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Papers
20 resultsShowing papers similar to Optimized Classification of Suspended Particles in Seawater by Dense Sampling of Polarized Light Pulses
ClearProbing Individual Particles in Aquatic Suspensions by Simultaneously Measuring Polarized Light Scattering and Fluorescence
Researchers developed a portable optical sensor that simultaneously measures polarized light scattering and fluorescence from individual particles in water, enabling classification of microplastics versus microalgae in situ. This dual-measurement approach improves particle identification accuracy compared to single-measurement methods.
Differentiation of suspended particles by polarized light scattering at 120°
A polarized light scattering method was developed to rapidly distinguish different types of suspended particles in seawater, including microplastics, microalgae, and sediment. This optical approach could enable faster, real-time monitoring of microplastic concentrations in ocean water.
Classification of suspended particles in seawater using an in situ polarized light scattering prototype
This study developed and field-tested an underwater sensor that uses polarized light scattering to distinguish between microplastics, sediment particles, and phytoplankton in seawater in real time. Lab tests showed classification accuracy above 85%, and the device was successfully deployed in a Chinese coastal bay across two seasons. Such in-situ monitoring tools could greatly improve our ability to track microplastic concentrations in the ocean without the labor-intensive sample collection and lab analysis currently required.
Classification of Microplastic Particles in Water using Polarized Light Scattering and Machine Learning Methods
Researchers developed a reflection-based, in-situ classification method for microplastic particles in water using polarized light scattering combined with machine learning, successfully identifying colorless particles in the 50-300 micrometer range. The approach circumvents transmission-based interference problems and offers a pathway toward continuous, large-scale microplastic monitoring in aquatic environments.
Recognition of microplastics suspended in seawater via refractive index by Mueller matrix polarimetry
Researchers developed a method to identify microplastics suspended in seawater using Mueller matrix polarimetry, which measures how particles interact with polarized light. The study successfully classified different types of microplastics based on their refractive index, even for irregularly shaped particles with varying sizes, suggesting this approach could advance in-situ microplastic detection in ocean water.
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.
In-situ Detection Method for Microplastics in Water by Polarized Light Scattering
Researchers developed an in-situ detection method for microplastics in water using polarized light scattering at 120 degrees, enabling real-time measurement of individual particles without sample collection or laboratory processing.
Optical System for In-situ Detection of Microplastics
Researchers developed a portable optical system capable of detecting, identifying, continuously monitoring, and quantifying microplastics in situ at natural water bodies. The system uses optical techniques to observe the temporal behavior of microplastic concentrations at fixed locations, enabling real-time environmental monitoring without sample collection and laboratory processing.
Underwater Particle Classification Detector using Mueller Matrix and Fluorescence Signal
A new underwater particle classification detector using Mueller matrix polarimetry was developed to distinguish microplastics from natural particles like sediment and organic matter in situ. The instrument advances real-time, in-water monitoring of microplastics without requiring sample collection and laboratory analysis.
An Artificial Intelligence based Optical Sensor for Microplastic Detection in Seawater
Researchers developed an AI-based optical sensor system combining an optical detection subsystem and an image acquisition subsystem to detect and identify microplastic particles in seawater, distinguishing them from naturally occurring marine particles. The device applies AI algorithms to analyze consecutive image frames and classify particles as microplastic or non-microplastic, with the full system housed in two portable cases.
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.
Imaging-based lensless polarisation-resolving fluid stream analyser for automated, label-free and cost-effective microplastic classification
Researchers developed an imaging-based, lensless, polarisation-resolving fluid stream analyser for automated, label-free, and cost-effective microplastic classification in liquid samples, addressing the lack of in-situ monitoring solutions for ocean environments. The device operates at high flow rates using a custom illumination circuit to reduce motion blur, providing quantitative classification of microplastics without the labour intensity and cost of traditional sampling methods.
A Portable Optical Sensor for Microplastic Detection: Development and Calibration
Researchers built a portable, low-cost optical sensor prototype designed to detect microplastics by shining multiple wavelengths of light through water samples. The device measures how different plastic particles absorb and scatter light, producing color spectra that can help identify microplastics. The sensor offers an affordable field-deployable option for environmental monitoring, with potential future improvements using machine learning for automated identification.
Holographic and polarization features analysis for microplastics characterization and water monitoring
Researchers explored digital holography and polarization imaging as a combined technique for characterizing and classifying microplastics in water, computing features including angle of polarization (AoP) and degree of linear polarization (DoLP) to distinguish microplastics from biological and natural particles. The method demonstrated potential for real-time, non-contact, in situ microplastic detection and water quality monitoring.
High-throughput microplastic assessment using polarization holographic imaging
Researchers built a portable, low-cost system that uses holographic imaging and polarized light combined with deep learning to automatically detect, count, and classify microplastics in water in real time — without lengthy sample preparation. This tool significantly speeds up microplastic monitoring and could be widely deployed for environmental surveillance.
On the Potential for Optical Detection of Microplastics in the Ocean
This study examines the potential for optical methods to detect microplastics in ocean water at large spatial scales, noting that while optical detection is promising for overcoming the limitations of discrete water sampling, methods remain in early development and reference libraries of microplastic optical properties are sparse.
Detection of Microplastics in Water and Ice
Researchers explored optical detection methods for identifying microplastics floating on water surfaces or trapped in ice, taking advantage of the unique light-reflecting properties of different plastic types. Advances in optical detection are important for developing faster, non-destructive tools for monitoring microplastic pollution.
Polarization digital holography for advanced classification of microplastic particles
Researchers developed a polarization digital holography approach for classifying microplastic particles based on their optical birefringence properties, requiring minimal sample preparation. The non-destructive method can distinguish microplastics from biological material by detecting how particles alter light polarization states.
Outlook on optical identification of micro- and nanoplastics in aquatic environments
Researchers studied the optical properties of micro- and nanoplastics and evaluated near-infrared spectroscopy as a detection method for plastic particles in water, finding that optical techniques show promise for rapid, non-destructive identification. Improved optical detection methods could enable faster and more cost-effective monitoring of plastic pollution in aquatic environments.
Computational polarized holography for automatic monitoring of microplastics in scattering aquatic environments
Researchers developed an integrated imaging system based on computational polarized holography for automatic monitoring of microplastics in aquatic environments. The system enables accurate 3D tracking of dynamic microplastic particles, and a hybrid de-scattering algorithm substantially improves image quality even in turbid water conditions. An unsupervised clustering method was also developed to identify and classify different microplastics based on their multimodal features without manual annotation.