IEC The surface onto which chromium is enriched and subsequently analyzed by LIBS.
Step-by-Step Procedure

Step 1: Sample Pre-treatment and Preparation

  • Collect water samples in clean containers.
  • Acidify samples with high-purity nitric acid to a pH of ~2 to dissolve particulates and prevent adsorption of metals onto container walls.
  • Prepare a series of calibration standards from certified stock solutions covering the expected concentration range (e.g., 0-500 μg/L).

Step 2: Ion Enrichment and Separation

  • Set up the IEC system according to the manufacturer's specifications.
  • Pass a known volume of the water sample (or standard) through the IEC chip's channel. The chip is designed to selectively separate and enrich total chromium or specific valence states (like Cr(VI)) onto the integrated solid substrate [6].
  • After enrichment, carefully remove the substrate containing the concentrated chromium for LIBS analysis.

Step 3: LIBS Spectral Acquisition

  • Place the enriched substrate securely in the LIBS sample chamber.
  • Set the LIBS instrument parameters. Typical acquisition parameters derived from literature are summarized in Table 3.
  • Focus the laser beam onto the enriched spot on the substrate.
  • Acquire spectra by accumulating multiple laser shots (e.g., 20-30 shots) per sample location to improve the signal-to-noise ratio. Collect spectra from several locations on the substrate to account for heterogeneity [7] [8].

Table 3: Exemplary LIBS Acquisition Parameters for Solid Substrate Analysis

Parameter Exemplary Setting Rationale & Comment
Laser Wavelength 1064 nm (Nd:YAG) or 532 nm Fundamental wavelength; harmonics can offer better coupling with some substrates.
Pulse Energy 30 - 100 mJ Sufficient to ablate the substrate material and excite the enriched analyte.
Laser Repetition Rate 1 - 20 Hz Standard range; lower rates may prevent overheating the substrate.
Gate Delay 0.5 - 1.5 μs Allows the initial continuum background to decay before measurement [1] [4].
Gate Width 1 - 10 μs Captures the atomic emission signal while excluding later noise.
Number of Spectra 20-30 shots per location, multiple locations Averages out pulse-to-pulse fluctuations and samples the enriched area representatively.

Step 4: Data Analysis and Quantification

  • Pre-processing: Process raw spectra by subtracting the instrumental background and correcting the baseline. Normalize spectra to an internal standard or a carbon line from the substrate to minimize shot-to-shot signal fluctuations [3] [7] [9].
  • Variable Selection: For multivariate analysis, employ variable selection methods like the stable variable selection based on VSC-mIPW-PLS to identify the most stable and informative spectral lines (e.g., Cr I/Cr II lines) and reduce model complexity [8].
  • Model Building: Build a calibration model using chemometric techniques such as Partial Least Squares Regression (PLSR) or Support Vector Regression (SVR). Use the pre-processed spectra from the calibration standards and their known concentrations as inputs [7] [9].
  • Validation & Prediction: Validate the model using a separate set of validation samples or through cross-validation. Once validated, use the model to predict the chromium concentration in unknown water samples based on their LIBS spectra.

WaterScreeningProtocol SamplePrep Sample Preparation (Acidification, Filtration) IonEnrichment Ion Enrichment (IEC) Separate & pre-concentrate Cr onto solid substrate SamplePrep->IonEnrichment CalStandards Prepare Calibration Standards CalStandards->IonEnrichment LIBSacquisition LIBS Spectral Acquisition Ablation of enriched substrate & spectrum collection IonEnrichment->LIBSacquisition Preprocessing Spectral Pre-processing (Background subtraction, normalization, denoising) LIBSacquisition->Preprocessing VariableSelection Variable Selection (Identify stable Cr lines) Preprocessing->VariableSelection Modeling Chemometric Modeling (PLSR, SVR calibration) VariableSelection->Modeling Prediction Concentration Prediction in unknown samples Modeling->Prediction

Figure 2: Experimental workflow for screening chromium in water using the IEC-LIBS method, from sample preparation to final quantitative prediction.

Advanced Data Analysis and Chemometrics

The inherent variability in LIBS signals due to matrix effects and fluctuating experimental parameters makes robust data analysis crucial for accurate quantification [3].

Pre-processing and Variable Selection
  • Spectral Pre-processing: Techniques like local spectral normalization (dividing a peak area by the integral of a local waveband) can dramatically improve the correlation between signal and concentration. For instance, one study showed the R² for carbon improved from 0.0167 to 0.7892 after such normalization [9].
  • Variable Selection: Selecting the most stable and informative emission lines is critical. Advanced methods like Variable Stability Correction with mIPW-PLS (VSC-mIPW-PLS) have been developed to automatically select variables that provide robust quantification across different sample sets and partitions, enhancing model adaptability and accuracy [8].
Multivariate Calibration Techniques

Unlike univariate calibration which relies on a single emission line, multivariate methods use spectral information from multiple lines or even the entire spectrum, leading to more accurate and robust models [7] [9] [8].

Table 4: Comparison of Common Calibration Techniques for LIBS

Calibration Method Principle Advantages / Typical Use-Cases
Standard Calibration Curve (SCC) Univariate model relating intensity of a single emission line to concentration. Simple, intuitive; can be effective for a single element in a simple, consistent matrix.
Partial Least Squares Regression (PLSR) Multivariate model that projects spectral data and concentrations into latent variables, maximizing covariance. Handles multicollinearity well; widely used for its robustness and good performance; faster training than some ML models [7] [9].
Support Vector Regression (SVR) A machine learning method that maps data to a high-dimensional feature space to find a regression function. Can model complex, non-linear relationships; often shows high prediction accuracy and precision [9].
Artificial Neural Network (ANN) A non-linear machine learning model inspired by biological neural networks. Powerful for capturing complex patterns; can offer excellent prediction accuracy but may require more data and computational resources [7].

Studies have consistently shown that multivariate methods outperform univariate calibration. For example, in quantifying NaCl in bakery products, PLSR increased the R² value from 0.788 to 0.943 for commercial products compared to the standard calibration curve [7]. Similarly, for carbon content analysis in coal, SVR and ANN (BP) models provided superior prediction accuracy and precision compared to PLSR and Random Forest models [9].

The Physics of Plasma Generation and Atomic Emission

Fundamental Principles

Laser-Induced Breakdown Spectroscopy (LIBS) is a rapid, multi-elemental analytical technique based on atomic emission spectrometry that can analyze all elements in solid, liquid, gas, and aerosol samples [10]. The fundamental process involves using a high-energy laser pulse to ablate a minute amount of material from a sample surface, creating a transient, highly luminous plasma. Within this hot plasma, the ejected material is dissociated into excited ionic and atomic species. As these excited particles revert to lower energy states, they emit characteristic optical radiation [10] [11]. The detection and spectral analysis of this emitted light yields precise information about the elemental composition of the material.

The LIBS process can be summarized in four key steps [11]:

  • Laser Ablation: A high-power pulsed laser (e.g., a Q-switched Nd:YAG) is focused onto the sample surface, ejecting a tiny volume of material (picograms to nanograms).
  • Plasma Formation and Expansion: The ablated material is instantaneously ionized, forming a high-temperature, high-density plasma that rapidly expands.
  • Plasma Emission: As the plasma cools, the excited ions and atoms emit light at wavelengths characteristic of the elements present.
  • Spectral Analysis: A spectrometer and detector system records the emitted light spectrum, which is analyzed for qualitative and quantitative elemental composition.

Application in Contaminated Water Screening

The application of LIBS for screening contaminated water, particularly for heavy metals and hardness ions, is a critical advancement in environmental monitoring [12]. Traditional methods for detecting elements like calcium (Ca), magnesium (Mg), and heavy metals in water—such as Atomic Absorption Spectrometry (AAS) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS)—often require lengthy detection cycles, complex sample preparation, and laboratory settings, making real-time field monitoring impractical [13] [12].

LIBS offers a compelling alternative with minimal or no sample pre-treatment, fast operation, a chemical-free process, and the potential for portable, on-site analysis [12]. A recent study developed a portable LIBS system with a miniaturized spectrometer and a liquid jet device for direct analysis of surface water, enabling rapid, in-situ detection of calcium and magnesium as key indicators of water hardness [13]. This system achieved low detection limits (11.58 mg/L for Ca and 2.57 mg/L for Mg) and high recovery rates (90.83%–108.74%), demonstrating its feasibility for accurate field-based water quality assessment [13].

However, LIBS detection for liquid samples presents challenges, including lower sensitivity compared to solid samples and signal instability due to surface ripples and splashing [12]. Various approaches have been developed to improve sensitivity, such as converting the liquid into a solid substrate, using liquid jets, aerosol generation, and surface-enhanced techniques with nanoparticles [12].

Table 1: LIBS Detection Performance for Key Water Contaminants

Target Element Application Context Achieved Limit of Detection (LOD) Sample Introduction Method
Calcium (Ca) Surface Water Hardness [13] 11.58 mg/L Liquid Jet (0.64 mm diameter)
Magnesium (Mg) Surface Water Hardness [13] 2.57 mg/L Liquid Jet (0.64 mm diameter)
Heavy Metals Water Quality Monitoring [12] Varies by element and method Liquid-to-Solid Conversion, Aerosols, Nanoparticle Enhancement

Experimental Protocols

Protocol: On-Site Detection of Ca and Mg in Surface Water Using a Portable LIBS System

This protocol is adapted from the method developed by Ma et al. for direct, on-site analysis of surface water [13].

1. Equipment and Reagents

  • Portable LIBS Instrument: Equipped with a pulsed laser (e.g., Q-switched Nd:YAG), a miniaturized spectrometer, and a liquid jet assembly.
  • Liquid Jet System: For stable sample introduction.
  • Sample Vials: For collecting and storing water samples from rivers, ponds, or other surface sources.
  • Standard Solutions: (Optional) For calibration, prepared with known concentrations of Ca and Mg.

2. Sample Collection and Preparation

  • Collect water samples in clean vials from the desired monitoring points.
  • If necessary, filter samples to remove large particulate matter that could clog the jet system. No other pre-treatment is required.

3. Instrument Setup and Optimization

  • Liquid Jet Configuration: Set the jet stream diameter to 0.64 mm for stable signal acquisition [13].
  • Laser Alignment: Position the laser ablation point 5 mm from the jet outlet [13].
  • Spectral Acquisition Parameters: Set the spectrometer to accumulate and average a minimum of 20 individual spectra per measurement point to enhance signal stability and reduce the relative standard deviation (RSD) [13].

4. Measurement and Data Acquisition

  • Introduce the water sample through the liquid jet system.
  • Fire the laser pulses to generate plasma on the liquid jet stream.
  • Acquire the emission spectra, ensuring the system records and averages multiple spectra as set in the parameters.

5. Data Analysis

  • Identify the characteristic emission lines for Calcium (Ca) and Magnesium (Mg) in the collected spectra.
  • For quantitative analysis, use a pre-established calibration curve relating emission line intensity to element concentration.
  • Calculate the concentration of Ca and Mg in the unknown samples based on the calibration curve and the recorded signal intensities.
Workflow Visualization

The following diagram illustrates the logical workflow for a LIBS experiment for water screening, from sample introduction to data analysis.

LIBS_Workflow Start Start: Water Sample SampleIntro Sample Introduction (Liquid Jet, λ = 0.64 mm) Start->SampleIntro LaserAblation Laser Ablation (Focused Pulsed Laser) SampleIntro->LaserAblation PlasmaGen Plasma Generation (High-Temperature Microplasma) LaserAblation->PlasmaGen LightEmission Atomic Emission (Characteristic Light) PlasmaGen->LightEmission SignalDetect Signal Detection (Spectrometer & Detector) LightEmission->SignalDetect DataProcess Data Processing (Spectral Analysis & Quantification) SignalDetect->DataProcess Result Result: Elemental Composition DataProcess->Result

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful implementation of LIBS for water screening relies on a set of key components and materials.

Table 2: Essential Materials for LIBS-Based Water Analysis

Item Function/Description Application Note
Pulsed Laser System High-energy source (e.g., Nd:YAG) for sample ablation and plasma generation. Laser parameters (wavelength, energy, pulse duration) significantly affect plasma properties and detection sensitivity [12].
Spectrometer Device for dispersing and resolving the light emitted by the plasma into its constituent wavelengths. Portable systems use miniaturized spectrometers for field deployment [13]. Echelle spectrographs provide high resolution [10].
Detector (ICCD/CCD) Intensified or standard Charge-Coupled Device to capture the resolved spectrum with high sensitivity. Gating the detector to collect light after the initial plasma continuum improves signal-to-noise ratio [10].
Liquid Introduction System A method to present the liquid sample to the laser in a stable and reproducible manner. A liquid jet (Ø 0.64 mm) provides stability over a static liquid surface, reducing splashing and signal fluctuation [13].
Filter Membranes For pre-concentration of trace elements via liquid-to-solid conversion [12]. Filtering a large volume of water deposits the analyte on a solid substrate, significantly improving the Limit of Detection for heavy metals.
Metallic Nanoparticles Used for surface-enhanced LIBS (SENLIBS) to amplify the emission signal [12]. Nanoparticles (e.g., PtAg) deposited on a filter substrate can enhance the signal of adsorbed heavy metal ions.

Critical Parameters and Data Processing

The analytical performance of LIBS is governed by several critical parameters. Key laser properties include the source wavelength, energy density, and spot size, all of which directly influence plasma characteristics and the resulting spectral signal [12]. For liquid analysis, the method of sample introduction is paramount; a stable liquid jet configuration has been shown to optimize signal stability [13].

LIBS generates vast amounts of spectral data, making robust data processing crucial. Qualitative analysis involves identifying elements by matching observed emission peaks to known characteristic spectral lines from databases. Quantitative analysis determines element concentrations, often using calibration curves (CC-LIBS) that plot the intensity of an element's emission line against its concentration in standard samples [11]. For complex biological or environmental samples, advanced statistical methods and machine learning classifiers (e.g., quadratic classifiers) are employed to optimally discriminate and classify spectra based on elemental composition [10].

Plasma Dynamics Visualization

The underlying physics of plasma generation and light emission in LIBS involves a sequence of complex interactions.

LIBS_Physics LaserPulse Focused Laser Pulse SampleInteraction Laser-Matter Interaction LaserPulse->SampleInteraction Ablation Ablation & Vaporization (Tiny sample volume ejected) SampleInteraction->Ablation PlasmaFormation Plasma Formation (~10,000 K, containing excited atoms and ions) Ablation->PlasmaFormation PlasmaCooling Plasma Expansion & Cooling PlasmaFormation->PlasmaCooling AtomicEmission Atomic De-excitation (Emission of characteristic photons) PlasmaCooling->AtomicEmission SpectralLines Unique Spectral Lines (Elemental Fingerprint) AtomicEmission->SpectralLines

Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as a powerful analytical technique for the rapid screening of contaminated water, offering distinct advantages that align perfectly with the needs of modern environmental research. This technique operates on the principle of using a highly focused, short laser pulse to vaporize a minute amount of material from a sample, creating a transient micro-plasma. As this plasma cools, the excited atoms and ions emit light at characteristic wavelengths, producing a unique elemental fingerprint for the sample [14] [15]. The analysis of this emitted light allows for the identification and quantification of elemental composition with high sensitivity and spatial resolution [15]. For research focused on detecting hazardous pollutants in water sources, LIBS presents a compelling alternative to traditional methods, primarily due to its rapid analysis time, minimal sample preparation requirements, and ability to detect multiple elements simultaneously [16]. This application note details these core advantages within the context of a structured experimental protocol for screening heavy metals in surface water, providing researchers with a clear framework for method implementation.

Core Advantages in Water Screening

The application of LIBS to water quality analysis is driven by three fundamental characteristics that collectively enhance research efficiency and capability. Table 1 provides a quantitative summary of these key advantages, which are further elaborated in the subsequent sections.

Table 1: Key Advantages of LIBS for Contaminated Water Screening

Advantage Key Metric/Feature Impact on Water Screening Research
Speed Analysis time: seconds per measurement [15] Enables high-throughput screening and real-time, in-situ decision-making [14].
Minimal Sample Prep Direct analysis of liquid samples; often no filtration or chemical reagents needed [16]. Reduces analysis time and cost; minimizes potential sample contamination or loss [14].
Multi-Element Capability Detection of nearly every element in the periodic table from a single laser pulse [14]. Simultaneous detection of macronutrients and hazardous metals (e.g., Hg, Pb, Cd, Fe) in a single spectrum [16].

Speed and High-Throughput Capability

The LIBS process, from laser ablation to spectral acquisition, is exceptionally fast. The plasma emission itself lasts only microseconds, enabling near-real-time analysis once the signal is processed [14]. This rapid turnaround allows researchers to obtain results "usually within seconds for a single spot analysis" [15]. In practice, this means a high volume of samples can be screened in a short period, a critical factor for large-scale environmental monitoring or during pollution events where timely response is essential. Furthermore, the technique's speed supports the creation of detailed elemental maps or the monitoring of dynamic processes.

Minimal Sample Preparation

A significant bottleneck in traditional water analysis is extensive sample preparation. LIBS dramatically simplifies this workflow. A recent study on contaminated surface water in Iraq highlighted that LIBS allows for "quick, in-situ, and real-time analyzing of water samples with little sample preparation and analysis without the need for additional chemical reagents" [16]. For liquid analysis, specialized equipment like the SciAps Z-9 Liquidator can nebulize a small volume (1-2 mL) of sample into a fine mist for direct analysis, eliminating the need for complex pre-concentration or digestion steps required by other techniques [14]. This not only saves time and resources but also reduces the potential for sample contamination or the alteration of elemental speciation during preparation.

Broad Multi-Element Detection

LIBS is renowned for its ability to perform multi-element detection in a single shot. The broadband emission spectroscopy captures multiple spectral lines at once, producing a comprehensive elemental fingerprint [14]. This is particularly valuable in water pollution studies, where contaminants are often diverse. For instance, research has successfully used LIBS to simultaneously identify atomic lines of macronutrients (N, K, Ca, Mg), hazardous metals (Hg, Pb, Cd, Fe), and micronutrients (Ti, Cr, Co) in a single recorded spectrum from a water sample [16]. This broad coverage ensures that both expected and unexpected contaminants can be detected, providing a more complete picture of water quality.

Experimental Protocol: Screening for Heavy Metals in Surface Water

The following diagram illustrates the end-to-end experimental workflow for a LIBS-based water screening study, from sample collection to data interpretation.

D Start Sample Collection (Surface Water) A Sample Preparation (Filtration or Nebulization) Start->A B LIBS Analysis (Laser Ablation & Plasma Formation) A->B C Spectral Data Acquisition B->C D Data Pre-processing (Background Subtraction, Normalization) C->D E Qualitative & Quantitative Analysis D->E F Data Interpretation & Reporting E->F End Conclusion on Pollutant Trends & Health Risk F->End

Detailed Methodology

Objective: To qualitatively and quantitatively diagnose heavy metal pollutants and other elements in contaminated surface water samples using Calibration-Free LIBS (CF-LIBS) analysis [16].

Materials and Reagents:

  • Water Samples: Collect surface water samples (e.g., from rivers, lakes) in pre-cleaned polypropylene bottles. Samples should be stored at 4°C if not analyzed immediately.
  • Reference Materials: (For quantitative validation) Certified Reference Materials (CRMs) for trace elements in water.

Instrumentation:

  • A typical LIBS system for liquid analysis consists of:
    • Pulsed Laser Source: Q-switched Nd:YAG laser (e.g., 1064 nm, 5-10 ns pulse width, 10-100 Hz repetition rate).
    • Spectrometer: A broadband spectrometer system such as the Avantes AvaSpec-NEXOS, which offers ultra-fast acquisition with integration times as short as 9 μs, is ideal for capturing transient plasma emissions [15]. For comprehensive coverage, a system with multiple spectrometers spanning UV to NIR (e.g., 190–950 nm) is recommended [14].
    • Liquid Sampling Attachment: A nebulizer system (e.g., SciAps Z-9 Liquidator) to convert the liquid sample into a fine mist or a system with a static liquid cell [14].
    • Detection System: Gated ICCD or CCD detector for time-resolved spectral acquisition.
    • Computer: For system control, data acquisition, and subsequent analysis.

Procedure:

  • Sample Preparation (1-2 hours):
    • Allow samples to reach room temperature.
    • Gently agitate to ensure homogeneity.
    • If necessary, filter samples through a 0.45 μm membrane filter to remove large suspended particulates. For CF-LIBS, analysis can be performed without filtration [16].
    • For analysis with a nebulizer, no further preparation is needed. Transfer 1-2 mL of sample to the nebulizer's reservoir [14].

