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61,005 resultsShowing papers similar to Detection of microplastics in water using electrical impedance spectroscopy and support vector machines
ClearApproaches to Detect Microplastics in Water Using Electrical Impedance Measurements and Support Vector Machines
Researchers developed an electrical impedance spectroscopy method enhanced with machine learning to detect microplastics in water, achieving over 98% classification accuracy for stationary samples and over 85% for dynamic flow measurements across different plastic materials and particle sizes.
Electrical impedance spectroscopy based strategy for detecting and differentiating microplastics in water
Researchers developed a submersible electrical impedance spectroscopy approach capable of detecting and differentiating microplastics directly in biologically active aquatic environments, overcoming the labor-intensive preprocessing requirements of conventional FTIR and Raman methods.
Microplastic Identification Using Impedance Spectroscopy and Machine Learning Algorithms
Scientists developed a new method to detect and classify microplastics in water using electrical measurements and machine learning. The system can identify different sizes of PET microplastic particles with high accuracy, offering a potential tool for real-time water quality monitoring. Better detection methods like this are important for understanding how much microplastic contamination exists in drinking water and other water sources.
Measuring Microplastic Concentrations in Water by Electrical Impedance Spectroscopy
Researchers developed a method using electrical impedance spectroscopy to measure microplastic concentrations in water samples without requiring complex laboratory equipment. The technique can distinguish between different concentrations and types of plastic particles based on their electrical properties. The study offers a potentially faster and more accessible approach for routine microplastic monitoring in water treatment and environmental settings.
Protocol for low-cost quantification of microplastics through electrochemical impedance spectroscopy from aqueous matrices
Most methods for detecting microplastics in water require expensive equipment or time-consuming laboratory steps. This study presents a simple protocol using electrochemical impedance spectroscopy (EIS) — measuring how microplastics change the electrical resistance of a solution — to rapidly and cheaply quantify plastic particles in water samples. Validated against conventional optical methods, the approach could make routine microplastic monitoring more affordable and accessible, particularly for lower-resource settings or high-throughput screening applications.
Microplastic Detection in Water Using a Sensor Network, An Electronic Tongue and Spectroscopy Impedance
Researchers developed an electronic sensor system using impedance spectroscopy to detect microplastics in drinking water without needing expensive laboratory equipment. By running 160 experiments with different water contaminant combinations, they showed that the technique can distinguish microplastic contamination using electrochemical signals and statistical analysis. Affordable, portable detection systems like this are important for monitoring water supplies in regions where lab infrastructure is limited.
A microfluidic approach for label-free identification of small-sized microplastics in seawater
Researchers developed a microfluidic approach for label-free identification of small microplastics in seawater, using impedance-based detection to distinguish different polymer types without chemical labeling, enabling faster and more practical environmental monitoring.
Development of microfluidic device to monitor the contamination in drinking water using impedance spectroscopy
Researchers developed a microfluidic device using electrical impedance spectroscopy to detect and monitor microplastic particles in drinking water. The device aimed to provide a real-time, sensitive method for MP contamination monitoring at the point of use.
Investigating microplastics through electrochemical impedance spectroscopy: an analytical method for their label-free analysis
Researchers demonstrated that electrochemical impedance spectroscopy (EIS) — a technique that measures how materials resist electrical current — can quickly detect and quantify microplastics in water without chemical labels, and can even distinguish between clean plastic particles and those contaminated with lead ions. This label-free method offers a faster, simpler alternative to conventional lab techniques for monitoring microplastic pollution and the toxic metals they carry.
A Microwave-Based Sensing Platform for Microplastic Detection and Quantification: A Machine Learning-Assisted Approach
Researchers developed a low-cost microwave sensor combined with machine learning to detect and quantify microplastics in water and identify polymer types in unknown samples. The platform achieved the highest sensitivity reported among microwave-based approaches for microplastic detection, offering a promising low-cost alternative to spectroscopy-based methods.
Training and evaluating machine learning algorithms for ocean microplastics classification through vibrational spectroscopy
Researchers evaluated multiple machine learning algorithms for automatically classifying ocean microplastics using infrared spectroscopy data across 13 polymer types. The study found that Support Vector Machine classifiers provided the best balance of simplicity and accuracy, offering a practical tool for faster and more reliable identification of microplastic contaminants.
Detection and Classification of Microplastics in Water Source Using Svm
Researchers developed a machine learning system using Support Vector Machines (SVM) to automatically identify and classify microplastics in water samples based on their size, shape, and light-reflection properties captured through high-resolution imaging. The automated approach enables faster, more consistent microplastic monitoring compared to manual inspection, supporting real-time pollution tracking.
