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Papers
61,005 resultsShowing papers similar to Microplastic detection and recognition system enabled by a triboelectric nanogenerator and machine learning techniques
ClearDetection of Microplastics Based on a Liquid–Solid Triboelectric Nanogenerator and a Deep Learning Method
Scientists developed a new microplastic detection device based on a liquid-solid friction generator combined with deep learning AI to identify different types of plastic particles. The system can classify microplastics by material type with high accuracy using electrical signals generated when plastic particles contact a liquid surface. This technology could make it easier and cheaper to monitor microplastic contamination in water supplies.
Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems
This review examines how triboelectric nanogenerators -- devices that harvest energy from motion and contact between materials -- can be combined with machine learning to create self-powered sensors for the Internet of Things. While not directly about microplastics, the technology has potential applications in environmental monitoring, including detecting microplastic contamination in water and air without requiring external power sources.
Machine learning-integrated droplet microfluidic system for accurate quantification and classification of microplastics
Scientists developed a new microplastic detection system that combines tiny droplet-based testing with machine learning to quickly identify and classify microplastic particles. This portable system can accurately detect microplastics on-site without expensive lab equipment, which could make widespread environmental and food safety monitoring much more practical.
Real-time detection for water pollutant based on triboelectric nanogenerators and machine learning
Scientists have developed a new device that can detect dangerous pollutants in water—including heavy metals, microplastics, and rust—by running water through a special sponge that creates electrical signals when contaminated. The system correctly identified these harmful substances 87% of the time and could work in different temperatures and water conditions. This technology could help communities quickly test their drinking water for pollutants that can cause health problems, potentially making water safety monitoring faster and more affordable.
Microplastic in situ detection based on a portable triboelectric microfluidic sensor
Researchers developed a portable triboelectric microfluidic sensor that detects microplastics in water by measuring electrical charges generated as particles flow through a microchannel, demonstrating linear response to polystyrene particle size and concentration for field-deployable environmental monitoring.
Approaches 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.
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.
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.
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.
Cost-Effective and Wireless Portable Device for Rapid and Sensitive Quantification of Micro/Nanoplastics
Researchers developed a wireless portable device for rapid quantification of micro- and nanoplastics in water samples, offering a field-deployable alternative to laboratory-based analysis for environmental monitoring.
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.
Machine Learning Method for Microplastic Identification Using a Combination of Machine Learning and Raman Spectroscopy
Researchers developed a machine learning method for identifying microplastics using a combination of multiple spectroscopic techniques, improving classification accuracy beyond single-method approaches and enabling automated polymer identification.
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.
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.
3D Plasmonic Gold Nanopocket Structure for SERS Machine Learning‐Based Microplastic Detection
Researchers developed a new paper-based detection system that uses gold nanostructures and machine learning to quickly identify microplastics in water samples. The device works like a filter and sensor combined, capturing microplastics and identifying their type without complex sample preparation. This portable technology could make it much easier to test drinking water and environmental samples for microplastic contamination on-site.
Rapid identification of microplastic using portable Raman system and extra trees algorithm
Researchers developed a portable Raman spectroscopy system combined with a machine learning algorithm to rapidly identify and classify different types of microplastics in the field. Portable real-time identification tools are important for environmental monitoring programs that need to quickly characterize microplastics without sending samples to a laboratory.
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.
Clasificación de microplásticos usando lenguas electrónicas
Colombian engineers developed an electrochemical sensor array (electronic tongue) combined with pattern recognition algorithms to classify microplastics in drinking water samples in near-real time. The system successfully distinguished between clean tap water and water spiked with PET microplastics, demonstrating a potential alternative to slow, labor-intensive laboratory methods. Fast, continuous monitoring tools like this are critical for water utilities that need early warning of microplastic contamination.
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.
Design, fabrication, and application of electrochemical sensors for microplastic detection: a state-of-the-art review and future perspectives
This review covers recent advances in electrochemical sensors for detecting microplastics in environmental samples, which offer advantages in sensitivity and portability over conventional laboratory methods. Researchers highlight strategies using nanomaterials, molecular imprinting, and surface-enhanced techniques to improve detection capabilities. The study suggests that electrochemical sensors represent a promising path toward affordable, rapid, on-site monitoring of microplastic pollution.
Innovative prototype for the mitigation of water pollution from microplastics to safeguard the environment and health
Researchers developed an innovative prototype device for removing microplastics from water through a combination of filtration and electrocoagulation, demonstrating high MP removal efficiency from both synthetic and real water samples in controlled trials.
Artificial Intelligence-Based Microfluidic Platform for Detecting Contaminants in Water: A Review
This review explores how microfluidic devices combined with artificial intelligence can detect water pollutants including microplastics and nanoplastics in real-time, outside the laboratory. Traditional water testing requires large lab equipment, but these portable chip-based systems can identify contaminants quickly and accurately using machine learning. This technology could improve monitoring of microplastic contamination in drinking water and other water sources.
Droplet-based Opto-microfluidic Device for Microplastic Sensing in Aqueous Solutions
Researchers developed a microfluidic device using light to detect plastic microspheres in water droplets, offering a new tool for identifying microplastic contamination in aquatic environments.
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.