  • Instrument Calibration and Setup (1 hour):

    • Power on the LIBS system and laser. Allow the laser to stabilize for approximately 30 minutes.
    • Align the optical path to ensure the laser is focused precisely on the liquid aerosol stream or within the liquid cell.
    • Optimize the timing parameters for the detector delay time and gate width to maximize signal-to-noise ratio for the liquid sample. A typical delay time is 1-2 μs.
    • Perform a wavelength calibration of the spectrometer using a mercury-argon lamp or other standard light source.

  • Data Acquisition (Minutes per sample):

    • Introduce the sample into the analysis region (via nebulization or in a cell).
    • Set the laser energy (e.g., 30-50 mJ/pulse) and repetition rate (e.g., 10 Hz).
    • Acquire LIBS spectra by accumulating multiple laser shots (e.g., 50-100 shots) per measurement to improve the signal-to-noise ratio.
    • Record at least three replicate measurements per sample.
    • Record a background spectrum (with the laser on but no sample plasma) for subtraction.

  • Data Analysis (Variable):

    • Pre-processing: Process raw spectra by subtracting the background, normalizing to a reference signal (e.g., the intensity of a hydrogen or oxygen line from water), and applying smoothing if necessary.
    • Qualitative Analysis: Identify elements present by matching the observed emission line peaks in the spectrum to known atomic emission databases (e.g., NIST database).
    • Quantitative Analysis (CF-LIBS): a. Integrate the area under the peak for identified elemental lines. b. Assume the plasma is in Local Thermodynamic Equilibrium (LTE) and optically thin. c. Use the Saha-Boltzmann equation to plot the Boltzmann plot for the identified elements. d. Calculate the plasma temperature and electron density. e. Determine the concentration of each element using the CF-LIBS algorithm [16] [11].

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful implementation of LIBS for water screening relies on a suite of specialized instrumentation and consumables. Table 2 details the key components and their functions within the experimental workflow.

Table 2: Essential Research Reagent Solutions for LIBS-Based Water Screening

Item/Category Function in Experiment Example Specifications & Notes
High-Energy Pulsed Laser Generates the focused laser pulse required for ablation and plasma formation on the liquid sample. Nd:YAG laser (1064 nm, 5-10 ns pulse width, 10-100 Hz). The core of the LIBS system [15].
Broadband Spectrometer Captures the full spectrum of light emitted by the cooling plasma, enabling multi-element detection. Systems like Avantes AvaSpec-NEXOS or multi-channel units covering 190-950 nm [14] [15]. Critical for detecting a wide range of elements.
Liquid Sampling Accessory Presents the liquid sample to the laser in a form suitable for efficient plasma generation. Nebulizer (e.g., SciAps Z-9 Liquidator) or static liquid cell. Allows for analysis of small sample volumes (1-2 mL) with minimal prep [14].
Certified Reference Materials (CRMs) Used for validating the quantitative accuracy of the CF-LIBS method and for building calibration curves (CC-LIBS). Trace elements in water CRMs. Essential for quality control and method development [11].
High-Purity Gases (Argon/Nitrogen) Purging gas to create an inert atmosphere around the plasma, enhancing signal intensity for some elements, and as a carrier gas for nebulizers. High-purity (99.998%+) Argon. Can significantly improve signal-to-noise ratio for elements like Carbon [14].
Data Analysis Software Processes raw spectral data, performs peak identification, and executes advanced algorithms (CF-LIBS, machine learning) for quantification. Software with peak identification (e.g., Avantes Specline) and custom algorithms for CF-LIBS [15] [11].

Representative Data and Analysis

Expected Outcomes

A study applying CF-LIBS to contaminated surface water in Iraq successfully identified and quantified a range of elements, demonstrating the multi-element capability of the technique [16]. The results from three different sampling sites (S1, S2, S3) showed distinct contamination profiles. Table 3 summarizes the reported concentration trends, providing an example of the kind of data this protocol can generate.

Table 3: Example Elemental Concentration Trends from LIBS Analysis of Surface Water (in descending order of concentration) [16]

Sample ID Detected Elements (Most to Least Concentrated)
S1 Mn > S > Fe > N > Co > Ti > Ni > Cr > Ca
S2 Cu > S > Al > N > Fe > Co > Mn > Cr > Ti > Cd > K > Ca
S3 N > Hg > S > Fe > Cu > Co > P > Ca > Cr > Mn > Ti > Mg

Data Interpretation and Signaling Workflow

The following diagram maps the logical pathway from raw spectral data to a final research conclusion, highlighting the role of advanced data processing techniques.

D SpectralData Raw LIBS Spectrum PreProcess Data Pre-processing SpectralData->PreProcess ElementID Element Identification (Peak Assignment) PreProcess->ElementID QuantAnalysis Quantitative Analysis ElementID->QuantAnalysis ML Advanced Modeling (AI/Machine Learning) QuantAnalysis->ML For Classification HealthRisk Health Risk Assessment QuantAnalysis->HealthRisk

The data processing workflow often extends beyond basic quantification. The integration of Artificial Intelligence (AI) and machine learning (ML) models is a growing trend to handle the complexity of LIBS data, improve classification accuracy, and compensate for matrix effects [11]. These models can be trained to differentiate between contamination sources or to classify pollution levels based on the spectral fingerprint.

Laser-Induced Breakdown Spectroscopy stands as a formidable technique for the screening of contaminated water, characterized by its rapid analysis, minimal demands for sample preparation, and comprehensive multi-element detection capability. The experimental protocol and data presented herein provide a concrete framework for researchers to leverage these advantages in environmental monitoring applications. The ability to perform quick, in-situ analysis with a full elemental overview makes LIBS an invaluable tool for identifying pollution trends and assessing potential long-term health risks, as demonstrated in recent field studies [16]. As the technology continues to advance, particularly with the integration of more sophisticated data analysis techniques like artificial intelligence, its role in ensuring water safety and supporting public health research is poised to expand significantly.

Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as a prominent analytical technique for environmental monitoring, offering unique advantages for rapid elemental analysis across diverse sample types. This optical atomic emission technique uses pulsed laser beams to generate a plasma in which atoms and ions from solid, liquid, or gas targets are promoted to excited states [17]. During plasma expansion and cooling, these species relax to fundamental energy states, emitting specific radiations in the UV-visible-NIR range characteristic of their electronic transitions [17]. The resulting wavelength and intensity data provide qualitative and quantitative information about elements present in the sample, making LIBS particularly valuable for environmental contamination assessment.

The technique has gained significant traction in environmental applications due to its minimal sample preparation, rapid analysis capabilities, and suitability for field-deployable instrumentation [18]. Unlike traditional techniques like ICP-MS and AAS that require complex sample digestion using corrosive acids and lengthy preparation times, LIBS can analyze samples with little to no pretreatment [18]. This advantage is particularly valuable for environmental screening applications where timely results are critical for decision-making.

Table: Comparative Analysis of LIBS with Other Analytical Techniques for Environmental Monitoring

Technique Sample Preparation Analysis Speed Detection Limits Portability Key Applications in Environmental Monitoring
LIBS Minimal or none Seconds to minutes ppm to ppb range Excellent (handheld systems available) Real-time field analysis of soils, water, aerosols
ICP-MS Extensive (acid digestion) Minutes to hours ppt to ppb range Poor (lab-based) Precise quantification of trace metals in diverse samples
XRF Minimal Minutes ppm range Good (handheld systems available) Heavy metal screening in soils and sediments
AAS Extensive (digestion, dilution) Minutes per element ppb range Poor (lab-based) Determination of individual metal concentrations

Historical Development and Technological Evolution

The application of LIBS to environmental monitoring has expanded substantially over the past decade, with significant progress in the types and number of environmental samples investigated [17]. Early LIBS systems were primarily laboratory-based, focusing on fundamental research and method development. The miniaturization of components, particularly solid-state lasers and spectrometers, facilitated the development of portable and handheld systems that revolutionized field applications [19].

Technological advancements have addressed initial limitations through various innovative approaches. Signal enhancement strategies such as collinear double-pulse LIBS, nanoparticle-enhanced LIBS, and resonance-enhanced LIBS have significantly improved detection limits and sensitivity [18]. Additionally, the integration of advanced chemometric methods and machine learning algorithms has enhanced the quantitative capabilities of LIBS by mitigating matrix effects and improving calibration models [17] [18].

The historical application of LIBS in environmental monitoring has primarily focused on several key areas:

  • Soil and Geological Analysis: Early applications targeted heavy metal contamination in soils, with ongoing research addressing challenges related to matrix effects and moisture content [17] [18].
  • Water Quality Monitoring: Development of specialized approaches for liquid analysis including liquid jet systems, surface analysis, and nanoparticle-enhanced detection [17] [18].
  • Atmospheric Monitoring: Application to aerosols and particulate matter with ongoing methodological refinements [17].
  • Waste Analysis: Particularly electronic waste and landfill leachates, with emphasis on rapid sorting and contamination assessment [17].

The LIBS market has demonstrated robust growth, reflecting increasing adoption across environmental monitoring applications. The global LIBS analyzers market was valued at approximately USD 106.8 million in 2024, with projections to reach USD 175.6 million by 2032, exhibiting a compound annual growth rate (CAGR) of 6.3% during the forecast period [20]. Alternative estimates suggest the broader LIBS market reached approximately USD 590 million in 2023, with anticipated growth to around USD 1.15 billion by 2032 at a CAGR of 7.9% [21].

Table: LIBS Market Segmentation and Growth Trends

Segment Market Characteristics Key Growth Drivers Projected CAGR Dominant Regions/Applications
By Product Type
Portable/Handheld LIBS Dominant segment (46.5% of 2024 revenue); superior mobility and flexibility Field deployment needs, technological miniaturization 6.3% [20] North America and Asia-Pacific for field geology and scrap metal yards
Desktop LIBS Higher precision for laboratory environments Requirement for enhanced spectral resolution and stability ~6% [20] Research institutions and quality control laboratories
By Application
Metal Processing & Recycling Largest application segment (38% of LIBS applications) Quality control demands, alloy verification needs 7-9% [20] [19] Global, with strong growth in Asia-Pacific
Environmental & Chemical Analysis Significant and growing segment Stringent environmental regulations, pollution monitoring needs 5.4% [19] North America and Europe with expanding Asia-Pacific adoption
Pharmaceutical & Scientific Research Emerging application area Quality assurance requirements, research funding 6-8% [20] North America and Europe
By Region
North America Largest market (34.7% of 2024 revenue) Federal funding, aerospace-defense ecosystems, mining sector ~6% [19] United States and Canada
Asia-Pacific Fastest growing region Rapid industrialization, environmental concerns, infrastructure investments 8.7% [20] China, India, and Southeast Asia
Europe Steady adoption Stringent environmental regulations, industrial automation ~6% [19] Germany, Norway, EU countries

Several key factors drive this market growth:

  • Stringent Environmental Regulations: Environmental monitoring requirements have become 42% more stringent since 2020 across North America and Europe, creating significant opportunities for portable LIBS systems [20]. Regulations targeting hazardous elements in environment, food, and consumer goods favor field-deployable LIBS tools that can screen soils, plastics, or other materials without laboratory backlogs [19].
  • Industrial Automation and Quality Control: The global push toward Industry 4.0 and smart manufacturing is driving substantial growth, with manufacturing sectors increasingly relying on LIBS for real-time elemental analysis during production processes [20].
  • Miniaturization and Cost Reduction: Advances in photonic integration have yielded sub-5 nm resolution micro-spectrometers that fit on a fingertip, while progress in GHz-repetition micro-lasers has improved battery runtime and thermal safety of handheld analyzers [19].
  • Emerging Economies: The Asia-Pacific region represents the fastest growth trajectory, led by China's rare-earth monopoly and battery gigafactories that need rapid elemental balance checks [19].

LIBS for Contaminated Water Screening: Applications and Protocols

Fundamental Principles of Water Analysis

LIBS technology faces unique challenges when applied to water analysis, primarily due to the attenuation of laser energy and plasma quenching in liquid environments [18]. Various methodologies have been developed to address these challenges, including:

  • Liquid Jet Systems: Creating stable liquid jets for direct analysis while minimizing splashing and maintaining consistent laser-sample interaction [18].
  • Surface Analysis: Focusing laser pulses on the liquid surface, though this approach can be affected by surface ripples and waves [18].
  • Sample Solidification: Freezing liquid samples or depositing them on filter substrates to convert them to solid form for analysis [17].
  • Nanoparticle-Enhanced LIBS: Using modified surface-enhanced Raman scattering substrates to improve sensitivity for trace metal detection in liquids [17].

Recent research has demonstrated the effectiveness of indirect LIBS for determining non-metallic elements in water, such as chlorine and sulfur, expanding the technique's applicability beyond metal detection [17].

Integrated LIBS-Raman for Microplastic and Heavy Metal Co-contamination

A significant advancement in water contamination screening is the development of integrated LIBS-Raman systems for simultaneous detection of microplastics and adsorbed heavy metals [22]. This multi-modal approach addresses the growing concern about microplastic contamination and their role as vectors for heavy metal transport in aquatic environments.

The protocol for integrated LIBS-Raman analysis involves:

G Sample Collection Sample Collection Filtration (1mm sieve) Filtration (1mm sieve) Sample Collection->Filtration (1mm sieve) Density Separation Density Separation Filtration (1mm sieve)->Density Separation Filter Transfer Filter Transfer Density Separation->Filter Transfer Raman Analysis Raman Analysis Filter Transfer->Raman Analysis Polymer Identification Polymer Identification Raman Analysis->Polymer Identification LIBS Analysis LIBS Analysis Polymer Identification->LIBS Analysis Heavy Metal Detection Heavy Metal Detection LIBS Analysis->Heavy Metal Detection Data Integration Data Integration Heavy Metal Detection->Data Integration Contamination Assessment Contamination Assessment Data Integration->Contamination Assessment

Integrated LIBS-Raman Analysis Workflow

Sample Collection and Preprocessing:

  • Collect water samples (typically 100L) using stainless steel equipment
  • Sieve through 1mm mesh stainless steel sieve to concentrate particulate matter
  • Perform density separation to isolate microplastics from organic and inorganic matter
  • Transfer samples to filters for analysis [22]

Raman Analysis for Microplastic Characterization:

  • Use laser energy of 0.8 mJ and exposure time of 4s for Raman measurements
  • Create reference spectral database for common plastic polymers (PA, PC, PS, PP, PE)
  • Identify unknown microplastics by comparing measured spectra with reference database
  • Characterize polymer type based on characteristic Raman bands [22]

LIBS Analysis for Heavy Metal Detection:

  • Employ calibration curves developed using standard samples with known heavy metal concentrations
  • Focus laser pulses on microplastic surfaces to detect adsorbed heavy metals
  • Utilize characteristic emission lines for heavy metal identification (e.g., Cd I 228.8 nm, Pb I 405.78 nm)
  • Perform quantitative analysis based on calibration curves and signal intensity [22]

This integrated approach enables comprehensive assessment of both particulate plastic pollution and associated heavy metal contaminants, providing a more complete picture of water quality issues.

Experimental Protocol for Direct Water Contamination Screening

For direct screening of heavy metals in water, the following protocol is recommended:

Equipment Setup:

  • Portable or benchtop LIBS system with Nd:YAG laser (1064 nm wavelength)
  • Spectrometer covering UV-visible range (200-800 nm)
  • Sample chamber with liquid jet system or stabilized surface arrangement
  • Data processing software with chemometric capabilities [18]

Calibration Procedure:

  • Prepare standard solutions with known concentrations of target heavy metals (Cd, Pb, Hg, Cr, As)
  • Generate calibration curves for each element using characteristic emission lines
  • Validate calibration with independent standard reference materials
  • Implement internal standardization when necessary to compensate for matrix effects [18]

Sample Analysis Protocol:

  • Filter water samples to remove large particulates if using liquid jet systems
  • Maintain consistent flow rate for liquid jet systems (typically 0.5-2 mL/min)
  • For surface analysis, ensure minimal disturbance to liquid surface
  • Acquire multiple spectra (typically 30-50 laser pulses) per sample to ensure representative analysis
  • Average spectra to improve signal-to-noise ratio
  • Apply appropriate chemometric methods (PCA, PLS) for quantitative analysis [18]

Quality Assurance:

  • Analyze method blanks regularly to monitor potential contamination
  • Include certified reference materials in analysis sequence
  • Perform duplicate analyses to assess precision
  • Monitor plasma temperature and electron density for consistency [18]

The Researcher's Toolkit: Essential Solutions and Reagents

Table: Essential Research Reagent Solutions for LIBS-based Water Contamination Screening

Reagent/Solution Composition/Specifications Primary Function in LIBS Analysis Application Notes
Standard Metal Solutions High-purity nitrate or chloride salts in deionized water (Cd, Pb, Hg, Cr, As) Calibration curve development, method validation Prepare fresh solutions weekly; acidify to pH < 2 for stability
Internal Standard Solution Yttrium or Indium solutions of known concentration Compensation for matrix effects, signal normalization Particularly important for liquid analysis with varying salinity
Sample Preservation Solution Ultrapure nitric acid (trace metal grade) Sample stabilization during storage Maintain pH < 2; use certified DNA-free solutions for low-biomass samples [23]
Density Separation Solution Sodium chloride or sodium iodide solutions of specific density Separation of microplastics from organic/inorganic matter Enables isolation of microplastics for subsequent LIBS-Raman analysis [22]
Decontamination Solution 10% nitric acid followed by sodium hypochlorite (DNA removal) Equipment decontamination between samples Critical for preventing cross-contamination, especially in trace analysis [23]
Certified Reference Materials NIST-traceable environmental reference materials Method validation, quality control Essential for verifying analytical accuracy and precision

Advanced Applications and Future Outlook

The future of LIBS in environmental monitoring is shaped by several emerging trends and technological integrations:

Integration with Artificial Intelligence and Machine Learning: Advanced data analytics integration represents the next frontier for LIBS technology, with machine learning algorithms improving measurement accuracy by 40% in recent trials [20]. AI-enhanced LIBS systems can automatically identify spectral patterns, classify samples, and predict elemental concentrations with minimal human intervention, potentially reducing the need for highly specialized operators [19].

Multi-Modal Spectroscopy Systems: The combination of LIBS with complementary analytical techniques like Raman spectroscopy provides more comprehensive material characterization [22] [19]. These hybrid systems can simultaneously provide elemental composition (via LIBS) and molecular structure information (via Raman), offering powerful solutions for complex environmental samples like microplastics with adsorbed contaminants [22].

Miniaturization and Field Deployment: Ongoing development of smaller, more power-efficient LIBS systems continues to expand field applications [19]. Recent systems have demonstrated capabilities for underwater analysis at 6,000 m depth, eliminating core-sample retrieval delays [19]. The successful deployment of a 65 g LIBS payload on NASA's Mars 2020 rover, representing an 87% weight reduction versus earlier designs, highlights the potential for extreme environment monitoring [19].

Expanding Application Frontiers: Emerging applications include:

  • Battery Supply Chain Monitoring: Field-portable LIBS units can detect lithium or carbon elements invisible to XRF, making them indispensable for core-shed screening and grade-control loops in critical mineral exploration [19].
  • Precision Agriculture: LIBS is being applied for nutrient tracking in hydroponics, where real-time readings aid precision fertilization [19].
  • Biomedical and Forensic Applications: Growing use of LIBS for trace element analysis in biological tissues and forensic evidence [21].
  • Climate Science: Analysis of ice cores and atmospheric particulates for climate reconstruction and pollution source tracking [23].

Despite these promising developments, challenges remain in further improving the sensitivity and accuracy of LIBS systems, particularly for complex environmental matrices. The technique continues to face competition from established technologies like XRF and ICP-MS, though its unique advantages position it for continued growth in specific application niches, particularly those requiring rapid, in-situ analysis with minimal sample preparation [19].

LIBS in Action: Methodologies and Real-World Applications in Water Screening

Water hardness, primarily determined by the concentration of calcium (Ca) and magnesium (Mg) ions, is a critical parameter for assessing water quality in environmental, industrial, and domestic applications [24] [25]. Long-term consumption of water with excessively low hardness may increase the prevalence of certain health conditions, while elevated levels can cause gastrointestinal disturbances and lead to scale formation in industrial equipment [24]. Traditional methods for determining water hardness, such as EDTA titration and inductively coupled plasma optical emission spectrometry (ICP-OES), are time-consuming, require extensive sample preparation, or rely on specialized operator expertise [24] [26].

Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as a powerful analytical technique for rapid, multi-elemental analysis with capabilities for online, real-time, and in-situ operation [24] [27]. This application note details a novel methodology that combines aerosolization with a modified one-point calibration LIBS (OPC-LIBS) model for the direct analysis of calcium and magnesium hardness in surface water, providing a case study demonstrating its application to environmental water samples.

Experimental Principle and Workflow

Direct analysis of liquid samples via LIBS presents challenges due to plasma quenching effects, which result in poor spectral stability and intensity [24]. To overcome this limitation, the presented method transforms liquid water samples into a solid aerosol form using a rapid aerosol generation device. This approach promotes complete ionization of aqueous solutions and enhances detection sensitivity. The analytical principle couples this aerosolization with a modified OPC-LIBS quantitative model that utilizes the Multi-Element Saha-Boltzmann (ME-SB) plot for accurate plasma temperature calculation, requiring only a single matrix-matched standard sample for calibration [24].

The workflow for direct water hardness analysis encompasses sample introduction, aerosol generation, LIBS spectral acquisition, and quantitative analysis using the modified OPC-LIBS model, as illustrated below.

G Figure 1. Experimental Workflow for LIBS-Based Water Hardness Analysis Sample Water Sample Collection Aerosol Aerosol Generation (Collison Nebulizer, 1 L/min) Sample->Aerosol LIBS LIBS Spectral Acquisition (532 nm laser, 200 mJ) Aerosol->LIBS Analysis Quantitative Analysis (Modified OPC-LIBS with ME-SB Plot) LIBS->Analysis Results Calcium & Magnesium Concentration Results Analysis->Results

Materials and Methods

Key Research Reagent Solutions and Essential Materials

The following table details the essential materials and reagents required for implementing the LIBS-based water hardness analysis method.

Table 1: Essential Research Reagents and Materials for LIBS Water Hardness Analysis

Item Specification/Composition Function in Analysis
Standard Solution Ca (50 mg/L), Mg (50 mg/L), Sr (500 mg/L) Single-point calibration for modified OPC-LIBS model [24]
Collison Nebulizer Flow rate: 1 L/min Aerosol generation from liquid water samples [24]
Laser Source Wavelength: 532 nm, Pulse Width: 7 ns, Energy: 200 mJ Plasma generation through laser ablation [24]
Spectrometer Dual-channel (250-500 nm & 570-780 nm) Collection of atomic emission spectra from plasma [24]
Drying Tube N/A Reduces humidity-induced signal fluctuations by absorbing water molecules [24]

Detailed Experimental Protocol

Sample Preparation and Aerosolization
  • Collection: Collect surface water samples (e.g., from rivers, reservoirs, or lakes) in clean, pre-rinsed containers.
  • Filtration (Optional): Filter samples if significant particulate matter is present to prevent nebulizer clogging.
  • Aerosol Generation: Introduce the liquid sample into the Collison nebulizer. Maintain a constant flow rate of 1 liter per minute (L/min) at the outlet to produce a consistent aerosol stream.
  • Drying: Direct the generated aerosol through a drying tube. This step absorbs water molecules, mitigates humidity-induced signal fluctuations, and improves detection sensitivity by ensuring laser energy is not expended on water evaporation [24].
LIBS Spectral Acquisition
  • System Setup: Utilize a LIBS system equipped with a 532 nm Nd:YAG laser (7 ns pulse width, 200 mJ energy) and a dual-channel fiber-optic spectrometer covering 250-500 nm and 570-780 nm ranges.
  • Laser Focusing: Expand the laser beam and focus it orthogonally onto the center of the aerosol jet using an aspherical lens to achieve stable plasma excitation.
  • Light Collection: Collect plasma emission light using a pair of flat-convex lenses (f = 50 mm) and transmit it to the spectrometer via a bifurcated fiber (600 μm diameter).
  • Spectral Recording: Acquire LIBS spectra with the spectrometer. Accumulate multiple spectra (typically 50-100 laser pulses) to improve the signal-to-noise ratio.
Quantitative Analysis via Modified OPC-LIBS
  • Standard Sample Analysis: Obtain the LIBS spectrum of the standard sample containing known concentrations of Ca, Mg, and Sr.
  • Plasma Temperature Calculation: Calculate the plasma temperature using the Multi-Element Saha-Boltzmann (ME-SB) plot method based on the standard sample's spectrum and known elemental contents [24]. The ME-SB method is derived from the following equations governing atomic (Iki) and ionic (Imn) line intensities, number densities (NaI, NaII), and the Saha equation (N_S) [24]: I_ki = G * N_aI * A_ki * g_k * e^(-E_k/(K_B * T)) / U_I I_mn = G * N_aII * A_mn * g_m * e^(-E_m/(K_B * T)) / U_II N_a = N_aI + N_aII = C_a * N_S N_aII / N_aI = (2 / N_e) * (2 * π * m_e * K_B * T)^(3/2) / h^3 * (U_II / U_I) * e^(-E_ion/(K_B * T))
  • Unknown Sample Analysis: Apply the calculated plasma temperature to correct the Boltzmann plot of unknown water samples.
  • Concentration Determination: Determine the concentrations of Ca and Mg in the unknown samples using the modified OPC-LIBS model. Calculate total water hardness as the sum of Ca and Mg concentrations, typically expressed as mg/L of calcium carbonate (CaCO₃).

The data analysis procedure for converting raw spectral data into quantitative hardness values is summarized below.

G Figure 2. OPC-LIBS Quantitative Analysis Data Flow Spectra Raw LIBS Spectra (Standard & Unknown Samples) TempCalc Plasma Temperature Calculation via ME-SB Plot Spectra->TempCalc Model Apply Modified OPC-LIBS Model TempCalc->Model Quant Quantitative Results (Ca, Mg Concentrations) Model->Quant Hardness Total Hardness (mg/L as CaCO₃) Quant->Hardness

Results and Discussion

Analytical Performance

The combination of aerosolization and modified OPC-LIBS was demonstrated to achieve high-precision rapid quantification of water hardness. The method was validated by analyzing real water samples from different sources (Yangtze River, reservoir, and underground) and comparing the quantitative results with those obtained by ICP-OES and other LIBS calibration methods [24].

Table 2: Comparison of Quantitative Analysis Performance for Water Hardness Using Different LIBS Calibration Methods [24]

Calibration Method Number of Standards Required Average Relative Error for Ca and Mg Key Advantages
Modified OPC-LIBS (with aerosolization) One (Ca: 50 mg/L, Mg: 50 mg/L, Sr: 500 mg/L) Lowest (22.23% lower than internal standard; 14.50% lower than external standard) High accuracy, rapid analysis, minimal standard preparation
Internal Standard LIBS Multiple (for calibration curve) ~22.23% higher than modified OPC-LIBS Corrects for pulse-to-pulse energy fluctuations
External Standard LIBS Multiple (for calibration curve) ~14.50% higher than modified OPC-LIBS Simple principle, widely used

The data in Table 2 clearly shows the superior performance of the modified OPC-LIBS method, which significantly reduces quantitative error compared to conventional LIBS calibration approaches while requiring only a single standard sample. This demonstrates the method's efficacy for rapid and accurate water hardness determination.

Significance in Contaminated Water Screening

This LIBS-based method addresses a critical need for simple, rapid, and online detection capabilities in water quality testing fields. The simplified calibration workflow, combined with the direct analysis of liquid samples via aerosolization, makes this technique particularly valuable for the screening of contaminated water sources where timely results are essential. The ability to perform high-accuracy quantification with a single standard sample significantly enhances analysis speed compared to traditional methods that require constructing multi-point calibration curves, positioning this LIBS approach as a powerful tool for environmental monitoring and industrial water quality control [24].

This application note has detailed a robust methodology for the direct analysis of calcium and magnesium hardness in surface water using laser-induced breakdown spectroscopy combined with aerosolization. The modified OPC-LIBS model, utilizing a single standard sample and the ME-SB plot for temperature calculation, provides a significant improvement in quantitative accuracy over traditional LIBS calibration methods. The experimental protocols and reagent specifications outlined herein provide researchers and scientists with a comprehensive framework for implementing this advanced technique in their water quality assessment and contaminated water screening programs.

Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as a powerful analytical technique for the rapid detection of heavy metal contaminants in environmental samples. This application note details the protocols and methodologies for using LIBS to screen lead (Pb), arsenic (As), and cadmium (Cd) in contaminated water, supporting research objectives outlined in the broader thesis on advanced spectroscopic techniques for environmental monitoring. The persistent nature and significant health risks associated with these heavy metals necessitate robust, real-time screening methods that overcome the limitations of conventional techniques like ICP-MS and AAS, which involve complex sample pretreatment, lengthy analysis times, and laboratory confinement [28] [29]. LIBS addresses these challenges by enabling rapid, in-situ, multi-element analysis with minimal sample preparation.

Quantitative Performance of LIBS for Heavy Metal Detection

The performance of LIBS in detecting heavy metals varies based on the sample matrix (liquid or solid), experimental configuration, and the specific element analyzed. The following tables summarize key quantitative performance metrics from recent studies.

Table 1: Detection Limits for Heavy Metals in Liquid Water Samples using LIBS

Heavy Metal Best Achieved LOD (µg/mL) Experimental Conditions Liquid Configuration Citation
Cadmium (Cd) 68 µg/mL (68 ppm) Nanosecond Laser, Liquid Jet Homemade liquid jet [30]
Lead (Pb) 0.023 µg/mL (23 ppt) Femtosecond Laser, Flowing Liquid Vertically flowing liquid [28]
Arsenic (As) Data not available in liquid - - -

Table 2: Detection Limits for Heavy Metals in Soil Samples using LIBS

Heavy Metal Achieved LOD (mg/kg) Experimental Conditions Validation Method Citation
Lead (Pb) 29.5 mg/kg Nd:YAG Laser (1064 nm), Argon Purging ICP-MS [29]
Arsenic (As) 95.5 mg/kg Nd:YAG Laser (1064 nm), Argon Purging ICP-MS [29]
Cadmium (Cd) Data not available - - -

Detailed Experimental Protocols

Protocol for High-Sensitivity Analysis of Flowing Liquid Water

This protocol, adapted from Chen et al. (2025), is designed for the high-sensitivity detection of Pb and other metals in aqueous solutions using a femtosecond laser system [28].

1. Reagent and Solution Preparation:

  • Prepare stock standard solutions (e.g., 1000 µg/mL) of Pb, Cr, and Cu from certified traceable salts.
  • Serially dilute stock solutions with deionized water (18.2 MΩ·cm resistivity) to create calibration standards covering the expected concentration range.

2. Liquid Jet System Setup:

  • Utilize a pump to create a stable, continuous vertical flow of the aqueous sample.
  • Configure the liquid jet to achieve a stream diameter of 0.84 mm and a flow rate of approximately 901 mm³/s (velocity ~1629 mm/s) [28].
  • Ensure the jet setup is vibration-free to maintain stability.

3. LIBS Instrumental Configuration:

  • Laser: Employ a femtosecond laser (e.g., Spectra-Physics Solstice Ace) with the following parameters:
    • Pulse Duration: 35 fs
    • Wavelength: 800 nm
    • Repetition Rate: 1 kHz
    • Pulse Energy: 5 mJ
  • Optics: Focus the laser beam onto the surface of the flowing liquid jet using a 100 mm focal length lens.
  • Spectrometer: Use a spectrometer (e.g., Andor Kymera 328i) with a 2400 grooves/mm grating and a spectral resolution of ~0.1 nm.
  • Detector: Employ an Intensified CCD (ICCD) camera. Set the gate width to 5 µs and the delay time to 2.5 µs for optimal signal-to-noise ratio [28].

4. Data Acquisition and Analysis:

  • Accumulate spectra for 10, 20, and 50 laser pulses (NSA) to evaluate the improvement in the Limit of Detection (LOD).
  • For each standard and sample, collect a minimum of three replicates.
  • Construct calibration curves by plotting the intensity of the characteristic emission line against the known concentration.
  • Calculate the LOD using the 3σ rule (LOD = 3σ/S, where σ is the standard deviation of the blank and S is the slope of the calibration curve).

Protocol for Solid Soil Sample Analysis

This protocol, based on the work in North Birmingham, Alabama, details the analysis of heavy metals in soil samples [29].

1. Sample Preparation:

  • Air-dry collected soil samples at room temperature.
  • Gently crush and homogenize the samples using an agate mortar and pestle to avoid cross-contamination.
  • Press the powdered soil into pellets using a hydraulic press without binders.

2. LIBS Instrumental Configuration:

  • Laser: Use a Q-switched Nd:YAG laser (e.g., Litron Laser Nano 120-20) with the following parameters [29]:
    • Wavelength: 1064 nm
    • Pulse Energy: 100 mJ
    • Pulse Duration: 10-20 ns
    • Repetition Rate: 10 Hz
  • Atmosphere Control: Place the sample in a chamber purged with argon gas to enhance signal intensity by doubling the spectral response for heavy metals [29].
  • Spectrometer: A broadband spectrometer (e.g., Avantes AvaSpec-ULS4096CL-EVO) covering 200-900 nm is recommended.
  • Detector: Use a non-gated or gated CCD detector. A delay time of 1.0 µs is typical.

3. Data Acquisition and Quantification:

  • Perform a minimum of 30 laser shots per sample location and average the spectra to improve reproducibility.
  • To mitigate matrix effects, normalize the spectral intensity of the analyte (As, Pb) to the total integrated signal of the plasma emission [29].
  • Build a uni-variate calibration model using soil standards with known concentrations.
  • Validate the LIBS results against a reference method such as ICP-MS.

Workflow Diagram: LIBS Analysis of Contaminated Water

The following diagram illustrates the core workflow for analyzing heavy metals in water using a liquid jet configuration.

G Start Sample Preparation (Aqueous Solution) A Set Up Liquid Jet Start->A B Configure LIBS Parameters A->B C Laser Ablation on Jet Stream B->C D Plasma Formation & Light Emission C->D E Spectrum Acquisition by ICCD D->E F Data Processing & Analysis E->F End Quantitative Results F->End

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Reagents and Materials for LIBS Analysis of Heavy Metals

Item Function / Purpose Specifications / Examples
Certified Reference Materials (CRMs) Calibration and validation of the LIBS system. Certified soil pellets (e.g., OREAS 147) [31], single-element aqueous standards.
High-Purity Solvents Preparation of standard solutions and sample dilution. Deionized water (18.2 MΩ·cm).
Laser System Source of energy for plasma generation. Femtosecond (e.g., 35 fs, 1 kHz) [28] or Nanosecond Nd:YAG (1064 nm, 100 mJ) [29] lasers.
Spectrometer Separation of plasma light into characteristic wavelengths. Czerny-Turner spectrometer with 2400 grooves/mm grating [28].
Detector Measurement of light intensity at specific wavelengths. Intensified CCD (ICCD) for time-gated detection [28].
Inert Gas Enhancement of signal intensity and plasma stability. Argon gas for purging the analysis chamber [29].
Liquid Jet Assembly Enables stable analysis of liquid samples by minimizing splashing. Peristaltic pump and nozzle for producing a 0.64-0.84 mm diameter stream [28] [13].

Data Processing and Signal Optimization

Workflow Diagram: Spectral Data Processing

After raw spectrum acquisition, robust data processing is crucial for accurate quantification. The following pathway outlines key steps.

G Start Raw LIBS Spectrum A Pre-processing Start->A A1 Background Subtraction A->A1 A2 Intensity Normalization A1->A2 A3 Peak Integration A2->A3 B Model Building A3->B B1 Univariate Calibration B->B1 B2 Multivariate Regression B->B2 End Concentration Prediction B1->End B2->End

Key Optimization Strategies:

  • Spectral Normalization: Normalize the analyte signal (e.g., Pb line) to the total integrated intensity of the plasma spectrum or an internal reference element (e.g., Fe in soils) to minimize pulse-to-pulse fluctuations and matrix effects [29].
  • Signal Accumulation: Accumulating multiple spectra (e.g., NSA=50) significantly enhances the signal-to-noise ratio, leading to lower Limits of Detection (LODs) [28].
  • Multivariate Analysis: Employ chemometric methods like principal component regression (PCR) or support vector machines (SVM) to model complex relationships between spectral data and concentration, thereby mitigating matrix effects [29].
  • Validation: Use robust validation methods such as "leave-one-sample-out" to obtain a more reliable estimate of the model's real-world performance [32].

LIBS presents a formidable analytical tool for the rapid and sensitive screening of heavy metal contaminants like lead, arsenic, and cadmium in water and soil. The protocols outlined herein provide a framework for achieving high-quality, quantitative results. The integration of optimized liquid sampling techniques, advanced signal processing, and robust calibration with certified standards is paramount for success. Future directions will focus on further improving LODs through nanoparticle enhancement, advancing portable and downhole LIBS instruments for field deployment [13] [33], and developing more sophisticated chemometric models to fully unlock the quantitative potential of LIBS for environmental protection and public health safety.

Liquid Jet Technology and Other Sampling Approaches for Aqueous Samples

Laser-Induced Breakdown Spectroscopy (LIBS) presents a powerful technique for the rapid, on-site elemental analysis of aqueous solutions, a capability of paramount importance for environmental monitoring, industrial process control, and contaminated water screening. However, direct analysis of liquids presents intrinsic challenges, including signal quenching, droplet splashing, and surface ripples, which limit analytical sensitivity. This application note details advanced sampling methodologies, with a focus on liquid jet technology, engineered to overcome these obstacles. We provide a comprehensive comparison of various sampling approaches, detailed experimental protocols for a representative liquid jet experiment, and a curated toolkit of essential reagents and materials. By framing this within the context of contaminated water screening, this document serves as a practical guide for researchers and scientists implementing robust, sensitive, and quantitative LIBS analysis for aqueous samples.

The elemental analysis of aqueous solutions is critical across numerous fields, from environmental protection to industrial process monitoring [34]. While techniques like Inductively Coupled Plasma Mass Spectrometry (ICP-MS) offer high sensitivity, they often require complex sample preparation, laboratory settings, and cannot provide real-time, on-site data [28] [34]. LIBS emerges as a compelling alternative, offering capabilities for rapid, multi-elemental analysis with minimal sample preparation [34].

The fundamental challenge in applying LIBS to liquids lies in the physical interaction between the laser pulse and the sample. Focusing a laser directly onto a static liquid surface leads to issues such as splashing, surface instability, and rapid plasma cooling, resulting in weak emission signals and poor reproducibility [28]. To circumvent these issues, several sampling strategies have been developed, which can be categorized into liquid bulk analysis, liquid-to-solid conversion, liquid-to-aerosol conversion, and liquid jet/flowing liquid analysis [34]. Among these, the liquid jet technique is particularly effective for high-sensitivity, real-time analysis, as it provides a fresh, stable, and reproducible target for each laser pulse, thereby enhancing the signal-to-noise ratio and lowering the limits of detection (LODs) [28] [13].

Sampling Approaches for Aqueous LIBS Analysis

The choice of sampling approach is the most critical factor determining the success of a LIBS analysis for aqueous solutions. The following section summarizes the primary methodologies, their principles, advantages, and limitations.

Table 1: Comparison of Sampling Approaches for Aqueous LIBS Analysis

Approach Principle Advantages Disadvantages/Challenges Reported LOD Examples
Liquid Jet / Flowing Liquid A continuous, stable stream of liquid is used as the laser ablation target. - Fresh surface for each pulse- Enhanced stability & reproducibility- Suitable for real-time, in-situ monitoring- Reduced splashing - Requires precise flow control- Optimization of jet diameter and laser alignment is critical - Cr: 0.061 µg/mL [28]- Pb: 0.045 µg/mL [28]- Cu: 0.023 µg/mL [28]- Ca: 11.58 mg/L [13]- Mg: 2.57 mg/L [13]
Liquid-to-Solid Conversion The aqueous sample is preconcentrated onto a solid substrate (e.g., filter, metal, pellet). - Significantly improved LODs via preconcentration- Simplified analysis similar to solid samples- Broadly applicable - Time-consuming preparation- Risk of secondary contamination- Requires additional equipment and steps - Sub-ppb levels for Cr, Pb, Cu using surface-enhanced substrates [35]
Liquid-to-Aerosol Conversion The liquid sample is nebulized into a fine mist or aerosol for laser ablation. - Large surface area for ablation- Continuous introduction of fresh sample - Complex setup requiring nebulizers and carrier gases- Potential for memory effects between samples - Information on specific LODs was not available in the provided search results.
Liquid Bulk (Static) The laser is focused onto the surface of a static liquid volume. - Simplest setup- No specialized equipment needed - Lowest sensitivity- Signal instability from ripples and splashing- Not suitable for quantitative analysis - Generally high LODs, making it less suitable for trace heavy metal detection [28]
Liquid Wheel A rotating wheel is used to create a thin, moving liquid film. - Provides a stable, renewable liquid surface- Amenable to process monitoring - Mechanical complexity of the rotating system - Developed for real-time process monitoring; specific LODs vary by element and application [35]

The liquid jet method involves propelling the aqueous sample through a nozzle to form a continuous, vertically flowing stream. This configuration offers several key benefits for LIBS analysis. The constant flow presents a fresh and stable surface for each laser pulse, minimizing the signal extinction and intensity fluctuations caused by surface perturbations in static liquids [28]. This stability directly translates to enhanced measurement reproducibility. Furthermore, the flowing nature of the jet reduces the accumulation of ablated material at the focal point, thereby mitigating memory effects between laser shots and ensuring that each spectrum is representative of the bulk solution. This makes the technique exceptionally well-suited for the continuous, real-time monitoring of water quality, such as screening for heavy metal contaminants like Chromium (Cr), Lead (Pb), and Copper (Cu) in industrial wastewater or natural water bodies [28] [13].

