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Papers
61,005 resultsShowing papers similar to Rapid classification of micro-particles using multi-angle dynamic light scatting and machine learning approach
ClearCharacterization and identification of microplastics using Raman spectroscopy coupled with multivariate analysis
Researchers developed a new method using Raman spectroscopy combined with machine learning to identify and classify seven types of microplastics with over 98% accuracy for most polymer types. The approach was also able to correctly identify real-world microplastic samples from snack boxes, water bottles, juice bottles, and medicine vials. This technique could make microplastic detection faster and more reliable compared to manual analysis methods.
Classification of household microplastics using a multi-model approach based on Raman spectroscopy
Researchers developed a machine learning approach combined with Raman spectroscopy to identify and classify microplastics commonly found in household products. By using multiple models together, they achieved over 98% accuracy in identifying seven types of standard and real-world microplastic samples, even after environmental weathering. This multi-model approach could provide a faster, more reliable tool for detecting and monitoring microplastic contamination in everyday settings.
Rapid identification of microplastic using portable Raman system and extra trees algorithm
Researchers developed a portable Raman spectroscopy system combined with a machine learning algorithm to rapidly identify and classify different types of microplastics in the field. Portable real-time identification tools are important for environmental monitoring programs that need to quickly characterize microplastics without sending samples to a laboratory.
Machine Learning Method for Microplastic Identification Using a Combination of Machine Learning and Raman Spectroscopy
Researchers developed a machine learning method for identifying microplastics using a combination of multiple spectroscopic techniques, improving classification accuracy beyond single-method approaches and enabling automated polymer identification.
Using optimized particle imaging of micro-Raman to characterize microplastics in water samples
Researchers developed a micro-Raman automatic particle identification technique that can characterize microplastics in water samples up to 100 times faster than traditional point-by-point detection methods, while maintaining high precision for identifying polymer types, sizes, and morphologies.
High-PrecisionRefractive Index-Based MicroparticleSorting Using Airy Beams
Researchers proposed an Airy beam-based optical sorting technique to separate microparticles by refractive index, enabling discrimination between particles of similar size but different composition. The method showed promise for identifying microplastic particles in complex mixtures and for medical diagnostic applications.
High-PrecisionRefractive Index-Based MicroparticleSorting Using Airy Beams
Researchers proposed an Airy beam-based optical sorting technique to separate microparticles by refractive index, enabling discrimination between particles of similar size but different composition. The method showed promise for identifying microplastic particles in complex mixtures and for medical diagnostic applications.
High-PrecisionRefractive Index-Based MicroparticleSorting Using Airy Beams
Researchers proposed an Airy beam-based optical sorting technique to separate microparticles by refractive index, enabling discrimination between particles of similar size but different composition. The method showed promise for identifying microplastic particles in complex mixtures and for medical diagnostic applications.
High-PrecisionRefractive Index-Based MicroparticleSorting Using Airy Beams
Researchers proposed an Airy beam-based optical sorting technique to separate microparticles by refractive index, enabling discrimination between particles of similar size but different composition. The method showed promise for identifying microplastic particles in complex mixtures and for medical diagnostic applications.
High-PrecisionRefractive Index-Based MicroparticleSorting Using Airy Beams
Researchers proposed an Airy beam-based optical sorting technique to separate microparticles by refractive index, enabling discrimination between particles of similar size but different composition. The method showed promise for identifying microplastic particles in complex mixtures and for medical diagnostic applications.
High-PrecisionRefractive Index-Based MicroparticleSorting Using Airy Beams
Researchers proposed an Airy beam-based optical sorting technique to separate microparticles by refractive index, enabling discrimination between particles of similar size but different composition. The method showed promise for identifying microplastic particles in complex mixtures and for medical diagnostic applications.
High-PrecisionRefractive Index-Based MicroparticleSorting Using Airy Beams
Researchers proposed an Airy beam-based optical sorting technique to separate microparticles by refractive index, enabling discrimination between particles of similar size but different composition. The method showed promise for identifying microplastic particles in complex mixtures and for medical diagnostic applications.
Machine learning-integrated droplet microfluidic system for accurate quantification and classification of microplastics
Scientists developed a new microplastic detection system that combines tiny droplet-based testing with machine learning to quickly identify and classify microplastic particles. This portable system can accurately detect microplastics on-site without expensive lab equipment, which could make widespread environmental and food safety monitoring much more practical.
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.
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.
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.
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.
Dynamic Light Scattering and Its Application to Control Nanoparticle Aggregation in Colloidal Systems: A Review
This review covers how dynamic light scattering technology is used to monitor and control nanoparticle clumping in colloidal systems. While not about microplastics directly, this measurement technique is relevant because it can help researchers better characterize nanoplastic particles in environmental and biological samples, improving our understanding of human exposure.
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.
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.
Rapidly Measuring Scattered Polarization Parameters of the Individual Suspended Particle with Continuously Large Angular Range
A method was developed to rapidly measure light scattering parameters of individual suspended particles across a wide angular range in a single measurement. The technique was validated using silica microspheres and demonstrated high measurement speed. Rapid characterization of particle optical properties has applications in monitoring suspended sediment, plankton, and microplastics in aquatic environments.
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.
Snapshot HoloSpec: dispersion-coded 4D feature learning for waterborne particle monitoring
Researchers developed a compact optical instrument that simultaneously captures 3D shape and spectral (color) information from water-suspended particles in a single snapshot, enabling real-time identification of six particle types including microplastics without any sample preparation. The system achieved a 98.1% classification score and could run continuously in aquatic environments. Real-time, in-situ monitoring tools like this could transform how we track microplastic pollution in rivers, lakes, and oceans.
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.