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Papers
20 resultsShowing papers similar to Smartphone-based holographic measurement of polydisperse suspended particulate matter with various mass concentration ratios
ClearHolographic imaging and machine learning for microplastic size and shape analysis in water
Researchers used a portable holographic camera paired with deep-learning AI to rapidly measure the size and shape of microplastics floating in water, finding the lightweight MobileNetV2 model outperformed the larger ResNet101 in classification accuracy. The method offers a cost-effective, field-deployable tool for monitoring microplastics in drinking water at scale.
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.
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.
Smartphone microscopic method for imaging and quantification of microplastics in drinking water
Researchers developed a low-cost smartphone microscope system capable of imaging and quantifying microplastic particles as small as 20 micrometers in drinking water. The setup uses a small sapphire ball lens attached to a smartphone camera combined with a density-based pre-concentration step. The study offers an accessible and affordable alternative to expensive laboratory instruments for monitoring microplastic contamination in drinking water.
Smartphone-enabled rapid quantification of microplastics
A smartphone-based system was developed to rapidly quantify microplastics from environmental samples, reducing analysis time from hours or days to a much faster workflow without requiring expensive lab equipment. The method was validated against standard techniques and shown to be suitable for field-deployable microplastic monitoring.
Holographic Classifier: Deep Learning in Digital Holography for Automatic Micro-objects Classification
Researchers developed a deep learning system using digital holography to automatically classify micro-objects such as microplastics and pollutant particles without manual image processing. The system achieved fast, accurate identification, offering a promising automated tool for environmental pollution monitoring.
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.
Towards cleaner waters: Advancing pollutant detection with artificial intelligence-assisted digital in-line holographic microscopy
This review examines how AI-assisted digital in-line holographic microscopy (DIHM) enables real-time detection of water pollutants including nano/microplastics, oil spills, and harmful algal blooms, comparing its capabilities against alternative detection techniques.
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.
Cost-Effective and Wireless Portable Device for Rapid and Sensitive Quantification of Micro/Nanoplastics
Researchers designed a low-cost, wireless portable device that can rapidly detect and quantify micro- and nanoplastics using fluorescent labeling and smartphone-based imaging. The device achieved sensitive detection across particle sizes from 50 nanometers to 10 micrometers and could transmit results wirelessly for analysis using machine learning algorithms. The technology could make field-based microplastic monitoring far more accessible and affordable than current laboratory methods.
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.
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.
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.
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.
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.
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.
Compact off-axis holographic slide microscope: design guidelines
Researchers designed a compact, 3D-printed off-axis holographic microscope that can identify and image microparticles in fluid in real time. This portable, low-cost device could be deployed in the field for monitoring microplastic contamination in water samples.
Merging holography, fluorescence, and machine learning for in situ continuous characterization and classification of airborne microplastics
Researchers developed an instrument combining holography, fluorescence, and machine learning for continuous, real-time characterization of airborne microplastics. The system can identify and classify microplastic particles in situ without requiring laboratory sample collection and analysis. The study represents an advance in monitoring technology that could improve understanding of atmospheric microplastic transport and human exposure.
Merging holography, fluorescence, and machine learning for in situ, continuous characterization and classification of airborne microplastics
This study combined holography, fluorescence microscopy, and machine learning for continuous in situ detection and classification of airborne microplastics without the need for sample collection and laboratory analysis. The system enabled real-time characterization of particle size, shape, and type in ambient air.
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.