Experimental Protocol: Direct Analysis Using a Liquid Jet System

The following protocol details a specific methodology for the high-sensitivity detection of heavy metals (Cr, Pb, Cu) in water using a femtosecond laser and a vertically flowing liquid jet, as demonstrated in recent research [28].

Apparatus and Reagents

Table 2: Research Reagent Solutions and Essential Materials

Item Function/Description Specification/Notes
Femtosecond Laser Plasma generation Wavelength: 800 nm; Pulse Duration: 35 fs; Repetition Rate: 1 kHz; Pulse Energy: 5 mJ [28].
Spectrometer & Detector Spectral acquisition Intensified CCD (ICCD) detector coupled to a spectrometer. Gating capability is crucial.
Liquid Jet System Sample introduction Consists of a pump, tubing, and a nozzle. Nozzle diameter critical (e.g., 0.84 mm [28] or 0.64 mm [13]).
Peristaltic or Syringe Pump Controlled fluid delivery Generates a stable, continuous liquid flow. Flow rate must be optimized (e.g., 901 mm³/s [28]).
Standard Solutions Calibration High-purity single- or multi-element standard solutions for Cr, Pb, Cu. Serial dilutions prepared in deionized water.
Lens Laser focusing Focal length typically 100 mm or similar.
Delay Generator System synchronization Precisely controls timing between laser pulse and detector gating.
Step-by-Step Procedure

  • Liquid Jet Assembly and Optimization: Connect the pump to the liquid reservoir and the jet nozzle using appropriate tubing. Position the nozzle to create a stable, vertical, and laminar flow. Key parameters to optimize include:

    • Jet Diameter: A diameter of 0.84 mm has been used successfully for heavy metal detection [28]. Another study optimized this to 0.64 mm for calcium and magnesium analysis to ensure signal stability [13].
    • Flow Velocity: Set the pump to achieve a flow rate of approximately 901 mm³/s, corresponding to a linear velocity of 1629 mm/s [28].
    • Laser Ablation Point: Precisely align the laser to focus on the center of the jet stream. The optimal distance from the jet outlet was found to be 5 mm in a similar setup [13].

  • Laser and Optical Alignment: Direct the femtosecond laser beam towards the liquid jet using a reflector. Focus the laser pulse onto the jet surface using a lens with a 100 mm focal length. Ensure the plasma plume is consistently generated at the focal point.

  • Spectral Acquisition Setup: Align the light collection system (lens or fiber optic) to efficiently collect the plasma emission and direct it to the spectrometer. Connect the ICCD detector to the spectrometer and a computer for data acquisition.

  • Optimization of Acquisition Parameters: This is a critical step for achieving high sensitivity.

    • Gate Delay: Determine the optimal delay between the laser pulse and the opening of the ICCD gate. A delay of 200 ns has been identified as ideal for maximizing the signal-to-noise ratio for Cu analysis, allowing the intense continuous background radiation to decay [28].
    • Gate Width: Set the detector gate width; a value of 5 µs was used in the referenced study [28].
    • Spectral Accumulation: Accumulate signals from multiple laser pulses to enhance the signal-to-noise ratio. Studies have used 10, 20, and 50 accumulations, with higher numbers generally improving LODs [28]. Another study averaged 20 spectra to reduce the relative standard deviation (RSD) from ~16% to 2% [13].

  • Calibration Curve Generation:

    • Prepare a series of standard solutions with known concentrations of the target analytes (e.g., Cr, Pb, Cu).
    • For each standard, acquire the LIBS spectrum under the optimized conditions.
    • Identify the most intense and isolated emission line for each element (e.g., Cu I at 324.75 nm, Cr I at 425.43 nm, Pb I at 405.78 nm).
    • Plot the net line intensity (peak intensity minus background) against the analyte concentration to generate a calibration curve for each element.

  • Sample Analysis:

    • Introduce the unknown water sample into the liquid jet system.
    • Acquire the LIBS spectrum using the identical parameters established during calibration.
    • Determine the concentration of the target elements by interpolating the measured spectral line intensity into the corresponding calibration curve.
Data Analysis and Performance Metrics

The performance of the LIBS system should be evaluated using the following metrics, calculated from the calibration data:

  • Coefficient of Determination (R²): Should be >0.99 for a reliable calibration.
  • Limit of Detection (LOD): Calculated as 3σ/m, where σ is the standard deviation of the blank signal and m is the slope of the calibration curve. The protocol above has achieved LODs in the range of 0.019–0.061 µg/mL for Cr, Pb, and Cu [28].
  • Relative Standard Deviation (RSD): A measure of precision. The described method can achieve RSDs between 1–4% [28].
  • Average Relative Error (ARE): A measure of accuracy. The method reported AREs in the range of 2–8% [28].

Visual Guide to Liquid Jet LIBS Workflow

The following diagram illustrates the logical workflow and key components of a typical liquid jet LIBS system for aqueous sample analysis.

G Start Start Analysis SampleIntro Sample Introduction (Pump & Reservoir) Start->SampleIntro JetForm Liquid Jet Formation (Nozzle: Ø 0.64-0.84 mm) SampleIntro->JetForm LaserAblation Laser Ablation (fs-laser, 5 mJ, 1 kHz) JetForm->LaserAblation PlasmaGen Plasma Generation (Gate Delay: ~200 ns) LaserAblation->PlasmaGen LightCollect Light Collection (Spectrometer + ICCD) PlasmaGen->LightCollect DataAcq Data Acquisition (10-50 spectra accumulation) LightCollect->DataAcq QuantAnalysis Quantitative Analysis (Calibration & LOD Calculation) DataAcq->QuantAnalysis Result Result: Elemental Concentration QuantAnalysis->Result

Diagram 1: Liquid Jet LIBS Experimental Workflow. This diagram outlines the key stages in a liquid jet LIBS analysis, from sample introduction to quantitative result.

Methodology Selection Framework

Choosing the optimal sampling method depends on the specific analytical requirements. The following decision diagram aids in selecting the most appropriate approach based on key criteria.

G Start Select Aqueous LIBS Method Q1 Primary Need for Ultra-trace Detection (ppb)? Start->Q1 Yes Yes Q1->Yes Yes No No Q1->No No Q2 Requirement for Real-time/On-site Analysis? Q2->Yes Yes Q2->No No Q3 Analysis Time/Cost a Major Constraint? Q3->Yes Yes Q3->No No SolidConv Liquid-to-Solid Conversion Yes->SolidConv LiquidJet Liquid Jet Yes->LiquidJet Aerosol Liquid-to-Aerosol Conversion Yes->Aerosol No->Q2 No->Q3 Bulk Liquid Bulk (Static) (Not recommended for quantitative trace analysis) No->Bulk

Diagram 2: Aqueous LIBS Methodology Selection Guide. This flowchart provides a logical framework for selecting the most suitable sampling approach based on analytical priorities.

Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as a powerful analytical technique for rapid elemental analysis of various sample types, including liquids, solids, and gases [12]. Its applications in environmental monitoring of water quality have gained significant attention due to several advantages over traditional methods: minimal sample preparation, rapid analysis capabilities, and the elimination of chemical reagents that could cause secondary pollution [12] [36]. While LIBS has been extensively applied for detecting metallic elements in water, its potential for screening microplastics and additives represents an innovative expansion of its capabilities.

The analysis of liquid samples via LIBS presents unique challenges, including plasma quenching by the liquid medium, signal instability due to bubble formation and splashing, and generally lower sensitivity compared to analysis of solid samples [12] [37]. These limitations have prompted the development of various sample introduction and pretreatment strategies to enhance LIBS performance for aqueous applications. This application note details advanced methodologies that overcome these challenges, enabling researchers to effectively utilize LIBS for comprehensive water contaminant screening, with particular emphasis on microplastics and their associated chemical additives.

Technical Background

LIBS Fundamentals for Liquid Analysis

LIBS operates on the principle of generating a high-temperature plasma on the sample surface using a focused pulsed laser beam. As the plasma cools, excited atoms and ions emit characteristic wavelengths of light, which are collected and analyzed to determine elemental composition [12] [37]. For liquid samples, this process is complicated by the rapid quenching effect of the surrounding water molecules, which shortens plasma lifetime and reduces emission intensity [37]. Additionally, the formation of bubbles and mechanical waves at the liquid surface interferes with consistent plasma generation [37].

The analytical performance of LIBS for water contaminants is quantified by several key parameters:

  • Limit of Detection (LOD): The lowest concentration that can be reliably detected
  • Signal-to-Noise Ratio (SNR): The ratio of signal intensity to background noise
  • Plasma Temperature: Affects excitation efficiency and spectral characteristics
  • Plasma Lifetime: Influences the temporal window for signal collection

Complementary Spectroscopic Techniques

While LIBS provides excellent elemental analysis capabilities, the identification and characterization of microplastics—which are primarily organic polymers—often benefit from complementary techniques. Vibrational spectroscopy methods including Fourier Transform Infrared (FT-IR) and Raman spectroscopy offer direct molecular identification of polymer types through their characteristic functional groups and molecular fingerprints [38]. Recent research has demonstrated that integrated approaches combining LIBS with these techniques enhance the accuracy of microplastic identification, particularly for complex environmental samples where overlapping signals and degradation products complicate analysis [38].

Advanced LIBS Methodologies for Enhanced Detection

Table 1: Comparison of Sample Introduction Methods for Aqueous LIBS Analysis

Method Principle Advantages Limitations Representative LODs
Acoustic Levitation (LIBS-AL) Suspends microliter droplets in standing acoustic waves, enabling evaporation and analyte preconcentration [37] Non-contact handling; minimal contamination; effective preconcentration; reduced plasma quenching [37] Requires specialized equipment; droplet stability considerations Mineral waters: Ca, Mg, Na, K, Li at mg/L levels [37]
Cation Exchange Membrane (CEM) Extracts target cations from solution onto solid substrate for analysis [39] Automated operation; rapid analysis (5 min/sample); suitable for online monitoring [39] Limited to ionic species; membrane capacity constraints Ca²⁺: 160 mg/L; Mg²⁺: 48 mg/L [39]
Ion Enrichment Chip (IEC-LIBS) Microfluidic chip separates and enriches target ions before LIBS detection [6] Valence state discrimination; environmentally friendly; low detection limits [6] Chip fabrication complexity; potential for channel clogging Total Cr: 10 μg/L; Cr(VI): 4 μg/L in water [6]
Liquid Jet Continuous stream of liquid provides fresh surface for each laser pulse [37] Stable signal; reduced splashing; continuous flow analysis High sample consumption; complex fluid handling system Not specified in available literature
Nebulization Converts liquid to fine aerosol before plasma generation [12] Increased surface area; reduced quenching effects Transport inefficiencies; potential for memory effects Not specified in available literature

Signal Enhancement Approaches

Table 2: LIBS Signal Enhancement Techniques for Water Analysis

Technique Mechanism Improvement Factors Implementation Complexity
Double-Pulse LIBS Second laser pulse re-heats initial plasma, increasing emission intensity [12] 10-100x signal enhancement; improved LODs [12] High (requires precise temporal synchronization)
Matrix Enhancement Addition of nanoparticles or advanced materials to sample [39] Increased ablation efficiency; plasmonic effects Medium (requires material synthesis and optimization)
Magnetic Confinement Application of magnetic field to constrain plasma expansion Extended plasma lifetime; improved signal collection Medium (requires magnet integration)
Laser Wavelength Optimization Adjusting laser wavelength to match sample absorption characteristics Improved plasma initiation; more efficient energy coupling Low (equipment dependent)

Experimental Protocols

Protocol 1: Acoustic Levitation LIBS (LIBS-AL) for Mineral Waters

Workflow Description: This protocol utilizes acoustic levitation to suspend and preconcentrate microliter-sized water droplets, enabling calibration-free quantification of elements through LIBS analysis [37].

G Start Sample Introduction A1 Acoustic Levitation Setup Start->A1 A2 Droplet Positioning in Pressure Node A1->A2 A3 Controlled Evaporation (Preconcentration) A2->A3 A4 Laser Pulse Ablation of Concentrated Residue A3->A4 A5 Plasma Emission Collection A4->A5 A6 Saha-Boltzmann Analysis A5->A6 End Quantitative Results (Calibration-Free) A6->End

Materials and Equipment:

  • Single-axis acoustic levitator with concave transducers
  • High-energy pulsed laser (Nd:YAG, 1064 nm typical)
  • Echelle spectrometer with ICCD detector
  • Commercial mineral water samples
  • Micropipettes for precise droplet manipulation

Procedure:

  • Levitator Setup: Configure acoustic levitator to generate stable standing waves with clearly defined pressure nodes [37].
  • Droplet Introduction: Introduce 2-5 μL water droplet into the pressure node using precision micropipette [37].
  • Evaporation Phase: Allow controlled evaporation for 60-180 seconds (environment-dependent) to concentrate non-volatile analytes [37].
  • Laser Ablation: Focus laser pulse onto concentrated residue with energy 30-50 mJ per pulse [37].
  • Spectral Acquisition: Collect plasma emission with 1 μs delay time and 1 ms integration gate width [37].
  • CF-LIBS Analysis: Apply Saha-Boltzmann plot method to determine plasma temperature and electron density for calibration-free quantification [37].

Key Parameters:

  • Laser wavelength: 1064 nm
  • Pulse energy: 30-50 mJ
  • Spectral range: 200-1000 nm
  • Delay time: 1 μs
  • Gate width: 1 ms

Protocol 2: Automated Cation Detection System

Workflow Description: This protocol integrates cation exchange membranes with an automated LIBS system for rapid detection of Ca²⁺ and Mg²⁺ in water samples [39].

G Start Sample Injection B1 CEM Installation in Fixture Start->B1 B2 Stirred Adsorption (Uniform Exchange) B1->B2 B3 Automatic Transfer to Cleaning Station B2->B3 B4 Deionized Water Rinsing B3->B4 B5 Drying Phase B4->B5 B6 LIBS Measurement at Focal Plane B5->B6 B7 Multi-Position Averaging B6->B7 End Concentration Output B7->End

Materials and Equipment:

  • Cation exchange membrane (CMI-7000 S, thickness 0.42 mm)
  • Automated sampling system with 2D mobile component
  • Magnetic stirring device
  • Diode-pumped solid-state laser (1064 nm, 50 mJ max)
  • USB2000+ fiber optic spectrometer
  • Deionized water supply

Procedure:

  • Membrane Preparation: Saturate CEM in 1 mol/L HCl for 24 hours, rinse with deionized water to pH 7 [39].
  • System Initialization: Install prepared CEM in fixture and position sample container.
  • Automated Sampling: Inject sample solution (typically 10-50 mL) into container.
  • Adsorption Phase: Activate magnetic stirring for programmed duration (typically 2-5 minutes) to ensure uniform ion exchange [39].
  • Transfer and Cleaning: Move CEM to cleaning station using 2D mobile component, rinse with deionized water.
  • Drying Process: Remove residual moisture without disturbing adsorbed cations.
  • LIBS Analysis: Position CEM at laser focal plane and acquire spectra at 5 different positions.
  • Signal Averaging: Average multiple spectra to improve signal-to-noise ratio.

Key Parameters:

  • CEM area: 3 cm²
  • Laser energy: 50 mJ
  • Spectrometer range: 200-1100 nm
  • Acquisition spots: 5 per sample
  • Total cycle time: ~5 minutes

Protocol 3: Valence-State Specific Chromium Detection

Workflow Description: This protocol combines ion enrichment chip technology with LIBS for sensitive detection and discrimination of chromium valence states in water and soil [6].

Materials and Equipment:

  • Custom ion enrichment chip with millimetric channel
  • Peristaltic pump for precise fluid handling
  • Nd:YAG laser system
  • ICCD spectrometer
  • pH adjustment reagents

Procedure:

  • Chip Preparation: Condition IEC channel with appropriate buffer solution.
  • Sample Loading: Introduce water sample through IEC channel at controlled flow rate.
  • Selective Enrichment: Utilize channel properties to separate and enrich total Cr and Cr(VI).
  • Elution and Collection: Recover enriched analytes in minimal volume.
  • LIBS Analysis: Deposit enriched sample on suitable substrate for LIBS measurement.
  • Valence State Discrimination: Compare spectral features to differentiate Cr(III) and Cr(VI).

Key Parameters:

  • Detection limit for total Cr in water: 10 μg/L
  • Detection limit for Cr(VI) in water: 4 μg/L
  • Recovery rates: 90-114%

The Scientist's Toolkit

Table 3: Essential Research Reagent Solutions for LIBS Water Analysis

Reagent/Material Function Application Example Considerations
Cation Exchange Membrane (CMI-7000 S) Extracts and preconcentrates cationic species from solution [39] Ca²⁺ and Mg²⁺ detection in aquaculture water [39] Exchange capacity: 1.6 meq/g; pre-saturation in HCl required
Ion Enrichment Chip (IEC) Microfluidic separation and enrichment of target ions [6] Chromium speciation in environmental waters [6] Millimetric channel design; enables valence state discrimination
Acoustic Levitator Contactless suspension and preconcentration of microliter droplets [37] Calibration-free analysis of mineral waters [37] Requires precise standing wave generation; specialized equipment
Nanoparticles (Ag, Au, Pt) Signal enhancement through plasmonic effects and improved ablation efficiency [12] Trace heavy metal detection in drinking water [12] Controlled synthesis required; potential aggregation issues
Electrospray Deposition System Liquid-to-solid conversion for improved LIBS stability [12] Analysis of low-concentration aqueous samples Complex apparatus; requires optimization of spray parameters
Solid-Phase Extraction Materials Selective enrichment of target analytes [12] Preconcentration of trace contaminants Selectivity depends on functional groups; capacity limitations

Data Analysis and Interpretation

Spectral Processing and Quantitative Analysis

LIBS spectral data requires careful processing to extract meaningful quantitative information. The fundamental steps include:

  • Background Subtraction: Remove continuum radiation from plasma background
  • Peak Identification: Match observed emission lines to known atomic transitions using standard databases (e.g., NIST Atomic Spectra Database)
  • Peak Integration: Calculate area under characteristic emission lines
  • Calibration: Relate peak intensity/area to concentration using calibration curves or CF-LIBS approaches

For CF-LIBS analysis, the methodology involves:

  • Constructing Saha-Boltzmann plots to determine plasma temperature
  • Calculating electron density from Stark-broadened line profiles
  • Solving system of equations based on measured line intensities and plasma parameters
  • Deriving elemental concentrations without external calibration standards [37]

Complementary Data Integration

For comprehensive microplastic and additive screening, LIBS data should be integrated with complementary analytical approaches:

  • FT-IR Spectroscopy: Provides definitive polymer identification through characteristic functional group absorptions [38]
  • Raman Spectroscopy: Offers molecular fingerprinting capabilities, particularly effective for symmetric covalent bonds [38]
  • Chemometric Analysis: Multivariate statistical methods (PCA, PLS) for extracting meaningful patterns from complex spectral data

Applications and Case Studies

Real-World Environmental Monitoring

Recent studies demonstrate the practical application of advanced LIBS methodologies for environmental water screening:

  • Surface Water Contamination Assessment: CF-LIBS analysis of contaminated surface waters in Iraq successfully identified and quantified multiple hazardous metals (Hg, Pb, Cd, Fe), macronutrients (N, K, Ca, Mg), and micronutrients (Ti, Cr, Co) without extensive sample preparation [16]. The study revealed distinct elemental profiles across different sampling sites, highlighting LIBS' capability for rapid environmental assessment.
  • Drinking Water Quality Assurance: LIBS-AL analysis of commercial mineral waters (Ľubovnianka, Sulinka, Vincentka) enabled simultaneous quantification of major elements (Ca, Mg, Na) and trace elements (Li, K) with minimal sample volume, demonstrating the method's suitability for compliance testing and quality control [37].

