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Papers
61,005 resultsShowing papers similar to A fractal analysis of the holographic diffraction patterns for detecting microplastics among diatoms
ClearIdentification 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.
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.
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.
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.
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.
Microplastic pollution monitoring with holographic classification and deep learning
This study used digital holographic microscopy combined with deep learning to classify microplastic particles in water samples, achieving high classification accuracy and demonstrating the potential for automated, high-throughput microplastic monitoring.
On the use of machine learning for microplastic identification from holographic phase-contrast signatures
This study applied machine learning to identify microplastic types from holographic phase-contrast imaging signatures, achieving rapid automated classification. Automated identification tools are important for scaling up microplastic monitoring in marine waters where manual identification is too slow and labor-intensive.
Geometric-Optical Model of Digital Holographic Particle Recording System and Features of Its Application
Not relevant to microplastics — this paper describes a calibrated geometric-optical model for a submersible digital holographic camera used to study plankton in the ocean, improving the accuracy of particle size and position measurements.
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.
Digital holographic approaches to the detection and characterization of microplastics in water environments
This review examines advances in using digital holography as a high-throughput tool for detecting and characterizing microplastics in water. Researchers discuss both the hardware and software developments, including the growing role of artificial intelligence for classification tasks. The study highlights the emergence of field-portable holographic flow cytometers as a promising technology for real-time water monitoring of microplastic contamination.
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.
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.
Deep Classification of Microplastics Through Image Fusion Techniques
Deep neural networks were applied to classify microplastic fibers captured via digital holography microscopy, using image fusion techniques on the Holography Micro-Plastic Dataset benchmark. The study demonstrated promising accuracy for distinguishing microplastics from other debris, advancing automated microplastic identification in water quality monitoring.
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.
Digital holographic imaging and classification of microplastics using deep transfer learning
Researchers developed a digital holographic imaging system combined with deep learning to automatically classify and analyze microplastic particles in water samples. Automated imaging and AI-based identification could significantly speed up and standardize microplastic monitoring, reducing the labor-intensive manual counting currently required.
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.
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.
Snapshot Polarization-Sensitive Holography for Detecting Microplastics in Turbid Water
Researchers developed a new imaging technique combining holography and polarimetry to detect microplastic particles in turbid water, a setting where traditional detection methods struggle. The approach uses differences in how light polarizes when passing through plastic versus natural particles to distinguish microplastics even in murky conditions. The study demonstrates a promising tool for faster, more practical monitoring of microplastic pollution in real-world water 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.
Polarization 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.
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.
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.