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Papers
20 resultsShowing papers similar to Differentiating Microplastics from Natural Particles in Aqueous Suspensions Using Flow Cytometry with Machine Learning
ClearFlow Cytometry as a Rapid Alternative to Quantify Small Microplastics in Environmental Water Samples
Researchers developed a flow cytometry method using fluorescent staining to rapidly detect and quantify small microplastics (1-50 micrometers) in environmental water samples, achieving over 80% recovery rates and significantly reducing analysis time compared to traditional microscopy.
A novel high-throughput analytical method to quantify microplastics in water by flow cytometry
Researchers developed a faster, high-throughput method using flow cytometry — a technology that rapidly counts and characterizes particles in liquid — to measure microplastics in water, achieving about 97% accuracy across multiple plastic types and sizes and offering a practical alternative to slow, labor-intensive microscopy-based counting.
Preliminary Results From Detection of Microplastics in Liquid Samples Using Flow Cytometry
Researchers developed a novel flow cytometry approach for in-situ detection and quantification of microplastics in liquid samples using fluorescent staining, testing nine polymer types under controlled laboratory conditions. The method offers a high-throughput alternative to traditional time-consuming microplastic detection protocols that risk sample contamination.
Flow cytometry as new promising detection tool for micro and submicron plastic particles
Researchers evaluated flow cytometry as a detection tool for micro- and nanoplastics, testing its ability to rapidly identify and count plastic particles in environmental and biological samples. Results demonstrated that flow cytometry offers a promising high-throughput approach for microplastic detection compared to more time-intensive conventional methods.
Can flow cytometry emerge as a high-throughput technique for micro- and nanoplastics analysis in complex environmental aqueous matrices?
Researchers reviewed the potential of flow cytometry — a technique that rapidly analyzes individual particles — as a high-throughput tool for detecting micro- and nanoplastics in water samples, finding it excels at measuring particles smaller than 20 micrometers that other methods struggle to detect. Using fluorescent dyes to tag plastics, the approach could enable near-real-time environmental monitoring at a scale no other current technique can match.
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.
Microplastics’ Shape and Morphology Analysis in the Presence of Natural Organic Matter Using Flow Imaging Microscopy
Researchers introduced an innovative flow imaging microscopy approach for rapidly identifying and quantifying microplastics in wastewater treatment plant samples. The study demonstrates that this method can simultaneously capture and classify polyethylene and polystyrene particles while also analyzing how natural organic matter affects microplastic shape and morphology.
Machine learning enhanced machine vision system for micro-plastics particles classification
Researchers developed a machine learning-based classification system using fluorescence microscopy with Nile Red staining to identify and categorize microplastic types in environmental samples, aiming to provide a faster and more automated alternative to labor-intensive manual identification methods.
Quantitively Analyzing the Variation of Micrometer-Sized Microplastic during Water Treatment with the Flow Cytometry-Fluorescent Beads Method
Researchers developed a flow cytometry-fluorescent bead method for quantitatively measuring the removal of micrometer-sized microplastics during water treatment processes, demonstrating a rapid and reliable analytical approach for evaluating treatment plant efficiency.
Evaluating theEfficiency of Enhanced Coagulationfor Nanoplastics Removal Using Flow Cytometry
Researchers evaluated the efficiency of enhanced coagulation for removing nanoplastics from water using flow cytometry as a quantification tool, addressing the interconnected challenges of nanoplastic removal and detection in conventional water treatment systems.
Identification of Microplastics in Aquatic Environments Using Oxidative Treatment and Automated Image Analysis
Researchers developed a cost-effective and replicable method for detecting microplastics in freshwater environments using oxidative treatment to digest organic matter from water samples, enabling cleaner isolation and more accurate identification of MP particles without requiring expensive instrumentation.
[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.
Detection 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.
Microplastic Identification via Holographic Imaging and Machine Learning
Researchers combined holographic imaging with machine learning algorithms to automatically identify and classify microplastics in water samples, achieving accurate particle detection without manual microscopy. This automated approach could significantly speed up microplastic monitoring in environmental samples.
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.
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.
Flow cytometry as new promising detection tool for micro and submicron plastic particles
Researchers evaluated flow cytometry as a tool for detecting and counting micro- and submicron plastic particles in environmental and biological samples. The method offered rapid throughput and the ability to distinguish plastic particles from biological material, but required careful optimization for complex matrices.
Evaluating the Efficiency of Enhanced Coagulation for Nanoplastics Removal Using Flow Cytometry
Flow cytometry was used to quantify fluorescently labeled nanoplastics removal during enhanced coagulation-flocculation water treatment, demonstrating that this technique enables accurate detection and process optimization for nanoplastic removal in drinking water treatment.
Real-Time Quantification of Microplastics in Aquatic Systems via Fluorescence Microscopy
Researchers developed a real-time fluorescence microscopy method capable of quantifying microplastics in aquatic systems with high precision, providing a faster and more accessible tool for monitoring microplastic contamination in drinking water reservoirs.
Deep Learning-Based Image Recognition System for Automated Microplastic Detection and Water Pollution Monitoring
This study developed a deep learning image recognition system to automate the detection and classification of microplastics from microscopy images of water samples. The system achieved high accuracy across particle types and sizes, offering a scalable and less labor-intensive alternative to manual microscopy for large-scale water pollution monitoring.