Emerging Applications in Microplastic Analysis

While traditional LIBS focuses on elemental composition, emerging approaches leverage its capabilities for microplastic characterization:

  • Additive Screening: LIBS can detect heavy metal stabilizers (e.g., Cd, Pb), colorants, and flame retardants associated with plastic materials
  • Polymer Identification: Through multivariate analysis of elemental fingerprints, particularly heteroatoms (Cl, F, N) characteristic of specific polymer classes
  • Degradation Assessment: Monitoring of environmental weathering through changes in surface composition and additive leaching

The integration of advanced sample introduction systems including acoustic levitation, cation exchange membranes, and ion enrichment chips with LIBS spectroscopy has significantly expanded the technique's capabilities for water contaminant screening. These methodologies overcome the traditional limitations of liquid LIBS analysis, enabling sensitive detection of metallic elements, potential characterization of microplastics and additives, and valence-state specific quantification of environmentally relevant elements.

The protocols detailed in this application note provide researchers with robust methodologies for implementing these advanced LIBS approaches in both laboratory and potential field settings. As LIBS technology continues to evolve, particularly with improvements in instrumental sensitivity and data analysis algorithms, its role in comprehensive water quality assessment including microplastic contamination is expected to expand significantly, offering rapid, multi-parameter screening capabilities complementary to established spectroscopic techniques.

Enhancing LIBS Performance: Strategies for Sensitivity and Accuracy

Overcoming Matrix Effects and Signal Fluctuations

Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as a powerful analytical technique for multi-elemental analysis, offering rapid, minimally destructive testing with minimal sample preparation. However, its quantitative analytical performance, particularly for complex liquid matrices like contaminated water, is significantly compromised by matrix effects and signal fluctuations [40]. These phenomena introduce substantial variability in plasma characteristics and elemental emission signals, thereby limiting the accuracy, precision, and detection sensitivity of LIBS measurements.

Matrix effects in aqueous analysis primarily arise from the fundamental physical differences between liquid and solid samples. The aqueous matrix itself contributes to strong background emission, while the high density and thermal conductivity of water lead to rapid plasma quenching, shortened lifetime, and unstable plasma formation [41] [40]. Furthermore, the dynamic surface tension and liquid flow characteristics often result in signal flickering and splashing during laser ablation. Signal fluctuations are further exacerbated by variations in laser energy output, shot-to-shot focusing inconsistencies, and sample surface heterogeneity, even in seemingly homogeneous liquids [42].

Understanding and mitigating these challenges is paramount for advancing LIBS applications in environmental monitoring, particularly for screening heavy metal contaminants in water. This application note provides a comprehensive overview of proven methodologies to overcome these limitations, featuring detailed protocols, performance comparisons, and practical implementation strategies tailored for researchers and analytical scientists.

Technical Approaches and Methodologies

Sample Pre-Treatment and Preconcentration Strategies

Sample pre-treatment represents the most effective approach for mitigating aqueous matrix effects, primarily by physically separating and concentrating the target analytes from the liquid medium.

2.1.1 Liquid-to-Solid Conversion (LSC)

LSC is the predominant pre-treatment method, accounting for approximately 50% of all aqueous LIBS applications [41]. This approach involves transferring dissolved analytes onto a solid substrate, effectively eliminating the problematic liquid matrix and providing a stable surface for laser ablation. The most common LSC techniques include:

  • Filter Deposition: Passing water samples through filter membranes to capture suspended particles or precipitates.
  • Evaporation & Calcination: Drying liquid droplets on a substrate followed by thermal treatment to remove volatile components.
  • Sorption & Preconcentration: Using functionalized substrates or nanoparticles to selectively adsorb target elements.

2.1.2 Chelator-Assisted Preconcentration

A recently demonstrated advanced LSC technique utilizes chelating agents like sodium diethyldithiocarbamate (DDTC) to form stable complexes with metal ions such as Pb²⁺ [43]. These complexes are subsequently centrifuged, deposited on a graphite substrate, and analyzed using spatially and temporally focused LIBS (SSTF-LIBS). This method has achieved remarkable detection limits of 2.82 ng/mL and 3.64 ng/mL for Pb²⁺ in tap water and river water, respectively, representing a 6.4-fold and 6.3-fold improvement over direct liquid analysis [43].

Table 1: Comparison of Aqueous LIBS Pre-Treatment Methods

Method Principle Key Advantages Typical Detection Limit Improvement Target Elements
Direct Liquid Analysis Laser ablation of liquid surface Minimal sample preparation; Rapid analysis Baseline Broad range, but with high LODs
Liquid-to-Solid Conversion (LSC) Analyte transfer to solid matrix Eliminates plasma quenching; Improves stability 10-100x [41] Metals, alkali/alkaline earth
Surface-Enhanced LSC (SE-LSC) LSC with signal-enhancing substrates Additional signal enhancement; Better LODs >100x for some elements [41] Trace metals
Chelator-Assisted Preconcentration Chemical complexation & centrifugation High selectivity; Exceptional LODs >600% for Pb²⁺ [43] Heavy metals (Pb, Cd, Hg, etc.)
Liquid-Gas Conversion (LAC) Analyte introduction as aerosol Continuous flow capability Moderate Volatile elements
Instrumental and Laser-Based Enhancements

Instrumental modifications provide an alternative pathway for signal stabilization and enhancement without extensive sample preparation.

2.2.1 Dual-Pulse LIBS (DP-LIBS)

DP-LIBS employs two sequential laser pulses—the first to generate a vapor cloud and the second to re-excite the ablated material. This configuration significantly enhances emission intensity and plasma temperature while reducing background continuum radiation [40].

2.2.2 Spatial and Temporal Focusing (SSTF-LIBS)

As implemented in the chelator-assisted protocol, SSTF-LIBS synchronizes laser pulses in both space and time, creating precisely controlled plasma conditions that minimize signal fluctuations and improve ablation efficiency [43].

2.2.3 Nanoparticle-Enhanced LIBS

The introduction of metal nanoparticles (e.g., gold or silver) to the sample matrix or substrate surface can induce localized surface plasmon resonance effects, dramatically enhancing the electromagnetic field in the ablation region and subsequently amplifying the LIBS signal intensity [40].

Data Processing and Algorithmic Solutions

Advanced computational methods address signal variations that persist despite physical and instrumental optimizations.

2.3.1 Machine Learning for Quantification

Machine learning algorithms, including XGBoost and neural networks, effectively model the complex, non-linear relationships between spectral features and elemental concentrations, compensating for matrix-induced interferences [44] [40].

2.3.2 Data Augmentation with Generative Models

When training data is limited, Generative Adversarial Networks (GANs) can create synthetic LIBS spectra that expand the diversity of the training dataset, improving the robustness and predictive accuracy of quantitative models against matrix effects and signal noise [44].

Experimental Protocols

Protocol 1: Chelator-Assisted Preconcentration for Trace Lead Detection

This protocol details the procedure for achieving ng/mL level detection of Pb²⁺ in water samples using DDTC chelation and SSTF-LIBS analysis [43].

Materials and Reagents

Table 2: Research Reagent Solutions for Chelator-Assisted LIBS

Item Function/Benefit Specification/Notes
Sodium Diethyldithiocarbamate (DDTC) Chelating agent for Pb²⁺ ions Forms stable, insoluble complex with Pb²⁺ [43]
Graphite Substrate Sample holder for preconcentrated analytes Provides clean, low-background surface for LIBS analysis [43]
Centrifuge Separates Pb-DDTC complex from solution Optimal at 14,000 rpm (17,800 g) for 5 minutes [43]
Titanium-Sapphire Femtosecond Laser Ablation source for SSTF-LIBS 800 nm, 100 Hz, 50 fs pulse duration [43]
Synchronized Grating & Lens System Enables spatial and temporal focusing -1 order diffraction from 600 l/mm grating [43]
Step-by-Step Procedure

  • Sample Collection and Preparation:

    • Collect 100 mL of water sample (tap water, river water, or standard solution).
    • Filter through a 0.45 μm membrane to remove suspended particulates.

  • Chelation Reaction:

    • Add DDTC to the filtered sample at a final concentration of 0.25 mg/mL.
    • Mix thoroughly and let stand for 10 minutes to ensure complete chelate formation.

  • Centrifugation:

    • Transfer the solution to centrifuge tubes.
    • Centrifuge at 14,000 rpm (approximately 17,800 g) for 5 minutes to pellet the Pb-DDTC complex.
    • Carefully decant the supernatant.

  • Sample Deposition:

    • Re-suspend the pellet in a minimal volume of deionized water.
    • Deposit the suspension onto a clean graphite substrate.
    • Allow to air-dry completely at room temperature.

  • SSTF-LIBS Analysis:

    • Place the substrate in the SSTF-LIBS instrument.
    • Align the laser focus precisely on the dried sample spot.
    • Acquire spectra using the following typical parameters:
      • Laser wavelength: 800 nm
      • Pulse duration: 50 fs
      • Pulse energy: 0.5-2.8 mJ
      • Repetition rate: 100 Hz
      • Gate delay: 1 μs
      • Gate width: 2 μs

  • Data Analysis:

    • Identify the Pb emission line at 405.78 nm.
    • Construct a calibration curve using Pb standard solutions processed identically.
    • Apply necessary chemometric corrections for accurate quantification.
Critical Parameters for Optimization
  • DDTC Concentration: 0.25 mg/mL provides optimal chelation efficiency. Higher concentrations may form soluble complexes reducing precipitation yield [43].
  • Centrifugation Time: 5 minutes is sufficient for complete precipitation. Prolonged centrifugation does not significantly improve recovery [43].
  • Laser Energy Stability: Maintain consistent pulse energy (<2% fluctuation) to minimize signal variations.

The workflow for this protocol is systematically presented below:

G start Start Water Sample Analysis filter Filter Sample (0.45 μm) start->filter chelate Add DDTC Chelator (0.25 mg/mL) filter->chelate incubate Incubate 10 min chelate->incubate centrifuge Centrifuge (14,000 rpm, 5 min) incubate->centrifuge deposit Deposit Pellet on Graphite Substrate centrifuge->deposit dry Air Dry deposit->dry analyze SSTF-LIBS Analysis dry->analyze measure Measure Pb 405.78 nm Line analyze->measure results Quantitative Results measure->results

Protocol 2: Data Augmentation for Improved Quantification

This protocol outlines a computational approach to enhance LIBS quantitative models using data augmentation, particularly beneficial when analyzing complex plant or biological matrices with significant heterogeneity [44].

Materials and Software Requirements
  • LIBS Spectrometer System with reproducible sampling capability
  • Python with TensorFlow/Keras or PyTorch libraries
  • Standard Reference Materials for model validation
  • Computing Workstation with adequate GPU resources
Step-by-Step Procedure

  • Spectral Data Acquisition:

    • Collect LIBS spectra from multiple points on homogenized standard samples.
    • Ensure representative sampling across the concentration range of interest.
    • Record corresponding reference concentrations from certified values or ICP-MS analysis.

  • Data Preprocessing:

    • Apply wavelength calibration to all spectra.
    • Perform intensity normalization (e.g., vector normalization, internal standard).
    • Remove cosmic ray spikes and smooth spectra if necessary.
    • Split data into training, validation, and test sets (e.g., 70:15:15 ratio).

  • Generative Adversarial Network (GAN) Training:

    • Configure a GAN architecture with:
      • Generator: Multi-layer perceptron with spectral noise input
      • Discriminator: Pattern recognition network for real/fake classification
    • Train using Wasserstein distance loss function for improved stability.
    • Monitor training progress to prevent mode collapse.

  • Spectral Data Augmentation:

    • Use the trained generator to create synthetic LIBS spectra.
    • Maintain realistic correlation structures between elemental lines.
    • Expand the training dataset by 3-5x while preserving spectral authenticity.

  • Quantitative Model Development:

    • Train multiple machine learning models (PLS, XGBoost, Neural Networks) on both original and augmented datasets.
    • Compare model performance using cross-validation on the test set.
    • Select the optimal model based on lowest RMSE and highest R² values.

  • Model Validation:

    • Validate the final model using independent test samples not used in training.
    • Assess prediction accuracy across the applicable concentration range.

The relationship between data processing components and their functions is illustrated below:

G real_data Original LIBS Spectral Data preprocess Preprocessing: Normalization, Calibration real_data->preprocess gan_training GAN Training (Wasserstein Distance) preprocess->gan_training combined_data Augmented Training Dataset preprocess->combined_data generator Trained Generator gan_training->generator synthetic_data Synthetic LIBS Spectra generator->synthetic_data synthetic_data->combined_data quant_model Quantitative Model (PLS, XGBoost, Neural Net) combined_data->quant_model final_model Validated Predictive Model quant_model->final_model

Performance Comparison and Data Analysis

Element-Specific Detection Capabilities

The effectiveness of matrix effect mitigation strategies varies significantly across different elements due to their unique physicochemical properties and spectroscopic characteristics.

Table 3: Typical Detection Limits (LOD) Achievable with Various LIBS Approaches for Water Analysis

Element Group Representative Elements Direct Liquid LIBS LOD (ppm) LSC-Based LOD (ppm) Advanced Methods LOD (ppm) Notes
Alkali Metals Li, Na, K 0.1-1 0.01-0.1 <0.01 (with SE-LSC) Low ionization energy enables high sensitivity [41] [42]
Alkaline Earth Metals Mg, Ca, Sr 1-10 0.1-1 0.05-0.5 (with DP-LIBS) Strong emission lines, moderate sensitivity [41]
Transition Metals Cr, Fe, Ni, Cu, Zn 10-100 1-10 0.1-1 (with Chelation) Environmental significance; Cr and Pb most studied [41] [43]
Toxic Heavy Metals Pb, Cd, Hg 10-50 1-5 0.001-0.005 (with Chelation) DDTC chelation enables ng/mL detection [43]
Non-Metals/ Halogens Cl, Br, F, S, P >500 50-100 ~10 (with LAC) High excitation energy; lines in IR/UV range [41]
Precision and Accuracy Assessment

Method precision in LIBS analysis is typically expressed as Relative Standard Deviation (RSD). With optimal experimental configurations, RSD values of 3-5% can be achieved for homogeneous liquid samples, improving to 1-2% for solid preconcentrates [42]. Accuracy, expressed as relative error from reference values, depends heavily on proper calibration strategy:

  • External Calibration: Suitable for simple matrices with minimal inter-element interference.
  • Internal Standardization: Essential for complex matrices, using an added element or existing major element as reference.
  • Chemometric Correction: Most effective for highly complex samples, using multivariate models to account for interferences.

For plant tissue analysis using the data augmentation protocol, prediction accuracy with R² values exceeding 0.90 has been demonstrated for multiple elements, a significant improvement over conventional linear regression models [44].

Matrix effects and signal fluctuations present significant but surmountable challenges in LIBS analysis of contaminated waters. The optimal approach combines appropriate sample preparation with instrumental optimization and advanced data processing:

  • For routine screening of multiple elements at moderate detection limits (ppb-ppm range), Liquid-to-Solid Conversion (LSC) methods provide the best balance of performance and practicality.

  • For ultratrace analysis of specific heavy metals requiring ng/mL sensitivity, chelator-assisted preconcentration combined with spatially and temporally resolved LIBS offers unparalleled performance.

  • For complex, heterogeneous samples where physical preparation is constrained, data augmentation coupled with machine learning quantification delivers significant accuracy improvements.

The continuing evolution of LIBS technology, particularly through the integration of novel preconcentration strategies, advanced laser configurations, and artificial intelligence, promises to further overcome current limitations and expand the application boundaries of this versatile analytical technique in environmental monitoring and contamination screening.

Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as a powerful analytical technique for the direct analysis of contaminated water, offering capabilities for rapid, in-situ monitoring of heavy metals and other pollutants with minimal sample preparation. However, its application to liquid samples presents significant challenges, including diminished plasma lifetime, splashing effects, and lower signal-to-noise ratios compared to solid sample analysis. These technical constraints ultimately degrade the detection limits and analytical precision necessary for environmental monitoring standards. Two particularly effective methods for overcoming these limitations are inert gas purging and spectral averaging. This application note details standardized protocols for implementing these signal enhancement techniques within the context of a research thesis focused on LIBS development for contaminated water screening, providing methodologies specifically tailored for researchers and scientists in analytical chemistry and environmental monitoring.

Inert Gas Purging for Underwater LIBS Plasma Enhancement

Principle and Mechanism

Inert gas purging operates on the principle of displacing the water or water vapor surrounding the laser-induced plasma with an inert gas, typically nitrogen (N₂) or argon (Ar). This creates a localized gaseous environment that is more favorable for plasma formation and sustenance. The core mechanisms behind the signal enhancement include:

  • Plasma Confinement: The inert gas confines the plasma, reducing its cooling rate and extending its lifetime, which allows more atomic emissions to be collected [45].
  • Reduced Quenching: Collisional de-excitation of excited species by water molecules is minimized, leading to more efficient light emission from the analyte elements [36].
  • Suppression of Splashing: The purging process can mitigate the splashing effect caused by laser-induced shock waves in liquids, leading to improved plasma stability and signal reproducibility [45].

Research Reagent Solutions and Essential Materials

Table 1: Essential Materials for Inert Gas Purging in LIBS

Item Specification/Function
Inert Gas High-purity Nitrogen (N₂) or Argon (Ar); displaces water/air to create stable plasma environment [46].
Mass Flow Controller (MFC) Digital MFC (e.g., Sensirion SFC5500/SFC6000D); provides precise, stable gas flow control for reproducible purging [47].
Purging Chamber/Nozzle Custom-designed cell or submerged nozzle; creates a localized gas-filled cavity or steady gas flow over the liquid surface [45].
Gas Delivery Tubing Chemically inert tubing (e.g., PTFE); ensures clean delivery of purge gas without introducing contaminants.
Laser Source Q-switched Nd:YAG laser (typical: 1064 nm, nanosecond pulse width); generates the plasma in the purged volume [45] [36].

Experimental Protocol for Submerged Target Purging

Objective: To enhance LIBS signal intensity and stability from a contaminated water sample by creating a transient local gas environment using a submerged purging nozzle.

Materials and Reagents:

  • Prepared standard solutions of target analytes (e.g., Cu, Pb, Cd) in deionized water.
  • High-purity nitrogen gas cylinder (≥99.99%).
  • PTFE tubing and fittings.

Equipment:

  • Q-switched Nd:YAG laser system.
  • Spectrometer with detector (ICCD or non-gated).
  • Mass Flow Controller (MFC).
  • Custom purging apparatus with a submerged nozzle.
  • Sample container and optical stages.

Procedure:

  • Setup Assembly: Connect the PTFE tubing from the nitrogen gas cylinder, through the MFC, to the submerged purging nozzle. The nozzle should be positioned to direct a vertical gas stream towards the water surface, immediately adjacent to the laser focal point.
  • Laser and Optical Alignment: Focus the laser beam approximately 1-2 mm above the nozzle outlet, ensuring the resulting plasma plume will be fully immersed in the gas flow. Align the collection optics to gather plasma emission from this region.
  • System Parameter Optimization:
    • Gas Flow Rate: Using the MFC, adjust the N₂ flow rate. A typical range is 0.5 - 2.0 liters per minute (L/min). Optimize for the formation of a stable, visible gas cavity with minimal surface turbulence [47] [46].
    • Laser Energy: Set the laser output energy to a level sufficient to generate a robust plasma. Often this is in the range of 50-100 mJ/pulse, but it requires optimization based on the specific setup.
    • Data Acquisition Delay: Set the detector delay time relative to the laser pulse. The optimal delay is typically between 1-5 µs, which allows the continuum background radiation to decay while capturing the persistent atomic emission lines.
  • Data Collection: Acquire LIBS spectra from the standard solutions. For each sample and condition, collect a minimum of 50-100 spectra to enable subsequent spectral averaging.

Safety Notes:

  • Wear appropriate laser safety goggles.
  • Ensure the experimental setup is well-ventilated, especially when analyzing volatile organic compounds.
  • Securely fasten all gas connections to prevent leaks.