Classification of Microplastic Particles in Water using Polarized Light Scattering and Machine Learning Methods
Researchers developed a reflection-based, in-situ classification method for microplastic particles in water using polarized light scattering combined with machine learning, successfully identifying colorless particles in the 50-300 micrometer range. The approach circumvents transmission-based interference problems and offers a pathway toward continuous, large-scale microplastic monitoring in aquatic environments.
Toward in Situ Identification of Microplastics in Water Using Raman Spectroscopy and Machine Learning
This study developed an early-stage system combining Raman spectroscopy and machine learning to identify microplastics directly in ocean water in real time, without needing to collect and process samples in a lab. A support vector machine classifier trained on spectral libraries correctly identified all pristine microplastic samples and most environmental ones, demonstrating that field-deployable automated detection is feasible. Accurate real-time monitoring tools are urgently needed to understand where microplastics concentrate in the ocean and to track pollution trends.
Raman Spectroscopy and Machine Learning for Microplastics Identification and Classification in Water Environments
Researchers combined Raman spectroscopy with machine learning algorithms for automated identification and classification of microplastics in water environments, achieving high accuracy in distinguishing different polymer types based on spectral fingerprints.
Microplastic detection and recognition system enabled by a triboelectric nanogenerator and machine learning techniques
Researchers developed a simple, rapid microplastic detection and identification device combining liquid-solid contact electrification with machine learning algorithms. The system could distinguish between different types of microplastics in water based on open-circuit voltage differences, offering a lower-cost and faster alternative to conventional detection methods.
Design and Development of an Advanced Sensor Prototype for the Detection of Microplastics
Researchers designed and developed an advanced sensor prototype for detecting microplastics in water, combining spectroscopic and signal processing technologies into a portable device. The prototype demonstrated accurate microplastic identification across multiple polymer types in field conditions.
Label-free impedimetric analysis of microplastics dispersed in aqueous media polluted by Pb2+ ions
Researchers developed a simple electrochemical method to distinguish between clean and lead-contaminated microplastics in water without needing complex laboratory equipment. The technique uses impedance measurements to rapidly detect whether microplastics carry adsorbed heavy metal pollutants. The approach could be useful for quick field assessments of how contaminated microplastics are in environmental water samples.
Coupling electrochemical and spectroscopic methods for river water dissolved organic matter characterization
Researchers combined electrochemical impedance spectroscopy with traditional light-based methods to better characterize dissolved organic matter in river water — organic compounds that interact with pollutants including microplastics. The integrated approach revealed patterns in organic matter composition that optical methods alone would miss, offering a more complete picture of water quality.
Microplastics Detection and Estimation by Electrical Impedance Spectroscopy Advances: Recent Trends
This review examines recent advances in electrical impedance spectroscopy (EIS) as a detection and estimation method for microplastics, surveying emerging trends in sensor design and signal analysis. The authors assess the potential of EIS-based approaches as rapid, cost-effective alternatives to conventional spectroscopic identification methods.
Flow-Through Quantification of Microplastics Using Impedance Spectroscopy
Impedance spectroscopy was demonstrated as a high-throughput, flow-through method for quantifying and sizing microplastics in water without visual sorting or preprocessing, with spike-and-recovery experiments in tap water validating its potential for rapid environmental monitoring.
Polymer bead size revealed via neural network analysis of single-entity electrochemical data
A neural network was trained to extract microplastic particle size from electrochemical current-spike data recorded when individual polymer beads collide with a microelectrode — a method that avoids the need for optical microscopy. Accurate near-real-time sizing of microplastics in solution is an important analytical advance for water quality monitoring, where detecting and characterizing small plastic particles quickly and affordably remains a major technical challenge.
Toward Continuous Nano-Plastic Monitoring in Water by High Frequency Impedance Measurement With Nano-Electrode Arrays
Researchers explored high-frequency impedance measurements using CMOS nano-electrode arrays as a potential tool for real-time, label-free monitoring of nanoplastic particles in water, demonstrating nano-scale detection capability with potential for continuous environmental monitoring.
Study on marine microplastics monitoring based on infrared spectroscopy technology
Researchers developed an infrared spectroscopy-based monitoring system for marine microplastics, applying support vector machine algorithms to hyperspectral images to identify plastic types and abundances in seawater. The study found microplastic abundances ranging from roughly 5 to 39 particles per litre across sampling sites, with fibers (53-68%) and debris (23-34%) as dominant shapes, demonstrating the method's feasibility for rapid environmental monitoring.