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Papers
61,005 resultsShowing papers similar to Statistical Mueller matrix driven discrimination of suspended particles
ClearRecognition 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.
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.
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.
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.
Probing 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.
Optimized Classification of Suspended Particles in Seawater by Dense Sampling of Polarized Light Pulses
Researchers developed an optical method using polarized light pulses to classify suspended particles in seawater, aiming to distinguish microplastics from natural particles like algae in situ. A reliable in-water optical sensor for microplastics would greatly improve environmental monitoring capability.
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.
Characterization of common plastic microspheres through holographic Mueller matrix imaging
Researchers used polarimetric in-line holographic imaging to measure Mueller matrices from common plastic microspheres, demonstrating that different plastic types exhibit distinctive polarimetric signatures. The study presents this holographic Mueller matrix approach as a deterministic tool for automated microplastic detection and characterization.
Advancing the Understanding of Microplastic Weathering: Insights from a Novel Polarized Light Scattering Approach
Researchers introduced a polarized light scattering technique to rapidly characterize microplastic weathering, which alters the physical and chemical properties of particles and affects their environmental behavior. The approach provides high-throughput, real-time insights into weathering-induced surface and structural changes that are difficult to capture with conventional methods.
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.
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.
Sorting microplastics from other materials in water samples by ultra-high-definition imaging
Researchers used a commercial particle analyzer with ultra-high-definition imaging to sort and identify microplastic particles in water samples. The device successfully distinguished between different plastic types based on how light scatters through or off their surfaces, and could separate microplastics from air bubbles and other non-plastic particles. The study demonstrates a relatively fast and accessible method for characterizing microplastic contamination in water.
Effect of medium refractive index on microparticle characterization by optical scattering
Researchers investigated how the refractive index of the medium affects optical scattering measurements used to characterize microplastic particles, finding that medium choice significantly influences size estimation accuracy. Machine learning was applied to improve classification of particles under varying optical conditions.
Material analysis with polarization holography and machine learning
Researchers developed a polarization holographic imaging system combined with machine learning to identify different materials, demonstrating the approach on microplastic identification. This novel optical method could become a fast, non-destructive tool for classifying microplastics in environmental samples.
Quantification of Very Low Concentrations of Colloids with Light Scattering Applied to Micro(Nano)Plastics in Seawater
Researchers evaluated static and dynamic light scattering techniques for detecting and quantifying colloidal microplastic and nanoplastic particles (0.1-0.8 micron diameter) at very low concentrations in marine water, demonstrating their potential as rapid, non-destructive monitoring tools.
Comprehensive Investigation of Microplastics size distribution in Marine Environment: Detection, Quantification, and Optical Analysis Using Static Light Scattering (SLS)
This study applied static light scattering to characterize microplastic size distributions in marine water samples, demonstrating the technique's capacity to rapidly quantify particle concentrations across a wide size range for environmental monitoring.
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.
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.
Polarization transmission characteristics of polystyrene microplastics in aqueous environments
This study investigated how polarized light interacts with polystyrene microplastic particles suspended in water. While primarily a detection methods paper, it advances techniques for identifying microplastics in water and biological fluids like blood and urine, which is essential for accurately measuring human exposure levels.
Batch analysis of microplastics in water using multi-angle static light scattering and chemometric methods
This study presents a batch analysis approach using multi-angle light scattering combined with chemometrics to measure microplastic size and concentration in water samples more quickly than single-particle methods. Faster analytical approaches are needed to scale up environmental microplastic monitoring.
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.
Intelligent polarization-sensitive holographic flow-cytometer: Towards specificity in classifying natural and microplastic fibers
An intelligent polarization-sensitive holographic flow cytometer was developed to classify natural and synthetic microplastic fibers at the micron scale, addressing the need for automated identification of the dominant form of microplastic pollution -- fibers -- in aquatic ecosystems.
Smart polarization and spectroscopic holography for real-time microplastics identification
Researchers developed a new optical imaging system called SPLASH that simultaneously captures polarization, holographic, and texture data from tiny particles — without needing a traditional spectrometer — and used machine learning to identify different types of microplastics with high accuracy. This approach could enable faster, more practical real-time monitoring of microplastic pollution in water.
Real-time microplastic detection using polarization digital holographic microscope
Researchers developed a real-time microplastic detection system using a polarization digital holographic microscope, enabling identification and characterization of MP particles in water based on their optical properties without the need for chemical staining or extensive sample preparation.