Spectral Averaging for Noise Reduction

Principle and Mechanism

Spectral averaging is a fundamental signal processing technique that improves the signal-to-noise ratio (SNR) by accumulating and averaging multiple spectra from the same sample location or under identical conditions. The underlying principle is that the desired analytical signal (elemental emission lines) is consistent and reproducible from shot-to-shot, while the noise (primarily from plasma fluctuations and detector readout) is random. Averaging N spectra theoretically improves the SNR by a factor of √N. This is particularly crucial in underwater LIBS, where plasma instability can lead to significant signal fluctuations [45] [36].

Experimental Protocol for Spectral Averaging

Objective: To reduce random noise in LIBS spectra, thereby improving the limit of detection and the reliability of qualitative and quantitative analysis.

Procedure:

  • Fixed Position Measurement: For a homogeneous liquid sample, focus the laser on a single spot. If using a flow cell, ensure a consistent, representative sample stream passes through the analysis point.
  • Pulse Train Acquisition: Acquire a sequence of single-shot LIBS spectra (a "packet") from the sample. A typical packet may contain 100 to 500 pulses.
  • Data Pre-processing:
    • Background Subtraction: Subtract the spectral background, which may include dark current of the detector and ambient light, from each individual spectrum.
    • Outlier Rejection: Implement an algorithm to identify and remove spectral outliers. A common method is to calculate the standard deviation of the intensity at a specific, stable wavelength for all spectra and reject those with intensities beyond ±3 standard deviations from the mean.
    • Wavelength Calibration: Ensure all spectra are accurately aligned to the wavelength axis using a reference spectrum from a standard lamp or a known elemental source.
  • Averaging: Compute the average intensity for each wavelength channel across all accepted spectra in the packet to produce a single, high-SNR representative spectrum for that sample.
  • Validation: Compare the SNR of the averaged spectrum to that of a single-shot spectrum. The SNR can be calculated as the peak intensity of an analyte line divided by the standard deviation of the background in a nearby, signal-free region.

Integrated Workflow and Data Analysis

Combined Application Workflow

For optimal results in contaminated water screening, inert gas purging and spectral averaging should be employed together. The following workflow diagram illustrates the integrated experimental and data processing sequence.

G Start Start Experiment Setup Setup LIBS and Purging Start->Setup OptGas Optimize Gas Flow Setup->OptGas OptLaser Optimize Laser Parameters OptGas->OptLaser Acquire Acquire Spectral Packet (100-500 shots) OptLaser->Acquire PreProcess Pre-process Spectra Acquire->PreProcess Average Compute Averaged Spectrum PreProcess->Average Analyze Quantitative Analysis Average->Analyze End Report Results Analyze->End

Quantitative Data Comparison

The efficacy of these techniques is demonstrated through their impact on key analytical figures of merit.

Table 2: Impact of Enhancement Techniques on LIBS Analytical Performance

Technique Key Parameter Typical Improvement/Value Notes
Inert Gas Purging Signal Intensity Increase of 2x to 10x Highly dependent on gas type, flow rate, and geometry [45].
Plasma Lifetime Extension to ~1-5 µs Allows for more effective time-gated detection [45].
Spectral Averaging Signal-to-Noise Ratio (SNR) Improves by ~√N (N=number of shots) Fundamental statistical limit; practical gains may be lower due to outliers [36].
Combined Approach Limit of Detection (LOD) Can be reduced by an order of magnitude LOD for Mg in water jet reported as low as 0.1 ppm [45].
Measurement Precision (RSD) Can improve from >20% to <10% Reduction in shot-to-shot fluctuation [45] [36].

Advanced Technique: Double-Pulse LIBS with Purging

For further signal enhancement, Double-Pulse (DP) LIBS can be integrated with gas purging. In this configuration, the first laser pulse generates a cavitation bubble in the water, and the second pulse is fired into the gas-filled bubble to produce a more robust plasma. The optimal inter-pulse delay is typically 15-30 µs, corresponding to the maximum expansion of the bubble before collapse. This synergistic approach can lead to signal enhancements an order of magnitude greater than single-pulse LIBS and can further reduce matrix effects, providing a powerful tool for complex contaminated water matrices [45].

The Role of Chemometrics and Machine Learning in Data Analysis

The integration of chemometrics and machine learning (ML) with laser-induced breakdown spectroscopy (LIBS) represents a paradigm shift in spectroscopic analysis, particularly for the screening of contaminated water. This application note details how these data analysis techniques transform complex spectral data into actionable insights for environmental monitoring. We provide a comprehensive overview of methodological frameworks, experimental protocols, and practical tools that enable researchers to leverage artificial intelligence (AI) for enhanced detection of heavy metals and other contaminants in water systems, facilitating rapid, on-site analysis crucial for timely environmental intervention.

Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as a powerful analytical technique for elemental analysis due to its minimal sample preparation requirements, capability for remote sensing, and rapid analysis times [48] [17]. The technique operates by focusing a high-energy laser pulse onto a sample, generating a microplasma whose emitted light is characteristic of the elemental composition of the ablated material [17]. However, LIBS spectra are inherently complex, containing substantial background noise, matrix effects, and overlapping spectral lines that challenge conventional analytical approaches [48].

Chemometrics and machine learning address these challenges by providing mathematical frameworks to extract meaningful chemical information from multivariate spectral data [49]. Within the context of contaminated water screening, these methods enable researchers to identify and quantify trace contaminants such as heavy metals—including chromium, cadmium, and lead—even in complex environmental samples [50]. The synergy between LIBS and machine learning is particularly valuable for developing portable field-deployable systems that offer real-time monitoring capabilities essential for environmental protection agencies and water management authorities [13].

Fundamental Concepts

Chemometrics in Spectroscopy

Chemometrics encompasses statistical and mathematical methods for extracting relevant chemical information from analytical data. In spectroscopic applications, key methods include:

  • Principal Component Analysis (PCA): An unsupervised technique for dimensionality reduction and exploratory data analysis that identifies patterns and outliers in spectral datasets [49].
  • Partial Least Squares (PLS) Regression: A supervised method that finds latent variables maximizing covariance between spectral data and analyte concentrations, widely used for quantitative analysis [51].
  • Multivariate Curve Resolution: Decomposes complex spectral mixtures into constituent component profiles [49].
Machine Learning Frameworks

Machine learning extends traditional chemometrics through automated pattern recognition and predictive modeling:

  • Supervised Learning: Algorithms trained on labeled data to perform regression (quantitative analysis) or classification (qualitative analysis) tasks [49].
  • Unsupervised Learning: Algorithms that discover latent structures in unlabeled data through clustering or dimensionality reduction [49].
  • Deep Learning: Utilizes multi-layered neural networks capable of hierarchical feature extraction from raw or minimally preprocessed spectral data [52] [49].

Experimental Protocols for Contaminated Water Screening

LIBS Analysis of Water Samples

Protocol Objective: Direct analysis of calcium and magnesium in surface water using portable LIBS [13].

Materials and Equipment:

  • Portable LIBS spectrometer with miniaturized components
  • Liquid jet sample introduction system
  • Standard solutions for calibration (Ca and Mg)
  • Surface water samples (river, pond, or industrial runoff)

Procedure:

  • System Setup: Configure the liquid jet apparatus to produce a stable stream with diameter of 0.64 mm.
  • Laser Alignment: Position the laser ablation point 5 mm from the jet outlet for optimal signal stability.
  • Signal Optimization: Acquire and average 20 individual spectra to reduce relative standard deviation (RSD) from approximately 16% to 2%.
  • Calibration: Analyze standard solutions with known concentrations of Ca and Mg to build calibration models.
  • Sample Analysis: Directly introduce water samples into the liquid jet system and acquire LIBS spectra.
  • Data Processing: Apply machine learning algorithms to predict contaminant concentrations.

Validation:

  • Calculate recovery rates for quality control (acceptable range: 90-108%)
  • Report detection limits (Ca: 11.58 mg/L, Mg: 2.57 mg/L)
  • Compare results with reference methods where available
Machine Learning Workflow for Spectral Analysis

Protocol Objective: Implement chemometric models for quantitative analysis of heavy metals in water.

Procedure:

  • Data Preprocessing:
    • Apply smoothing algorithms (Savitzky-Golay) to reduce spectral noise
    • Perform baseline correction to remove background interference
    • Normalize spectra to correct for intensity variations
    • Employ wavelet transforms for signal denoising [51]

  • Feature Selection:

    • Identify characteristic emission lines for target contaminants
    • Apply variable importance measures (e.g., Random Forest feature ranking)
    • Use interval PLS (iPLS) to select informative spectral regions [51]

  • Model Development:

    • Split data into training (70%), validation (15%), and test (15%) sets
    • Train multiple algorithms (PLS, SVM, Random Forest, CNN)
    • Optimize hyperparameters through cross-validation
    • For deep learning approaches, use convolutional neural networks (CNNs) with 1D convolutional layers for spectral feature extraction [51]

  • Model Validation:

    • Evaluate performance using root mean square error (RMSE), coefficient of determination (R²)
    • Assess robustness through cross-validation and external test sets
    • Apply model interpretation techniques (SHAP, Grad-CAM) to identify influential spectral regions [49]

The following workflow diagram illustrates the complete process from sample collection to final analysis:

cluster_1 Data Acquisition cluster_2 Chemometric Processing cluster_3 Machine Learning SampleCollection SampleCollection LIBSAnalysis LIBSAnalysis SampleCollection->LIBSAnalysis SampleCollection->LIBSAnalysis SpectralPreprocessing SpectralPreprocessing LIBSAnalysis->SpectralPreprocessing FeatureSelection FeatureSelection SpectralPreprocessing->FeatureSelection SpectralPreprocessing->FeatureSelection ModelTraining ModelTraining FeatureSelection->ModelTraining Validation Validation ModelTraining->Validation ModelTraining->Validation Deployment Deployment Validation->Deployment Validation->Deployment

Performance Metrics and Analytical Figures of Merit

Table 1: Quantitative Performance of LIBS with Chemometrics for Water Analysis

Analyte Detection Limit Recovery Rate (%) Optimal Model Key Spectral Lines (nm)
Calcium 11.58 mg/L 90.83-101.74 PLS Ca II 393.3, Ca II 396.8
Magnesium 2.57 mg/L 93.43-108.74 SVM Mg I 285.2, Mg II 279.5
Chromium Literature values: ~0.5-5 mg/L 85-110 Random Forest Cr I 425.4, Cr II 267.7
Cadmium Literature values: ~1-10 mg/L 85-110 CNN Cd I 228.8, Cd I 326.1
Lead Literature values: ~2-15 mg/L 85-110 CNN Pb I 283.3, Pb I 405.8

Table 2: Comparison of Machine Learning Algorithms for LIBS Data Analysis

Algorithm Best For Advantages Limitations Implementation Considerations
PLS Quantitative analysis of major elements Simple, interpretable, works well with limited samples Assumes linear relationships Combine with iPLS for feature selection
SVM Classification of contaminant types Effective in high-dimensional spaces, handles nonlinearity Memory-intensive for large datasets Kernel selection critical (RBF recommended)
Random Forest Multi-element screening Robust to outliers, provides feature importance Less interpretable than linear models Tune number of trees and depth parameters
CNN Complex mixture analysis Automatic feature extraction, handles raw spectra Requires large training datasets Use 1D convolutional layers for spectra
XGBoost Challenging quantification tasks High accuracy, handles missing data Prone to overfitting without careful tuning Regularization parameters essential

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Research Reagent Solutions for LIBS Water Analysis

Item Function Application Notes
Portable LIBS Spectrometer Field-deployable elemental analysis Integrated liquid jet module essential for water analysis
Certified Reference Materials Quality control and method validation Include traceable heavy metal standards
Liquid Jet Sample Introduction System Stable presentation of liquid samples Optimal diameter: 0.64 mm; laser positioning critical
Spectral Calibration Standards Wavelength and intensity calibration Argon or neon lamps for wavelength; neutral density filters for intensity
Chemometric Software Platform Data preprocessing and model development Python with scikit-learn, MATLAB, or specialized chemometrics packages
Sample Filtration System Removal of particulate matter 0.45 μm filters to prevent nozzle clogging
pH Adjustment Reagents Sample preservation and standardization Nitric acid for metal stabilization

Advanced Applications in Environmental Screening

The combination of LIBS with machine learning enables several advanced applications for contaminated water screening:

Heavy Metal Detection and Speciation

Machine learning algorithms, particularly deep neural networks, can identify and quantify toxic heavy metals such as chromium, cadmium, and lead in environmentally aged microplastics and water samples [50]. PCA-based approaches facilitate the discrimination between different contamination sources and speciation states, which is critical for assessing environmental risk and designing remediation strategies.

Multi-Element Simultaneous Analysis

Ensemble methods like Random Forest and Extreme Gradient Boosting (XGBoost) enable the simultaneous quantification of multiple elements in complex water matrices [49]. These approaches automatically handle spectral interferences and matrix effects that would otherwise require extensive sample preparation with traditional analytical techniques.

Real-Time Monitoring and Early Warning Systems

The integration of portable LIBS systems with cloud-based machine learning models enables the development of continuous monitoring networks for watershed protection [13]. These systems can trigger alerts when contaminant levels exceed regulatory thresholds, allowing for rapid response to pollution events.

Troubleshooting and Method Validation

Common Challenges and Solutions:
  • Spectral Instability: Implement robust signal averaging and internal standardization protocols to improve reproducibility.
  • Matrix Effects: Use standard addition methods or machine learning approaches that are inherently resistant to matrix variations.
  • Limit of Detection Limitations: Employ nanoparticle-enhanced LIBS substrates or pre-concentration techniques for trace analysis [17].
  • Model Overfitting: Apply regularization techniques, feature selection, and cross-validation to ensure model generalizability.
Validation Requirements:

For regulatory applications, method validation should include:

  • Determination of accuracy (recovery studies) and precision (replicate analysis)
  • Establishment of detection and quantification limits
  • Demonstration of specificity through analysis of potential interferents
  • Assessment of model robustness to instrumental drift and environmental conditions

The integration of chemometrics and machine learning with LIBS technology has transformed the landscape of contaminated water screening, enabling rapid, accurate, and field-deployable analytical capabilities. The protocols and applications detailed in this document provide researchers with a comprehensive framework for implementing these advanced data analysis techniques in environmental monitoring contexts. As machine learning algorithms continue to evolve and portable LIBS systems become increasingly sophisticated, this synergistic approach promises to play an increasingly vital role in safeguarding water resources and public health through enhanced contaminant detection and quantification capabilities. Future developments will likely focus on autonomous calibration systems, explainable AI for regulatory acceptance, and miniaturized sensor networks for comprehensive watershed monitoring.

Laser-Induced Breakdown Spectroscopy (LIBS) is a rapid, versatile analytical technique that uses a high-power laser pulse to generate a micro-plasma on a sample surface, whose emitted light is then analyzed to determine elemental composition [53]. The analytical performance of LIBS, including its sensitivity, signal-to-noise ratio, and limit of detection, is highly dependent on the optimization of key operational parameters [12]. Within the specific context of contaminated water screening, where target analytes like heavy metals are often present at trace concentrations, this optimization becomes critical for obtaining reliable data [12]. This application note provides detailed protocols and data for researchers to systematically optimize laser energy, temporal gating, and focal conditions for LIBS analysis of aqueous samples.

The Scientist's Toolkit: Essential LIBS Components

A typical LIBS system for water analysis integrates several key components, from laser ablation to spectral detection. The table below details the essential research reagent solutions and hardware required.

Table 1: Key Research Reagent Solutions and Essential Materials for LIBS in Water Analysis

Item Name Function/Description Typical Specifications/Examples
Pulsed Laser System Generates high-energy pulses to ablate sample and form plasma. Nd:YAG laser (e.g., 1064 nm, 532 nm), ~5-20 ns pulse duration, tens to hundreds of mJ/pulse [54] [55].
Spectrometer Disperses plasma light into its constituent wavelengths for elemental identification. Echelle type for wide coverage or compact CCD for portability; resolution critical for line separation [54] [13].
Gated Detector (ICCD) Time-resolves signal, blocking early continuum radiation and collecting atomic/ionic line emission. Intensified CCD; gate times/delays of several microseconds typical [54] [53].
Sample Introduction System Presents liquid sample to laser in a stable, reproducible manner to mitigate splashing and signal instability. Liquid jet [13], magnetic confinement [56], or conversion to aerosol [12].
Focusing Lens Focuses laser to a small spot, achieving high power density for plasma ignition. Focal length (e.g., 75mm [55], 500mm [56]) critical for spot size and power density.
Optical Fibre Transmits collected plasma light from sample to spectrometer. High-OH, core diameter ~600 µm [57].
Data Analysis Software Performs qualitative and quantitative analysis, including peak identification, calibration, and chemometrics. Commercial spectrometer software or custom algorithms (e.g., via Origin Pro [57]).

Core Parameter Optimization and Protocols

Laser Energy and Focus Position

The energy of the laser pulse and its focal position relative to the sample surface directly influence plasma properties, ablation efficiency, and signal intensity.

Experimental Protocol: Optimizing Laser Energy and Focal Position

  • Preparation: Use a stable liquid jet system with a diameter of approximately 0.64 mm. Ensure the sample vessel is sealed in an argon atmosphere if analyzing reactive fluids [56].
  • Energy Ramp-Up: Begin with low pulse energy (e.g., 20 mJ). Gradually increase the energy in steps (e.g., 20 mJ increments up to 140 mJ [57]), recording LIBS spectra at each step.
  • Signal Monitoring: Monitor the intensity of a key analyte line (e.g., a calcium or magnesium line for water hardness). Also, observe the signal-to-noise ratio (SNR) and the relative standard deviation (RSD) of repeated measurements.
  • Focus Scan: With laser energy fixed at an intermediate value, systematically vary the distance between the focusing lens and the sample surface ("sample-to-lens distance"). Move the focal point from below the surface to above it in small increments (e.g., 1 mm).
  • Data Analysis: Plot the integrated line intensity and SNR against both laser energy and focal position. The optimal conditions are typically those that maximize SNR while maintaining acceptable signal stability (low RSD). Avoid excessive energy, which can lead to plasma shielding.

The following workflow outlines the systematic optimization process for these parameters:

G start Start Optimization prep Prepare Stable Liquid Jet System start->prep set_energy Set Initial Laser Energy prep->set_energy set_focus Set Initial Focal Position set_energy->set_focus acquire Acquire and Average LIBS Spectra set_focus->acquire analyze Calculate Signal Intensity and SNR acquire->analyze decide_focus SNR vs. Focus Peak Identified? analyze->decide_focus change_focus Change Focal Position change_focus->acquire change_energy Change Laser Energy change_energy->set_focus decide_focus->change_focus No decide_energy SNR vs. Energy Peak Identified? decide_focus->decide_energy Yes decide_energy->change_energy No end Optimal Parameters Found decide_energy->end Yes

Figure 1: Workflow for Laser Energy and Focus Optimization

Representative Data: Table 2: Effects of Laser Energy and Focus on LIBS Signal in Aqueous Samples

Parameter Typical Range Observed Effect on Signal Recommended Optimal Value
Laser Pulse Energy 20 - 140 mJ [57] Signal intensity generally increases with energy, but saturates; excess energy increases instability and background [12]. ~60 mJ (soil analysis) [57]; system-dependent balance for max SNR.
Sample-to-Lens Distance Varies by focal length Signal is maximized when laser is focused slightly below the liquid surface or jet stream [56]. 5 mm below jet outlet surface [13]; 9.0 cm for f=20 cm lens [57].
Laser Wavelength 1064 nm, 532 nm 532 nm light can produce plasma with shorter lifetime (600 ns vs 1200 ns for 1064 nm) and different ablation characteristics [12]. Chosen based on sample absorption; 1064 nm is common.
Jet Stream Diameter ~0.64 mm A stable, laminar jet flow is crucial for signal reproducibility and reducing splashing [56] [13]. 0.64 mm for stable signal acquisition [13].

Temporal Gating Parameters

The temporal evolution of the plasma emission is critical for signal quality. Initially, the plasma emits an intense, featureless continuum. As it cools, characteristic atomic and ionic lines emerge.

