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Application of Laser-Induced Breakdown Spectroscopy (LIBS) in Identifying and Quantifying Heavy Metals in Industrial Plastic-Waste Streams in Asia
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This review paper looks at a laser-based scanning technology (LIBS) that could quickly detect toxic heavy metals like lead, cadmium, and arsenic hiding in recycled plastics—much faster and cheaper than current lab testing methods. This matters because these contaminants can end up in everyday plastic products when recycling isn't properly screened, so faster detection could help keep hazardous materials out of items you use daily and reduce heavy-metal exposure risks.
The growing volume of plastic waste produced has made the need for efficient identification and quantification strategies to detect and identify harmful contaminants, particularly heavy-metals, in industrial plastic-waste streams more urgent than ever. Traditional chemical digestion methods (acid digestion, ICP-MS, or AAS) provide accurate quantification and are time-consuming, costly, and not high-throughput-sorting. Laser-induced breakdown spectroscopy (LIBS) offers several advantages, including rapid real-time analysis, minimal sample preparation, multi-element detection, and efficient elemental characterization. The current review aims to investigate the possibility of using the LIBS technique to detect and quantify some heavy-metals like lead (Pb), cadmium (Cd), chromium (Cr), and arsenic (As) in heterogeneous industrial plastic wastes. The calibration strategies (reference standards) as well as optimization of the signal response and algorithms for data processing, including chemometric models, are evaluated to reduce the limit of detection and enhance analytical accuracy. The results indicate that LIBS can detect heavy-metal contamination at concentrations relevant to regulatory limits and can be scaled for online quality-control monitoring in plastic recycling facilities. In this review, the results have been summarized to support the use of LIBS for waste management towards environmental compliance, reduced use of hazardous materials in recycling products, and reduced health problems due to heavy-metal pollution.
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Researchers used Laser-Induced Breakdown Spectroscopy (LIBS) to characterize and identify the sources of heavy metals contaminating microplastics collected from a remote coral island. LIBS proved an effective rapid analytical technique for trace metal determination in microplastics, avoiding the complex sample pretreatments required by conventional methods.
Laser ablation-based techniques for microplastic analysis: recent advances and applications
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This review evaluates two laser-based analytical techniques for detecting and characterizing microplastics: laser-induced breakdown spectroscopy (LIBS) and laser ablation mass spectrometry (LA-ICP-MS). Researchers found that both methods offer spatially resolved elemental analysis that can complement traditional polymer identification approaches. The study advocates combining these techniques for a more complete picture of microplastic composition and the toxic contaminants they carry.
Identifying microplastic litter with Laser Induced Breakdown Spectroscopy: A first approach
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Researchers demonstrated that Laser Induced Breakdown Spectroscopy (LIBS) can identify microplastic particles by their spectral fingerprints, offering a first approach to a rapid analytical technique for distinguishing plastic litter types.
Rapid characterization of heavy metals in single microplastics by laser induced breakdown spectroscopy
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This study used laser-induced breakdown spectroscopy (LIBS) to rapidly characterize heavy metal content in individual microplastic particles, providing single-particle metal analysis without complex extraction steps. Single-particle heavy metal quantification methods are important for understanding the per-particle metal burden that organisms ingest when they consume microplastics.
A Novel LIBS-Machine Learning Strategy for Multimetal Detection in Microsized PMMA Particles: Efficient Quantification for Composite Pollution
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Researchers combined laser-induced breakdown spectroscopy with machine learning to simultaneously quantify three heavy metals (Cr, Pb, Cu) adsorbed to 2 µm PMMA microplastic particles, demonstrating that plastic-metal composite pollution can be characterized by optimized PLS calibration models without chemical separation.
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