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Papers
61,005 resultsShowing papers similar to Polymer bead size revealed via neural network analysis of single-entity electrochemical data
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.
Microplastics detection by impact electrochemistry
This paper explores impact electrochemistry—a technique where individual particles colliding with an electrode generate detectable electrical pulses—as a method for detecting and characterizing microplastics in water. The approach offers the potential for rapid, single-particle detection without the need for complex sample preparation or optical instruments, which could make microplastic monitoring cheaper and more accessible. Developing faster and simpler detection methods is important for scaling up environmental monitoring programs.
Electrochemical Detection of Microplastics in Water Using Ultramicroelectrodes
Researchers developed a new electrochemical method for detecting microplastics in water using ultramicroelectrodes. The technique works by monitoring changes in electrical current when microplastic particles collide with and adsorb onto the electrode surface, and the size distributions obtained closely matched independent measurements, demonstrating its potential as a practical detection tool.
In situ Detection of Microplastics: Single Microparticle‐electrode Impacts
This study developed an electrochemical method using particle-impact techniques to detect and size individual polyethylene microparticles in water solution. The novel analytical approach enables detection of microplastics in aqueous samples in situ, without the need for filtration or sample processing that could introduce contamination.
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.
Convenient Size Analysis of Nanoplastics on a Microelectrode
Researchers developed a microelectrode-based method for size analysis of nanoplastics in suspension, enabling convenient, rapid characterization without specialized nanoparticle tracking instruments. The method accurately measured particle size distributions down to the nanometer range and showed potential for integration into routine environmental monitoring workflows.
Electrochemical Detection of Microplastics in Aqueous Media
Researchers demonstrated that microplastics in water can be detected electrochemically by counting oxygen reduction events when plastic particles collide with a carbon microwire electrode, finding a linear relationship between particle concentration and collision frequency.
Automatic Detection of Microplastics in the Aqueous Environment
Researchers developed a deep-learning system for real-time detection and counting of microplastics in freshwater, achieving high accuracy for particles 1 mm and larger.
Quantitative analysis of microplastics in water environments based on Raman spectroscopy and convolutional neural network
Researchers developed a method combining Raman spectroscopy with a convolutional neural network to measure microplastic concentrations in water. The approach achieved high accuracy across six different sizes of polyethylene particles in five real-world water environments, outperforming other machine learning models and offering a practical tool for quantitative microplastic monitoring.
When microplastics meet electroanalysis: future analytical trends for an emerging threat
This review examines the evolution of analytical methods for detecting microplastics, highlighting the emerging advantages of electroanalytical sensors — particularly for sub-micron particles — over traditional spectroscopic and thermal methods, and discussing the growing role of artificial intelligence in automated microplastic analysis.
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.
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.
Simultaneous Determination of Small Microplastics' Size, Type, Charge, Number and Mass Concentration by Machine-Learning Driven Single-Particle Sensing
Scientists developed a new method that can identify and measure tiny plastic particles (microplastics) in the environment much more precisely than before, determining their size, type, and amount all at once. This breakthrough could help us better understand how these plastic pollutants move through our environment and potentially affect human health. The technology represents a major step forward in tracking microplastic contamination, which is increasingly found in our food, water, and air.
Detection of microplastics in water using electrical impedance spectroscopy and support vector machines
Researchers developed an electrical impedance spectroscopy method combined with support vector machine classifiers that can distinguish polypropylene and polyolefin microplastics in water — including at varying salinity and organic content — offering a promising approach for rapid in-situ microplastic detection.
A Machine Learning Approach To Microplastic Detection And Quantification In Aquatic Environments
This study developed a machine learning approach for detecting and quantifying microplastics in aquatic environments, demonstrating that automated image analysis can improve throughput and accuracy compared to manual microscopic counting for environmental monitoring applications.
Integrating MetalAquaDect SERS platform: Machine-learning assisted real-time monitoring of sub-2mg/L microplastics and nanoplastics in complex matrices
Researchers used a machine learning-assisted SERS platform (AquaDect) to qualitatively and quantitatively detect microplastics and nanoplastics of multiple types and sizes in aqueous solutions at concentrations below 2 mg/L, demonstrating the approach across polystyrene, polyethylene, polypropylene, and PMMA.
Identification and velocity measurement of microplastics based on machine learning
Researchers developed a machine learning framework to simultaneously track multiple microplastics in water and measure their terminal settling velocities, capturing particle interaction dynamics that conventional single-particle tracking methods miss.
Emerging electrochemical techniques for identifying and removing micro/nanoplastics in urban waters
This review examines emerging electrochemical techniques for detecting and removing micro- and nanoplastics from urban waters, highlighting their advantages over conventional methods for enabling real-time monitoring and efficient degradation.
In Situ Determination of Chlorella Concentration Using Single Entity Electrochemistry
Researchers developed an electrochemical method for detecting individual algal cells in real time using an ultramicroelectrode and single-particle collision technique. The approach could distinguish individual Chlorella cells and relate collision frequency to algal concentration, offering potential for early detection of harmful algal blooms. While not directly focused on microplastics, the method provides a platform for monitoring water quality impacts related to microplastic-linked eutrophication.
Detecting Microplastics in Seawater with a Novel Optical Sensor Based on Artificial Intelligence Models
Detecting microplastics in seawater quickly and accurately is a major technical challenge, and this study developed a novel optical sensor that uses artificial intelligence to identify plastic particles from light-scattering data in real time. The AI-powered system was tested on seawater samples and showed promising accuracy for classifying microplastic types without the need for time-consuming laboratory processing. Automated in-situ sensors like this could enable continuous, large-scale ocean monitoring for microplastic pollution.
An Electrochemical Biosensing Approach for Detection of Microplastic Beads
Researchers developed an electrochemical enzyme-based biosensor to detect microplastic beads across a range of sizes in water, providing a simpler and lower-cost detection approach than conventional spectroscopic methods for environmental and public health monitoring.
Identification of microplastics using a convolutional neural network based on micro-Raman spectroscopy
Researchers combined micro-Raman spectroscopy with a neural network to identify microplastics, achieving over 99% accuracy across 10 different plastic types. The system was also tested on real environmental samples and performed well at classifying unknown particles. This AI-powered approach could make microplastic identification faster and more reliable for environmental monitoring.
Innovative methods for microplastic characterization and detection: Deep learning supported by photoacoustic imaging and automated pre-processing data
Researchers developed an innovative method combining photoacoustic imaging with deep learning to rapidly detect and characterize microplastics. The photoacoustic technology captured high-resolution images of diverse microplastic samples, while the neural network automated the classification process. The study demonstrates that this combined approach could enable faster, more accurate microplastic monitoring compared to conventional methods.
Riverine Microplastic Quantification: A Novel Approach Integrating Satellite Images, Neural Network, and Suspended Sediment Data as a Proxy
Researchers developed satellite-based models using neural network algorithms to estimate riverine microplastic concentrations, using suspended sediment concentration as a proxy, offering a cost-effective approach for broad-scale freshwater microplastic monitoring.