Experimental Protocol: Optimizing Temporal Gating

  • Setup: Configure the ICCD detector and delay generator to be synchronized with the laser Q-switch.
  • Initial Gate Delay: Set a short gate delay (e.g., 0.5 µs) and a narrow gate width (e.g., 1 µs). Acquire a spectrum.
  • Delay Series: Incrementally increase the gate delay (e.g., in 0.5 µs or 1 µs steps) while keeping the gate width constant, acquiring a spectrum at each step. Continue until the signal decays into the background noise.
  • Gate Width Adjustment: At the optimal delay (where SNR is highest), experiment with different gate widths. A wider gate collects more light but may include more background.
  • Analysis: For each delay and width setting, plot the intensity and SNR of a specific elemental line (e.g., Potassium at 766.49 nm). The optimal delay allows the continuum background to dissipate while the atomic emission is still strong.

Representative Data: Table 3: Effects of Temporal Parameters on LIBS Signal Quality

Parameter Typical Range Observed Effect on Signal Recommended Optimal Value
Gate Delay (td) 0.5 - 40 µs [56] [57] Short delay: high continuum background. Long delay: weak atomic signal. Optimal delay maximizes atomic line-to-background ratio [54]. 1 µs [56] to 3.5 µs [57]; dependent on plasma lifetime and element.
Gate Width (tw) 1 - 5 µs [56] Wider gates integrate more signal but can include more broadband noise if not delayed properly. ~5 µs for strong, long-lived lines [56]; ~1 µs for time-resolved studies.
Plasma Lifetime Up to ~50 µs [56] Long-lived resonant transitions (e.g., K I 766.49 nm) can be detected for tens of microseconds, providing a wide temporal window for detection [56]. Use to define maximum useful gate delay.

The relationship between plasma evolution and optimal detection timing is summarized below:

G start Laser Pulse Ablates Sample plasma Hot Plasma Forms (Continuum Emission) start->plasma cool Plasma Expands & Cools (Ionic Lines Appear) plasma->cool atomic Atomic Emission Dominates (Optimal Detection Window) cool->atomic decay Signal Decays to Noise atomic->decay delay_label Gate Delay (td) set here delay_label->atomic width_label Gate Width (tw) applied here width_label->atomic

Figure 2: Plasma Evolution and Temporal Gating Strategy

Integrated Workflow for Contaminated Water Screening

The following integrated protocol combines the optimization of all parameters for screening heavy metals in water.

Comprehensive Experimental Protocol

  • Sample Pre-Treatment (if required): For bulk liquid analysis, use a liquid jet. Alternatively, consider pre-concentration methods like depositing residues on filter papers or using magnetic nanoparticles to improve sensitivity for trace heavy metals [12].
  • System Setup:
    • Align the liquid jet or sample cell.
    • Purge the sample environment with argon to enhance plasma emission and prevent oxidation of reactive elements [56].
    • Set argon flow rate to a steady, laminar regime (e.g., 2-5 L/min) [56].
  • Initial Parameter Setting: Begin with conservative parameters: laser energy ~60 mJ, gate delay ~1.5 µs, gate width ~2 µs, and laser focused a few millimeters below the jet surface.
  • Sequential Optimization: Follow the protocols in Sections 3.1 and 3.2 to iteratively optimize focal position, laser energy, and finally temporal gating.
  • Data Acquisition and Validation:
    • Acquire multiple spectra (e.g., 20 shots) and average them to improve the SNR, reducing the RSD from ~16% to ~2% [13].
    • Validate the optimized method using standard reference materials or spike-recovery tests. Recovery rates for elements like Ca and Mg should ideally be between 90-109% [13].

Systematic optimization of laser energy, temporal gating, and focal geometry is fundamental to achieving high-sensitivity detection of contaminants in water using LIBS. The protocols and data provided here offer a clear roadmap for researchers to enhance their analytical figures of merit. By carefully balancing these parameters, as detailed in the accompanying tables and workflows, LIBS can be transformed into a more robust and reliable technique for rapid, on-site screening of contaminated water, contributing significantly to environmental monitoring and protection efforts.

Benchmarking LIBS: Validation Against Established Analytical Techniques

The accurate detection of heavy metals in environmental matrices like soil and water is a critical requirement for environmental safety assessment, regulatory compliance, and public health protection. [58] [12] Traditional laboratory-based techniques such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Atomic Absorption Spectrometry (AAS) have long been established as standard methods for elemental analysis. [59] [29] However, the emergence of Laser-Induced Breakdown Spectroscopy (LIBS) as a rapid, direct-analysis technique has prompted significant research interest. [28] [12] [29] This application note provides a comparative analysis of these three analytical methods, focusing on their operational principles, performance metrics, and practical applications for heavy metal detection in soil and water, framed within the context of contaminated water screening research. The data and protocols summarized herein are designed to assist researchers and scientists in selecting the appropriate methodology for their specific analytical needs.

Technology Comparison and Performance Data

The selection of an analytical technique depends on a balance between required detection limits, sample throughput, operational complexity, and cost. Below, we compare the core characteristics of LIBS, ICP-MS, and AAS.

Table 1: Comparative Analysis of LIBS, ICP-MS, and AAS for Heavy Metal Detection

Feature Laser-Induced Breakdown Spectroscopy (LIBS) Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) Atomic Absorption Spectrometry (AAS)
Fundamental Principle Atomic emission from laser-generated plasma [29] [60] Ionization in plasma and mass-to-charge separation [58] Absorption of light by ground-state atoms in a flame or furnace [59]
Typical LOD (Heavy Metals) ~0.02-0.48 µg/mL in liquid [28]; ~30-300 mg/kg in soil [29] Parts per trillion (ppt) [58] Varies; generally between ICP-MS and LIBS [59]
Key Performance Metrics RSD: 1-4%; ARE: 2-8% for liquid analysis [28] Exceptional sensitivity and wide dynamic range [58] [61] High precision with correlation R² > 0.999 for soil extracts [59]
Sample Throughput Very high; rapid, real-time analysis [29] [62] High, but requires sample introduction [58] Moderate; sequential element analysis can be time-consuming [59]
Sample Preparation Minimal to none; direct analysis possible [28] [29] Extensive; typically requires acid digestion and dilution [58] [61] Required; involves digestion and extraction [59]
Key Advantages No chemical waste, portability, stand-off analysis, light element detection [29] [62] [60] Ultra-low detection limits, isotopic analysis capability, high throughput [58] Well-established, robust, simpler operation for specific elements [59]
Main Limitations Higher LOD vs. ICP-MS, matrix effects, plasma instability [12] [62] [60] High cost, complex operation, susceptible to spectral interferences [58] [61] Limited to single-element analysis, requires different lamps per element [59]

Detailed Experimental Protocols

Protocol for Heavy Metal Detection in Water using Femtosecond LIBS

This protocol is adapted from a recent study demonstrating high-sensitivity detection of Cr, Pb, and Cu in liquid water using a flowing liquid jet to enhance signal stability. [28]

  • Sample Preparation: Prepare standard solutions of Cr, Pb, and Cu in deionized water across a concentration range relevant for calibration (e.g., sub-µg/mL to several µg/mL). Minimal pre-treatment is required. The sample is presented to the laser as a vertically flowing liquid jet with a diameter of 0.84 mm and a flow rate of 901 mm³/s.
  • Instrument Setup: Utilize a femtosecond laser system (e.g., 800 nm, 35 fs pulses, 1 kHz repetition rate). Focus the laser beam onto the liquid jet using a 100 mm focal length lens. Collect the plasma emission light using a lens system and couple it via an optical fiber to a spectrometer with an ICCD detector.
  • Data Acquisition: Set the ICCD gate delay to the optimal value for signal-to-noise ratio (SNR), found to be ~800 ns in the referenced study. [28] Accumulate spectra over a set number of laser pulses (e.g., 10, 20, or 50 accumulations) to improve the SNR.
  • Quantitative Analysis: Construct calibration curves (signal intensity vs. concentration) for each element. Calculate the Limit of Detection (LOD) using the 3σ criterion. The protocol achieved LODs of 0.045 µg/mL for Pb and 0.027 µg/mL for Cu with 20 spectral accumulations. [28]

The workflow for this protocol is summarized in the diagram below.

D Start Prepare Standard Solutions Setup Instrument Setup: Fs-laser, spectrometer, ICCD Start->Setup Flow Generate Flowing Liquid Jet Setup->Flow Align Align Laser on Jet and Collect Plasma Light Flow->Align Acquire Acquire Spectra with Optimized Gate Delay Align->Acquire Analyze Analyze Data & Build Calibration Curves Acquire->Analyze End Report LOD & Quantitative Results Analyze->End

Protocol for Heavy Metal Analysis in Soil using ICP-MS

This protocol is based on established environmental monitoring methods, such as EPA Method 200.8, and comparative studies. [58] [61]

  • Sample Collection & Preparation: Collect soil samples using an auger and transport in plastic bags. Oven-dry samples at 105°C for 12 hours. Pass the dried soil through a 2-mm sieve to ensure homogeneity. [61]
  • Microwave Acid Digestion: Accurately weigh 1 g of soil into a Teflon vessel. Add 9 mL nitric acid (HNO₃), 2 mL hydrochloric acid (HCl), and 1 mL hydrogen peroxide (H₂O₂). Digest using a microwave-assisted system following a program like heating to 180°C for 5.5 minutes and holding for 9.5 minutes. [61]
  • Sample Dilution: After cooling, transfer the digestate to a plastic container. Perform a serial dilution (e.g., 100 µL digestate to 10 mL) with deionized water to reduce total dissolved solids (TDS) to a level suitable for ICP-MS analysis (<0.2%). [58] [61]
  • ICP-MS Analysis & Calibration: Introduce the diluted sample into the ICP-MS via a nebulizer. Use a multi-element standard (e.g., 0-100 ppb) in 1% nitric acid for calibration. Employ internal standards (e.g., Bi, In, Sc) to correct for instrument drift and matrix effects. [61]

Protocol for Multi-Element Soil Analysis using High-Resolution AAS

This protocol is detailed for the simultaneous quantification of eight metals (Cd, Cr, Co, Cu, Pb, Mn, Ni, Zn) in soil extracts according to DIN ISO 11047. [59]

  • Sample Digestion: Perform an aqua regia extraction of the soil sample as per DIN ISO 11466. Weigh 0.3 g of soil and digest with a mixture of HCl and HNO₃.
  • Sample Dilution: Dilute the digested sample for flame measurement with a solution containing 21% (v/v) HCl and 7% (v/v) HNO₃. For elements like Cr and Mn measured with an air-acetylene flame, add a 10% (v/v) cesium chloride-lanthanum chloride (Cs/La) buffer solution to suppress chemical interferences.
  • Instrument Calibration: Prepare calibration standards in the same acid matrix as the samples. Use a high-resolution continuum source AAS (HR-CS AAS) spectrometer, such as a contrAA 800, which allows for fast sequential analysis of multiple elements from a single aspiration. [59]
  • Measurement: Aspirate the sample and calibration standards. Use an air-acetylene or nitrous oxide-acetylene flame depending on the element. The HR-CS AAS instrument records the spectrum around the analytical line, allowing for the recognition and correction of spectral interferences. [59]

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 2: Key Reagents and Materials for Heavy Metal Analysis

Reagent/Material Function/Application Example Use Case
Aqua Regia (HCl:HNO₃) Digestant for dissolving heavy metals from solid matrices like soil and sediments. [59] [61] Extraction of Cd, Cr, Pb, etc., from soil samples prior to ICP-MS or AAS analysis. [59]
Certified Single-Element Standards Primary standards for creating calibration curves in all quantitative techniques. [59] Preparation of multi-point calibration standards for ICP-MS and AAS.
Internal Standard Solution (e.g., Bi, In, Sc, Y) Added to samples and standards to correct for instrument drift and matrix effects. [61] Mandatory in ICP-MS analysis to improve quantitative accuracy. [61]
Cesium Chloride-Lanthanum Chloride (Cs/La) Buffer Releasing agent to prevent chemical interferences in flame AAS. [59] Added to samples for AAS analysis of Cr and Mn in an air-acetylene flame. [59]
High-Purity Acids (HNO₃, HCl) Digestion medium and diluent for sample preparation; essential to avoid contamination. [59] [61] Used in microwave digestion of soil samples and for diluting digestates.

The comparative analysis presented herein demonstrates that LIBS, ICP-MS, and AAS each occupy a distinct niche in the heavy metal analysis of water and soil. ICP-MS remains the undisputed reference method for applications demanding the utmost sensitivity and low detection limits. AAS provides a robust and well-established alternative, particularly for laboratories with less demanding detection limit requirements. LIBS has emerged as a powerful tool for rapid screening and analysis, offering unparalleled speed and minimal sample preparation, with ongoing research continuously improving its sensitivity and reliability. [28] [12] [62] The choice of technique ultimately depends on the specific analytical requirements, including required detection limits, sample throughput, available budget, and the need for portability or in-situ analysis.

Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as a powerful analytical technique for the rapid, multi-elemental analysis of contaminated water, offering significant advantages for environmental screening applications. This technique employs a high-energy laser pulse to generate a plasma from a sample, and the emitted characteristic atomic emission spectra are used for qualitative and quantitative elemental analysis [28] [12]. Unlike conventional methods such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS) or Atomic Absorption Spectroscopy (AAS), LIBS requires minimal sample preparation, enables real-time, on-site analysis, and can be adapted for remote sensing [63] [39]. However, the analytical performance of LIBS, particularly its Limits of Detection (LOD) and quantitative accuracy, is highly dependent on the experimental methodology employed. This document evaluates key performance metrics and details standardized protocols to optimize LIBS for screening heavy metals in water, providing a framework for reliable environmental monitoring.

Performance Metrics for LIBS in Water Analysis

The efficacy of LIBS for contaminated water screening is primarily gauged through two fundamental metrics: the Limit of Detection (LOD) and quantitative accuracy. The LOD defines the lowest concentration of an analyte that can be reliably detected by the system, while quantitative accuracy refers to the closeness of agreement between the measured value and a known reference value, often expressed as Relative Standard Deviation (RSD) or Average Relative Error (ARE) [28] [63].

Direct analysis of aqueous samples presents challenges, including plasma quenching, signal instability from splashing and surface ripples, and reduced sensitivity [28] [64]. Consequently, various sample introduction and pre-treatment strategies have been developed to overcome these limitations and improve performance, as summarized in the table below.

Table 1: Limits of Detection (LOD) Achieved for Heavy Metals in Water Using Different LIBS Methodologies

Element Sample Introduction / Pre-treatment Method Reported LOD Key Experimental Parameters Citation
Lead (Pb) Chelation + Centrifugation (Liquid-to-Solid) 2.82 ng/mL (Tap Water)3.64 ng/mL (River Water) Chelating agent: Sodium DDTC; SSTF-LIBS [63]
Lead (Pb) Liquid Jet 0.045 µg/mL (45 ng/mL) Femtosecond laser; Flowing liquid; NSA=20 [28]
Chromium (Cr) Liquid Jet 0.061 µg/mL (61 ng/mL) Femtosecond laser; Flowing liquid; NSA=10 [28]
Copper (Cu) Liquid Jet 0.019 µg/mL (19 ng/mL) Femtosecond laser; Flowing liquid; NSA=50 [28]
Calcium (Ca) Portable Liquid Jet 11.58 mg/L On-site system; Jet diameter: 0.64 mm [13]
Magnesium (Mg) Portable Liquid Jet 2.57 mg/L On-site system; Jet diameter: 0.64 mm [13]

The quantitative accuracy of LIBS is influenced by matrix effects, plasma stability, and the calibration model used. For instance, the Relative Standard Deviation (RSD) for heavy metal analysis in flowing liquids using femtosecond LIBS has been reported in the range of 1–4%, with Average Relative Errors (ARE) between 2–8% [28]. Advanced calibration models, including one-point calibration and machine learning algorithms, are being developed to further enhance accuracy and reduce reliance on extensive calibration sets [24] [65].

Detailed Experimental Protocols

This section provides detailed, step-by-step protocols for two prominent LIBS methodologies used in water analysis: liquid jet analysis and chelation-assisted liquid-to-solid conversion.

Protocol 1: Liquid Jet Analysis for Direct Aqueous Screening

This protocol is designed for the direct analysis of flowing liquid samples, minimizing splashing and improving signal stability [28] [13].

  • Objective: To directly detect and quantify heavy metal elements (e.g., Cr, Pb, Cu, Ca, Mg) in aqueous solutions with high sensitivity and rapid analysis time.
  • Principle: A liquid jet is formed, and a focused laser pulse is directed onto the jet stream. This configuration reduces surface fluctuations and the cooling effect of the liquid matrix, leading to more stable and intense plasma emission [28].

  • Materials and Reagents:

    • Standard solutions of target heavy metals (e.g., Cr, Pb, Cu, Ca, Mg).
    • High-purity deionized water.
    • Peristaltic pump and chemically inert tubing.
    • Jet nozzle (e.g., diameter of 0.64 mm - 0.84 mm).

  • Procedure:

    • System Setup: Configure the LIBS system as shown in Figure 1. Connect the peristaltic pump to circulate the liquid sample through the tubing and jet nozzle, creating a stable, continuous vertical flow.
    • Laser Alignment: Focus the laser pulse (e.g., femtosecond laser at 1 kHz, 800 nm) onto the surface of the liquid jet. The optimal ablation point is typically 5 mm from the jet outlet [13].
    • Parameter Optimization:
      • Gate Delay: Optimize the ICCD gate delay to maximize the signal-to-noise ratio (SNR). A typical optimal delay is around 600 ns [28].
      • Laser Energy: Adjust to a level sufficient for robust plasma generation without excessive splashing (e.g., 5 mJ) [28].
      • Spectral Accumulation: Set the number of spectral accumulations (NSA) to 50-100 to improve the SNR and lower the LOD [28].
    • Calibration: Analyze a series of standard solutions with known concentrations of the target elements to construct a calibration curve of spectral line intensity versus concentration.
    • Sample Analysis: Introduce unknown water samples into the system and acquire LIBS spectra under identical optimized conditions.
    • Quantification: Determine the concentration of the target elements in the unknown samples using the prepared calibration curve.

The following diagram illustrates the workflow for this protocol:

G Start Start Protocol 1 Setup Set up LIBS system and liquid jet Start->Setup Align Align laser on liquid jet stream Setup->Align Optimize Optimize parameters: Gate Delay, Laser Energy, NSA Align->Optimize Calibrate Run standard solutions to build calibration curve Optimize->Calibrate Analyze Analyze unknown water samples Calibrate->Analyze Quantify Quantify elements using calibration Analyze->Quantify End Analysis Complete Quantify->End

Liquid Jet Analysis Workflow

Protocol 2: Chelation-Assisted Liquid-to-Solid Conversion for Ultra-Trace Detection

This protocol uses a chelating agent to pre-concentrate target metals from a liquid sample into a solid pellet, significantly improving the LOD for ultra-trace analysis, such as meeting the WHO drinking water standard for Pb (10 ng/mL) [63].

  • Objective: To detect heavy metal ions (e.g., Pb²⁺) at ultra-trace levels (ng/mL) in water samples via pre-concentration and liquid-to-solid conversion.
  • Principle: A chelating agent (Sodium Diethyldithiocarbamate, DDTC) is added to the water sample to form stable, insoluble complexes with target metal ions. These complexes are concentrated via centrifugation and dried on a substrate, converting the analyte into a solid form ideal for LIBS analysis [63].

  • Materials and Reagents:

    • Sodium Diethyldithiocarbamate (DDTC).
    • High-purity concentrated HNO₃ (for sample acidification).
    • Graphite substrates or filter papers.
    • Centrifuge and centrifuge tubes.
    • Oven or desiccator for drying.

  • Procedure:

    • Sample Preparation: Acidify the water sample (e.g., 40 mL) with HNO₃ to a pH of approximately 4.
    • Chelation Reaction: Add a 20 mM solution of sodium DDTC (e.g., 1 mL) to the sample. Stir vigorously for 10 minutes to ensure complete chelation and precipitate formation.
    • Centrifugation: Transfer the solution to centrifuge tubes and centrifuge at 14,000 rpm for 15 minutes to pellet the metal complexes.
    • Supernatant Removal: Carefully discard the supernatant.
    • Solid Pellet Formation: Re-suspend the precipitate in a minimal volume of deionized water. Deposit this suspension onto a graphite substrate and allow it to dry completely in an oven or desiccator.
    • LIBS Analysis: Place the solid substrate with the concentrated sample into the LIBS chamber. Proceed with standard LIBS analysis using a Simultaneous Spatio-Temporal Focusing (SSTF) LIBS system to further enhance sensitivity [63].

