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Papers
20 resultsShowing papers similar to Identification of Microplastics in Aquatic Environments Using Oxidative Treatment and Automated Image Analysis
ClearA straightforward method for microplastic extraction from organic-rich freshwater samples
Researchers developed a streamlined method for extracting microplastics from organic-rich freshwater samples using centrifugation and enzymatic digestion. They found that the approach effectively concentrated microplastics while preserving their integrity and minimizing contamination, at lower cost than existing methods. The study offers a practical and accessible protocol for laboratories studying microplastic pollution in freshwater environments.
A field deployable imaging system for detecting microplastics in the aquatic environment
Researchers built a portable imaging system for detecting microplastics in water that can be deployed directly in the field rather than requiring laboratory analysis. The system uses a de-scattering algorithm to produce clear images even in turbid water conditions and can identify particles as small as 50 micrometers. This low-cost tool could make routine microplastic monitoring of rivers, lakes, and coastal waters much more practical and accessible.
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.
Using optimized particle imaging of micro-Raman to characterize microplastics in water samples
Researchers developed a micro-Raman automatic particle identification technique that can characterize microplastics in water samples up to 100 times faster than traditional point-by-point detection methods, while maintaining high precision for identifying polymer types, sizes, and morphologies.
A novel method for organic matter removal from samples containing microplastics
Researchers developed a novel organic matter removal method for wastewater treatment plant sludge samples containing microplastics, demonstrating that the approach is more time- and cost-effective than existing techniques while preserving microplastic integrity for accurate quantification and identification.
The development of an analytical procedure for the determination of microplastics in freshwater ecosystems
Researchers developed an improved analytical procedure for detecting and identifying microplastics in freshwater environments. The method combines careful sample preparation with advanced instrumental techniques like infrared and Raman spectroscopy. The study highlights the importance of standardized methods to ensure that microplastic measurements across different studies are reliable and comparable.
Microplastics quantification in sewage sludge: A rapid and cost-effective approach
Researchers developed a rapid and cost-effective image-based method for quantifying microplastics in sewage sludge, using digital image analysis to count and size MP particles without requiring expensive spectroscopic equipment, offering a practical tool for routine sludge monitoring.
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.
Development of a Near-Infrared Imaging System for Identifying Microplastics in Water
Researchers developed a near-infrared imaging system capable of automatically identifying and characterizing microplastics suspended in water, successfully obtaining material identification images without the manual sorting typically required by conventional methods.
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.
A straightforward protocol for extracting microplastics from freshwater sediment with high organic content
Researchers developed a simplified protocol for extracting microplastics from organic-rich clayey freshwater sediments. The method achieved recovery rates exceeding 83% for five common plastic types and minimized particle loss by reducing container transfers, offering a practical and effective approach for environmental monitoring.
Protocol for microplastic pollution monitoring in freshwater ecosystems: Towards a high-throughput sample processing - MICROPLASTREAM
Scientists developed a standardized high-throughput protocol for processing freshwater microplastic samples, addressing the challenge that freshwater samples contain far more organic matter than seawater. Consistent, efficient processing methods are essential for generating comparable microplastic data across different rivers and lakes.
Identification of microplastics in wastewater samples by means of polarized light optical microscopy
Scientists tested polarized light optical microscopy as a rapid method for identifying microplastics in wastewater samples, finding it could distinguish synthetic polymer particles from natural debris based on their optical properties without requiring expensive spectroscopy equipment.
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.
Validation of microplastic sample preparation method for freshwater samples
Researchers developed and validated a standardized sample preparation method for extracting microplastics from freshwater samples, testing enzymatic digestion and density separation steps to improve recovery rates and reduce measurement uncertainty across different particle types.
Automatic Counting and Classification of Microplastic Particles
Researchers developed an automatic system for counting and classifying microplastic particles in marine samples, applying image analysis techniques to address the growing problem of plastic debris entering the food chain via marine species ingestion.
Differentiating Microplastics from Natural Particles in Aqueous Suspensions Using Flow Cytometry with Machine Learning
Researchers developed a stain-free flow cytometry method combined with machine learning to rapidly distinguish microplastics from natural particles like algae and sediment in water samples. The approach achieved identification accuracies over 93% and was validated in freshwater environmental samples, offering a time-efficient screening tool for microplastic monitoring.
Rapid and reliable detection of microplastics in drinking water using fluorescence microscopy
Researchers developed a fluorescence-based method for rapid detection and quantification of microplastics in drinking water, addressing the need for faster and more practical monitoring tools. The method achieved high sensitivity and allowed polymer discrimination without requiring expensive spectroscopic instrumentation.
A promising method for fast identification of microplastic particles in environmental samples: A pilot study using fluorescence lifetime imaging microscopy
Researchers piloted fluorescence lifetime imaging microscopy as a fast method for identifying microplastic particles in environmental samples. The study suggests this technique could simplify microplastic analysis by potentially eliminating the need for extensive extraction steps, enabling more direct identification of plastic particles in complex matrices.
Evaluation of the Presence of Microplastics in Wastewater Treatment Plants: Development and Verification of Strategies for Their Quantification and Removal in Aqueous Streams
Researchers evaluated microplastic presence in wastewater treatment plants and developed a pilot capture system capable of detecting, quantifying, and removing microplastic particles from water. The study found that conventional treatment processes are insufficient for complete microplastic removal, highlighting the need for dedicated technologies to address this gap in water treatment infrastructure.