We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
20 resultsShowing papers similar to Machine learning-integrated droplet microfluidic system for accurate quantification and classification of microplastics
ClearDetection of Microplastics Using Machine Learning
Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual 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 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.
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.
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.
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 Method Research of Intelligent Detection System for Marine Microplastics Driven by Microfluidic Chip
Researchers designed an intelligent detection system for marine microplastics using a microfluidic chip combined with machine learning image analysis. Simulation testing validated the chip's ability to capture and sort microplastic particles from seawater samples, with AI classification achieving high accuracy across particle types.
Efficient Prediction of Microplastic Counts from Mass Measurements
Scientists developed machine learning models to estimate the number of microplastic particles from aggregate weight measurements, potentially offering a faster and cheaper alternative to manual counting. Efficient quantification methods are critical for large-scale monitoring of microplastic contamination in environmental samples.
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.
[Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics].
This review examines the application of machine learning (ML) methods for identifying microplastics and assessing their environmental risks, covering techniques for improving the accuracy and reliability of microplastic detection across different environmental media. Researchers highlight how ML can systematically analyse pollution characteristics and support ecological risk evaluation of microplastic contamination.
Automated Machine-Learning-Driven Analysis of Microplastics by TGA-FTIR for Enhanced Identification and Quantification
Researchers developed an automated machine-learning system to identify and measure microplastics using a combination of heat analysis and infrared spectroscopy. The system can distinguish between different plastic types more accurately and faster than manual methods. Better detection tools like this are important because reliable measurement of microplastics in food, water, and the environment is essential for understanding human exposure levels.
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.
Fluorescence Machine Vision-Based Rapid Quantitative Characterization of Microplastics
Scientists developed a new system that uses special fluorescent dye and artificial intelligence to quickly detect and count microplastics (tiny plastic particles) in samples. The technology is faster and cheaper than current methods, which could help researchers better track these particles that may pose health risks when they get into our food and water. This advance could lead to better monitoring of microplastic pollution and help protect human health.
Deep learning-powered efficient characterization and quantification of microplastics
Researchers developed an artificial intelligence framework that uses deep learning to automatically identify and quantify microplastics from infrared spectra and visual images. The system achieved high accuracy in classifying plastic types and counting particles, dramatically reducing the time needed compared to manual analysis. This tool could make large-scale microplastic monitoring faster and more consistent across different research laboratories.
Predicting the toxicity of microplastic particles through machine learning models
Researchers developed machine learning models to predict microplastic particle toxicity from physical and chemical descriptors, addressing the classification challenge posed by the enormous diversity of particle types that cannot be characterized using conventional chemical hazard methods. The models provided accurate toxicity predictions across diverse microplastic types, offering a practical screening tool for the field.
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.
Machine Learning Advancements and Strategies in Microplastic and Nanoplastic Detection
This systematic review looks at how machine learning is improving our ability to detect tiny microplastics and nanoplastics in the environment. Better detection methods matter because accurately measuring plastic contamination is the first step toward understanding — and reducing — human exposure.
Compact low-cost sensor for microplastics detection and classification in marine and aquatic environments
Researchers developed a compact, low-cost sensor for detecting and classifying microplastics in marine and aquatic environments, designed to reduce the economic burden of MP monitoring along coastlines and enable more frequent and scalable environmental surveillance.
Compact low-cost sensor for microplastics detection and classification in marine and aquatic environments
Researchers developed a compact, low-cost sensor for detecting and classifying microplastics in marine and aquatic environments, designed to reduce the economic burden of MP monitoring along coastlines and enable more frequent and scalable environmental surveillance.
Optofluidic light-droplet interaction for rapidly assessing the presence of plastic microspheres within aqueous suspensions
Scientists created a new device that can quickly detect tiny plastic particles (called microplastics) in water by shining light through water droplets and measuring changes in brightness. The device can spot extremely small amounts of plastic pollution - as little as 0.13 milligrams per gram of water. This technology could help us better monitor plastic contamination in our drinking water and environment, which is important since these tiny plastics can harm both ecosystems and human health.