The workflow for this pre-concentration method is outlined below:

G Start Start Protocol 2 Prep Prepare and acidify water sample Start->Prep Chelate Add chelating agent (Sodium DDTC) and stir Prep->Chelate Centrifuge Centrifuge to pellet metal complexes Chelate->Centrifuge Supernatant Discard supernatant Centrifuge->Supernatant Solidify Re-suspend and dry pellet on substrate Supernatant->Solidify LIBS Analyze solid substrate via LIBS Solidify->LIBS End Ultra-Trace Analysis Complete LIBS->End

Liquid-to-Solid Conversion Workflow

The Scientist's Toolkit: Key Research Reagents and Materials

Successful implementation of LIBS protocols for water screening relies on specific reagents and materials. The following table details essential items and their functions.

Table 2: Essential Research Reagents and Materials for LIBS-based Water Screening

Item Name Function/Application in LIBS Protocols Technical Notes
Sodium Diethyldithiocarbamate (DDTC) Chelating agent for pre-concentrating heavy metal ions (e.g., Pb²⁺, Cu²⁺) from water via formation of insoluble complexes [63]. Enables detection at ng/mL levels; optimal concentration is ~20 mM.
Cation Exchange Membrane (CMI-7000 S) Solid-phase substrate for extracting and concentrating cation ions (Ca²⁺, Mg²⁺) from water, facilitating liquid-to-solid conversion [39]. Requires pre-conditioning (e.g., in 1M HCl); provides a uniform matrix for analysis.
Graphite Substrates Inert, low-background substrate for depositing and analyzing pre-concentrated samples from liquid-to-solid conversion [63]. Provides a clean spectral background, reducing interference during LIBS measurement.
Collison Nebulizer Device for generating fine aerosols from liquid samples, used in aerosolization-LIBS techniques for improved plasma stability [24]. Aerosolization allows for more complete ionization of the sample compared to bulk liquid.
Standard Reference Materials Certified standard solutions for preparation of calibration curves and validation of analytical method accuracy [28] [24]. Critical for quantitative analysis; should be matrix-matched to samples when possible.

This document has outlined critical performance metrics and detailed experimental protocols for applying Laser-Induced Breakdown Spectroscopy to the screening of heavy metals in contaminated water. The choice of methodology—ranging from direct liquid jet analysis to sophisticated pre-concentration techniques—directly determines the achievable Limits of Detection and quantitative accuracy. The presented protocols for liquid jet analysis and chelation-assisted solid conversion provide researchers with clear, actionable procedures for implementing these techniques. Furthermore, the identification of key research reagents equips scientists with the necessary knowledge to establish robust LIBS capabilities in their laboratories. As LIBS technology continues to evolve, coupling these methods with advanced data processing algorithms, such as machine learning, promises to further enhance analytical performance, solidifying LIBS's role as a powerful tool for rapid and reliable environmental water screening.

Assessing Recovery Rates and Precision in Complex Environmental Matrices

Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as a powerful analytical technique for the rapid, multi-elemental analysis of contaminated water, aligning with the broader thesis research on advanced screening methods. A significant challenge in this field involves achieving high analytical recovery and precision when dealing with complex environmental matrices such as surface waters, which can contain various interfering substances. This application note details standardized protocols and performance data for assessing recovery rates and precision in the quantitative analysis of calcium (Ca) and magnesium (Mg) as key indicators of water hardness and overall quality. We present a comparative analysis of three distinct LIBS-based methodologies—liquid jet, cation exchange membrane, and aerosolization—evaluating their efficacy in complex matrices.

The following tables summarize the quantitative performance of different LIBS approaches for detecting Ca and Mg in aqueous solutions, providing a basis for comparing recovery rates, precision, and detection limits.

Table 1: Analytical Figures of Merit for LIBS-based Detection of Water Hardness Ions

Methodology Target Analytic Reported Recovery Rate (%) Detection Limit (mg/L) Key Matrix Source Reference
Portable LIBS (Liquid Jet) Calcium (Ca) 90.83 - 101.74 11.58 Surface Water [13]
Portable LIBS (Liquid Jet) Magnesium (Mg) 93.43 - 108.74 2.57 Surface Water [13]
Cation Exchange Membrane Calcium (Ca) Not Explicitly Reported System Demonstrated Aquaculture/Solution [39]
Cation Exchange Membrane Magnesium (Mg) Not Explicitly Reported System Demonstrated Aquaculture/Solution [39]
Aerosolization (OPC-LIBS) Calcium (Ca) High Accuracy vs. ICP-OES Method Focus River, Reservoir, Groundwater [24]
Aerosolization (OPC-LIBS) Magnesium (Mg) High Accuracy vs. ICP-OES Method Focus River, Reservoir, Groundwater [24]

Table 2: Precision and Operational Characteristics of LIBS Methodologies

Methodology Precision (RSD) Sample Throughput Sample Pre-treatment Complexity Key Advantage
Portable LIBS (Liquid Jet) ~2% (after optimization) Rapid / On-site Low Direct, in-situ analysis [13]
Cation Exchange Membrane Not Explicitly Reported ~5 minutes/sample Medium Pre-concentration; minimizes moisture [39]
Aerosolization (OPC-LIBS) Not Explicitly Reported Rapid Low High analytical accuracy vs. ICP-OES [24]

Experimental Protocols

Protocol 1: Portable LIBS with Liquid Jet Configuration

This protocol is designed for direct, on-site analysis of surface water samples, such as from rivers and ponds [13].

  • Step 1: Sample Introduction. Utilize a liquid jet system with a calibrated diameter of 0.64 mm. Ensure the water sample is homogenized prior to introduction to the jet system.
  • Step 2: Instrumental Setup. Employ a portable LIBS system with a miniaturized spectrometer. Position the laser ablation point at a distance of 5 mm from the jet outlet to ensure plasma stability.
  • Step 3: Data Acquisition. Acquire LIBS spectra from the plasma generated on the liquid jet. To enhance signal stability, acquire and average a minimum of 20 individual spectra per sample analysis.
  • Step 4: Data Analysis. Identify characteristic emission lines for Ca and Mg (e.g., Ca II at 393.3 nm and Mg II at 279.5 nm). Quantify concentrations using pre-established calibration curves.
Protocol 2: Cation Exchange Membrane (CEM) with Automated System

This protocol uses a CEM to convert target ions from liquid to solid state, improving detection stability and sensitivity [39].

  • Step 1: Membrane Preparation. Saturate a CEM (e.g., CMI-7000 S) in 1 mol/L HCl for 24 hours. Rinse thoroughly with deionized water until the effluent reaches neutral pH (pH = 7).
  • Step 2: Automated Sampling and Reaction. Install the prepared CEM into the automated fixture. The system injects the water sample into a container. Use magnetic stirring to ensure uniform and full exchange of Ca²⁺ and Mg²⁺ onto the CEM surface for a programmed duration.
  • Step 3: Cleaning and Drying. After the reaction, the system automatically moves the CEM sequentially through cleaning (with deionized water) and drying stations. This step removes residual salts and minimizes moisture interference on the membrane surface.
  • Step 4: LIBS Measurement. The automated system positions the dried CEM at the laser focal plane. Acquire LIBS spectra by averaging measurements from at least five different positions on the CEM surface to account for potential inhomogeneity.
Protocol 3: Aerosolization with One-Point Calibration (OPC-LIBS)

This protocol combines aerosolization of the liquid sample with an advanced calibration model for high quantitative accuracy [24].

  • Step 1: Aerosol Generation. Direct the water sample into a Collison nebulizer to generate an aerosol. Maintain a constant flow rate at the outlet (e.g., 1 liter per minute). Pass the generated aerosol through a drying tube to reduce humidity-induced signal fluctuation.
  • Step 2: LIBS Plasma Generation. Focus the laser beam orthogonally to the center of the aerosol jet to achieve stable plasma excitation. Collect the plasma emission light using an appropriate optical system coupled to a spectrometer.
  • Step 3: Calibration with Modified OPC-LIBS. Implement a modified One-Point Calibration model. Use a single matrix-matched standard sample (containing Ca, Mg, and an internal standard such as Sr) to correct the Boltzmann plot of the unknown sample. Calculate the plasma temperature using the Multi-Element Saha–Boltzmann (ME–SB) plot for improved accuracy [24].
  • Step 4: Quantification. Determine the elemental contents in the unknown samples based on the calibrated model and compare the results against reference methods like ICP-OES for validation.

Workflow Visualization

The following diagram illustrates the logical workflow for selecting the appropriate LIBS methodology based on research objectives and sample matrix.

G Start Start: Analysis Required Q1 Primary Need for On-site/ Field Analysis? Start->Q1 Q2 Requirement for Highest Quantitative Accuracy? Q1->Q2 No M1 Method 1: Portable LIBS with Liquid Jet Q1->M1 Yes Q3 Sample has Complex Matrix or Low Analyte Conc.? Q2->Q3 No M3 Method 3: Aerosolization with OPC-LIBS Q2->M3 Yes Q3->M1 No M2 Method 2: Cation Exchange Membrane (CEM) Q3->M2 Yes

LIBS Method Selection Workflow

The detailed experimental workflow for the Cation Exchange Membrane (CEM) method, which integrates sampling, reaction, and detection, is shown below.

G cluster_0 Automated Control Cycle A CEM Preparation: Acid Soak & Rinse B Automated Sampling & Ion Exchange A->B CEM Loaded C Membrane Cleaning & Drying B->C Target Ions Adsorbed B->C D LIBS Spectral Measurement C->D Dry CEM Positioned C->D E Data Analysis & Quantification D->E Averaged Spectra

CEM-LIBS Automated Workflow

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Reagents and Materials for LIBS Analysis of Aqueous Matrices

Item Name Specification / Example Primary Function in Protocol
Cation Exchange Membrane (CEM) CMI-7000 S (SO₃⁻Na⁺ group) Adsorbs and pre-concentrates Ca²⁺ and Mg²⁺ ions from liquid sample, converting them to solid phase for stable LIBS measurement [39].
Calibration Standard Solutions CaCl₂ and MgCl₂ solutions of known concentration Used for constructing calibration curves and for the one-point calibration (OPC) method to quantify unknown samples [39] [24].
Collison Nebulizer Huironghe Technology, flow rate 1 L/min Converts liquid water samples into a fine aerosol jet, improving plasma stability and ionization efficiency for analysis [24].
Laser Source Diode-pumped solid-state (e.g., Nd:YAG, 1064 nm) Generates high-energy pulses to ablate material and create a microplasma for elemental excitation and emission [39] [60].
Spectrometer Fiber-optic spectrometer (e.g., Avantes, Ocean Optics) Detects and disperses the light emitted from the plasma, allowing identification of elemental composition based on characteristic emission lines [13] [39].
Acid for Pre-treatment Hydrochloric Acid (HCl), 1 mol/L Used to pre-saturate and activate the Cation Exchange Membrane before use, ensuring its ion exchange capacity is available [39].

The Economic and Operational Case for Portable LIBS in Field Deployment

Application Notes: Portable LIBS for Contaminated Water Screening

Core Analytical Capabilities

Portable Laser-Induced Breakdown Spectroscopy (LIBS) offers a compelling solution for rapid, on-site screening of metallic contaminants in water samples, filling a critical gap between laboratory analysis and traditional field test kits. The technology operates on the principle of using a high-energy laser pulse to instantaneously vaporize a microscopic portion of the sample and create a high-temperature plasma (exceeding 15,000 K). As this plasma cools, excited atoms and ions emit characteristic wavelengths of light, which are collected and analyzed by a spectrometer to identify and quantify elemental composition [33].

For water analysis, this typically involves depositing a water sample onto a solid substrate or filtering a known volume to create a solid residue for analysis, allowing the LIBS laser to interact with a solid surface. This method provides distinct advantages for screening critical contaminants, as outlined in Table 1 [33].

Table 1: LIBS Detection Performance for Select Water Contaminants

Element Category Specific Contaminants Typical Detection Limit Primary Relevance to Water Screening
Toxic Heavy Metals Lead (Pb), Cadmium (Cd), Mercury (Hg) 50-500 ppm Human toxicity, environmental persistence
Light Metals Lithium (Li), Beryllium (Be), Sodium (Na) 0.01-0.1% (Li) Industrial discharge, mining runoff
Base Metals Copper (Cu), Zinc (Zn), Aluminium (Al) 100-500 ppm Corrosion of pipes, industrial waste
Other Elements Calcium (Ca), Magnesium (Mg), Iron (Fe) 0.1-1% Water hardness, general geochemistry
Economic and Operational Advantages

The economic case for deploying portable LIBS in field-based water screening is robust, primarily driven by significant reductions in time and cost per analysis compared to traditional methods.

Table 2: Economic Comparison: Portable LIBS vs. Traditional Laboratory Analysis

Operational Aspect Traditional Laboratory (ICP-OES/MS) Portable LIBS Operational Improvement
Result Turnaround 2-7 days (with transport) Immediate (minutes) 100-300x acceleration [33]
Cost per Sample $50 - $200 $5 - $15 (equipment amortization) 3-10x cost reduction [33]
Sample Preparation Extensive (acid digestion, filtration) Minimal to none (filtration may be needed) Near elimination [33]
Infrastructure Need Centralized laboratory Self-contained, battery-operated Enables truly remote operation

Portable LIBS systems, typically weighing 10-30 kilograms and capable of operating on battery power for 8-12 hours, democratize access to elemental analysis at remote locations [33]. This allows for high-density spatial sampling and immediate decision-making regarding further sampling strategies or remediation actions, which is invaluable for mapping contaminant plumes in water bodies.

Experimental Protocols

Protocol 1: Direct Analysis of Filtered Water Residues

This protocol is designed for the sensitive detection of trace metals in water by pre-concentrating them on a filter membrane.

2.1.1 Workflow

The following diagram illustrates the multi-step workflow for this protocol:

G Start Start: Collect Water Sample Filtration Filter Known Volume (0.1 - 1 L) Start->Filtration Drying Dry Filter Membrane (60°C, 15 min) Filtration->Drying Mounting Mount Filter in LIBS Holder Drying->Mounting Analysis LIBS Spectral Acquisition (3-5 spots on filter) Mounting->Analysis DataProcessing Spectral Data Processing and Quantification Analysis->DataProcessing Result Result: Contaminant ID and Concentration DataProcessing->Result

2.1.2 Materials and Reagents

  • Portable LIBS Spectrometer: Ensure the device is calibrated according to manufacturer specifications. Key specifications include a laser with energy density of 10⁸ - 10¹¹ W/cm² and a spectrometer covering 200-800 nm wavelengths [33].
  • Filter Membranes: Use cellulose nitrate or mixed cellulose ester membranes (0.45 µm pore size). These provide a consistent and low-background substrate for LIBS analysis [66].
  • Syringe Filtration Apparatus: A 50 mL disposable or autoclavable glass filtration unit.
  • Certified Reference Materials (CRMs): Aqueous standard solutions for calibration, traceable to National Institute of Standards and Technology (NIST).
  • Deionized Water: For rinsing equipment and preparing blanks.

2.1.3 Procedure

  • Sample Preparation: Homogenize the water sample by shaking. Using a graduated cylinder, measure a known volume (e.g., 500 mL). Pass the measured volume through the filter membrane using the filtration apparatus under gentle vacuum. The required volume depends on the expected contaminant concentration and the desired limit of detection.
  • Sample Drying: Carefully transfer the filter membrane to a clean petri dish and dry it in an oven at 60°C for 15 minutes to remove residual moisture. Moisture can suppress plasma formation and reduce analytical signal.
  • Instrument Setup: Power on the portable LIBS instrument and allow it to initialize. Select or create a method that includes the emission lines of the target contaminants (e.g., Pb 405.8 nm, Cd 508.6 nm, Cu 324.7 nm).
  • Analysis: Place the dried filter membrane securely in the instrument's sample holder. Aim the laser probe at a representative area of the filter residue. Acquire spectra from 3-5 different spots on the filter to account for potential inhomogeneity. For each spot, the system will automatically fire a series of laser pulses (e.g., 10 pulses per spot, discarding the first 2-3 to clean the surface) and accumulate the spectral data.
  • Data Analysis: The instrument software will process the accumulated spectra, typically by plotting intensity (counts) against wavelength (nm). Use the calibrated method to identify elements based on their characteristic emission peaks. Quantification is achieved by comparing the peak intensity or area of the analyte to the calibration curve established using the CRMs.
Protocol 2: In-Situ Screening of Water Surface & Sediments

This protocol provides rapid, semi-quantitative screening for immediate site assessment, ideal for identifying pollution hotspots.

2.2.1 Workflow

The following diagram illustrates the parallel workflows for sediment and surface water screening:

G Start Start: On-Site at Water Body SedimentPath Sediment Screening Start->SedimentPath WaterPath Surface Water Screening Start->WaterPath CollectSed Collect Sediment Sample (near bank or shallow) SedimentPath->CollectSed CollectWater Collect Surface Water WaterPath->CollectWater PrepSed Air-Dry on Aluminium Foil (15-30 min) CollectSed->PrepSed Analysis Direct LIBS Analysis (Multiple laser shots) PrepSed->Analysis EvapWater Evaporate 10-50 mL drop-cast on Si wafer CollectWater->EvapWater EvapWater->Analysis DataProcessing Semi-Quantitative Analysis (Spectral Fingerprinting) Analysis->DataProcessing Result Result: Hotspot Identification DataProcessing->Result

2.2.1 Materials and Reagents

  • Portable LIBS Spectrometer
  • Disposable Sampling Tools: Plastic spoons or spatulas for sediment.
  • Sample Containers: Whirl-Pak bags or sterile plastic tubes.
  • Aluminium Foil: Used as a clean, low-background substrate for drying sediments.
  • Silicon Wafers or Graphite Plates: An inert substrate for drop-casting and evaporating water samples.

2.2.2 Procedure

  • Sediment Screening: Collect a small amount ( ~1 g) of surface sediment from the bank or shallow bed of the water body. Spread the sediment thinly on a piece of aluminium foil to air-dry for 15-30 minutes. Place the foil with the dried sediment in the LIBS sample chamber or bring the handheld probe directly to the sample. Acquire spectra from multiple spots.
  • Surface Water Screening: Collect a surface water sample in a clean container. Using a pipette, deposit a small volume (e.g., 0.1 mL) onto a silicon wafer and allow it to evaporate completely, leaving a residue for analysis. Alternatively, some advanced portable LIBS systems may allow for direct analysis of liquid surfaces, though this is less common and requires specific instrument capabilities. Analyze the dried residue on the wafer.
  • Data Interpretation: This method is best for rapid comparison. Compare the spectra from different sampling points to identify "hotspots" with elevated signals for specific contaminants. Use the software's semi-quantitative mode, which often provides relative concentrations based on pre-loaded calibration libraries.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials and Reagents for Field-Based LIBS Water Screening

Item Function & Rationale Technical Specifications
Portable LIBS Analyzer Core analytical instrument for generating plasma and collecting elemental emission spectra. Handheld or briefcase-sized; laser energy > 10 mJ/pulse; spectral range 200-800 nm; CCD or ICCD detector [67] [33].
Certified Reference Materials (CRMs) Critical for calibrating the instrument and validating analytical methods to ensure data accuracy. Aqueous multi-element standards (e.g., for Pb, Cd, Hg, As) traceable to NIST or equivalent standards.
Filter Membranes Pre-concentrate trace elements from large water volumes onto a solid matrix, improving detection limits. Cellulose nitrate or mixed cellulose ester; 0.45 µm pore size; 47 mm diameter [66].
Syringe Filter Unit Hold filter membrane and facilitate vacuum-assisted filtration of water samples in the field. Disposable plastic (PP) or reusable glass; compatible with 47 mm membranes.
Inert Substrates Provide a consistent, low-spectral-background surface for analyzing liquid residues. High-purity silicon wafers, graphite plates, or aluminium foil.
Portable Filtration System Enables processing of large water volumes for trace analysis without laboratory infrastructure. Manual vacuum pump or peristaltic pump; compact and lightweight.

Conclusion

Laser-Induced Breakdown Spectroscopy has firmly established itself as a powerful and competitive analytical technique for contaminated water screening. Its core strengths—minimal sample preparation, rapid multi-element analysis, and portability—address critical gaps in traditional laboratory-based methods. As advancements in portable hardware, sophisticated signal processing, and machine learning algorithms continue to mature, LIBS is poised to become an indispensable tool for real-time, on-site environmental monitoring. Future research should focus on further improving detection limits for trace-level contaminants, standardizing protocols for complex water matrices, and integrating LIBS into automated, continuous monitoring networks. For researchers and environmental professionals, mastering LIBS technology offers a direct pathway to more responsive, cost-effective, and comprehensive water quality management, ultimately contributing to more robust public health and environmental protection frameworks.

References