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Papers
61,005 resultsShowing papers similar to Snapshot Polarization-Sensitive Holography for Detecting Microplastics in Turbid Water
ClearPolarization Holographic Imaging for High-throughput Microplastic Analysis
Researchers developed a polarization holography system integrated with deep learning for high-throughput microplastic detection and analysis in aqueous environments. The system enables dynamic, real-time multimodal monitoring of microplastics by leveraging polarization contrast to distinguish particles in liquid samples.
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-sensitive digital holography for microplastic identification through scattering media
Researchers designed a polarization-sensitive holographic imaging system capable of identifying transparent microplastics through scattering media by measuring the degree of linear polarization (DoLP) as an angle-independent discriminating feature. The system enables non-destructive differentiation of microplastic types in turbid or complex optical environments where conventional imaging methods fail.
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.
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.
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 Digital Holographic systems to counteract microplastic pollution in marine waters
Researchers developed a digital holography system capable of detecting and classifying microplastic particles in seawater in a label-free, high-throughput manner. The system can identify plastic particles that are otherwise invisible to the naked eye and can be adapted for use with microfluidic devices. This technology offers a faster and more compact alternative to traditional microscopy methods for marine microplastic monitoring.
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.
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.
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.
Toward an All-Optical Fingerprint of Synthetic and Natural Microplastic Fibers by Polarization-Sensitive Holographic Microscopy
Researchers developed a polarization-sensitive digital holographic microscopy method that can generate unique all-optical fingerprints to distinguish synthetic microplastic fibers from natural fibers in water without destroying the sample.
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.
Micro-Objects Classification for Microplastic Pollution Detection using Holographic Images
Researchers developed a machine learning system that uses holographic 3D images to automatically classify microplastics in water samples, distinguishing them from other microscopic particles with high precision. Current microplastic monitoring is slow and labor-intensive, so automated detection tools are essential for large-scale environmental surveillance. This approach could significantly speed up the monitoring of microplastic pollution in aquatic environments.
Holographic characterization of contaminants in water: Differentiation of suspended particles in heterogeneous dispersions
Researchers used holographic imaging — a technique that measures the size and light-bending properties of individual particles — to simultaneously identify polystyrene microbeads, bacteria, and oil droplets in the same water sample, demonstrating a faster and more informative way to detect and classify multiple types of waterborne contaminants at once.
Detection of microplastic release into water from plastic containers based on lensless digital holography
Researchers used lensless digital holography to detect microplastics released from plastic food delivery containers into water, demonstrating that the technique can differentiate microplastic particles from other impurities and quantify their release under realistic conditions.
Automatic Detection of Microplastics by Deep Learning Enabled Digital Holography
Researchers developed a digital holography system combined with deep learning to automatically detect and identify microplastics in water without manual image analysis. The system processes raw holographic images directly, offering a faster and more scalable approach to microplastic monitoring in environmental samples.
Complete holography‐based system for the identification of microparticles in water samples
Researchers developed a comprehensive holography-based system for identifying and classifying microparticles — including microplastics — in water samples using microscopic holographic projections, designed for researchers without specialist holography expertise. The system is deployable as part of an autonomous sailboat robot for large-scale environmental monitoring of diverse microplastic types in water bodies.
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.
Compact holographic microscope for imaging flowing microplastics
Researchers developed a compact holographic microscope capable of imaging flowing microplastics in aquatic environments, providing a fast, quantitative method for real-time characterization of plastic particle size and shape distributions.
Identification of Microplastics Based on the Fractal Properties of Their Holographic Fingerprint
Researchers developed an AI-enabled holographic imaging approach to identify microplastics in water using the fractal properties of their holographic fingerprints, offering a fast, label-free identification method.
Digital holographic microplastics detection and characterization in heterogeneous samples via deep learning
Researchers used digital holographic microscopy combined with deep learning to detect and characterize microplastic particles in heterogeneous samples containing algae, microorganisms, and other natural particles. This automated approach could improve the speed and accuracy of environmental microplastic monitoring.
Imaging‐Based Lensless Polarization‐Sensitive Fluid Stream Analyzer for Automated, Label‐Free, and Cost‐Effective Microplastic Classification
Researchers developed an imaging-based lensless polarization-sensitive fluid stream analyzer that combines digital in-line holography with polarization sensitivity for automated, label-free, and cost-effective in situ detection and classification of microplastics in fluid streams, offering a practical tool for continuous aquatic monitoring without the labor costs of traditional sampling.
Identification of microplastics in a large water volume by integrated holography and Raman spectroscopy
A new technique combining holography and Raman spectroscopy was demonstrated to identify plastic pellets suspended in a large volume of water without physical contact. This non-destructive approach could enable real-time, in-water microplastic detection for environmental monitoring.
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.