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Papers
61,005 resultsShowing papers similar to Trapping and chemical characterization of sub-microplastics using Raman optical tweezers with machine learning
ClearRaman Tweezers for Small Microplastics and Nanoplastics Identification in Seawater
Researchers used Raman tweezers - optical tweezers combined with Raman spectroscopy - to capture and chemically identify individual small microplastic and nanoplastic particles in seawater samples in situ. This novel technique could enable real-time identification of the smallest plastic particles in marine environments, filling a critical gap in nano- and micro-plastic detection.
Detection and analysis of microplastics in the subtropical ocean of Okinawa using micro-Raman Optical Tweezers
Micro-Raman optical tweezers were used to isolate and identify individual microplastic particles from seawater samples collected off Okinawa, demonstrating that this single-particle technique can characterize polymer composition of very small particles that are difficult to detect with conventional methods.
Investigation of single sea microplastics by optical and Raman tweezers
Researchers investigated individual seawater microplastic particles using optical and Raman tweezers, applying laser-based trapping techniques to enable contactless manipulation and chemical characterization of single microplastic particles collected directly from the marine environment.
Investigation of single sea microplastics by optical and Raman tweezers
Researchers investigated individual seawater microplastic particles using optical and Raman tweezers, applying laser-based trapping techniques to enable contactless manipulation and chemical characterization of single microplastic particles collected directly from the marine environment.
Optical trapping studies of irregularly shaped microplastic particles
Researchers used optical tweezers coupled with Raman spectroscopy to characterize the trapping behavior of irregularly shaped microplastic particles from household plastics (PP, PET, HDPE) and beach-collected samples, building a database revealing how shape, composition, and size influence trapping stability.
Characterization 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.
Microplastic identification using Raman microsocpy
Researchers developed and implemented a Raman spectroscopy system for rapid detection and identification of microplastic particles on substrates. The system enables efficient chemical characterization of microplastics found across diverse environmental matrices including ocean, lakes, soil, beach sediment, and human blood.
Optical and Raman tweezers for the manipulation and characterization of cosmic dust and sea microplastics
Researchers used optical and Raman laser tweezers to manipulate and identify individual micro- and nanoplastic particles and cosmic dust grains. The technique can characterize particle composition and fragmentation behavior, offering a powerful new approach for studying how microplastics break down in the ocean.
Optical Extraction of Single Microplastics Followed by Online Molecular and Elemental Characterization
A new three-part instrument was built that uses an optical laser trap to isolate individual microplastic particles from complex samples, then identifies the polymer type using Raman spectroscopy and measures the particle's carbon mass using mass spectrometry. This advance allows much smaller microplastics to be detected and identified in difficult environmental matrices like soil or high-carbon water, improving the precision of contamination assessments.
Nanoplastic Analysis by Online Coupling of Raman Microscopy and Field-Flow Fractionation Enabled by Optical Tweezers
Researchers developed a new analytical technique for detecting nanoplastics by combining field-flow fractionation with online Raman microspectroscopy, using optical tweezers to trap particles and overcome weak scattering signals. The method successfully identified polymer and inorganic particles ranging from 200 nm to 5 micrometers at concentrations around 1 mg/L.
Raman Spectroscopy and Machine Learning for Microplastics Identification and Classification in Water Environments
Researchers combined Raman spectroscopy with machine learning algorithms for automated identification and classification of microplastics in water environments, achieving high accuracy in distinguishing different polymer types based on spectral fingerprints.
Identification of Microplastics Using a Custom Built Micro-Raman Spectrometer
Researchers built a custom micro-Raman spectrometer and demonstrated its use for identifying microplastic polymer types in environmental samples, achieving sensitive and specific polymer identification at particle sizes down to a few micrometers.
A semi-automated Raman micro-spectroscopy method for morphological and chemical characterizations of microplastic litter
Researchers developed a semi-automated Raman micro-spectroscopy method coupled with static image analysis for characterizing microplastics, achieving morphological and chemical identification of over 1,000 particles in under three hours, with polyethylene, polystyrene, and polypropylene as the dominant types in the environmental sample.
Development of automated microplastic identification workflow for Raman micro-imaging and evaluation of the uncertainties during micro-imaging
Researchers developed an automated identification workflow for Raman micro-imaging of microplastics, validating it with artificial samples of known polymer microspheres and showing that the workflow reliably identifies plastic type and estimates particle size across a range of sizes.
Identification of microplastics using Raman spectroscopy: Latest developments and future prospects
This review summarizes the latest advances in using Raman spectroscopy to identify microplastics in environmental samples, highlighting improvements in speed, sensitivity, and the ability to characterize plastic type and surface chemistry.
Microfluidics-based electrophoretic capture and Raman analysis of micro/nanoplastics
Researchers developed a microfluidics-based electrophoretic capture system combined with Raman spectroscopy analysis to detect and characterize micro- and nanoplastics from aquatic ecosystems, exploiting differences in polymer composition to improve identification accuracy.
Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning
Researchers combined highly reflective substrates with machine learning to accurately identify individual nanoplastic particles using Raman spectroscopy, a technique that traditionally struggles with particles this small. Their approach achieved over 97 percent accuracy in distinguishing between different types of nanoplastics including polystyrene, polymethyl methacrylate, and polyethylene. The method represents a significant advance in the ability to detect and monitor nanoplastic pollution at the individual particle level.
Study on Rapid Recognition of Marine Microplastics Based on Raman Spectroscopy
Researchers developed a rapid identification system for marine microplastics using Raman spectroscopy, enabling quick determination of plastic type and size. Fast, accurate identification tools are critical for monitoring the growing problem of microplastic pollution in ocean environments.
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.
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.
Plasmonic nanostructures for environmental monitoring and/or biological applications
This study used optical tweezer micro-Raman spectroscopy to identify and size-classify microplastics from a Chinese lake, and developed a plasmonic nanostructure system for detecting nanoplastics. Better detection tools for both micro- and nano-scale plastic particles are essential for accurately assessing environmental contamination and human exposure.
Identification of microplastics using a convolutional neural network based on micro-Raman spectroscopy
Researchers combined micro-Raman spectroscopy with a neural network to identify microplastics, achieving over 99% accuracy across 10 different plastic types. The system was also tested on real environmental samples and performed well at classifying unknown particles. This AI-powered approach could make microplastic identification faster and more reliable for environmental monitoring.
Flow Plastometry of Microplastics Using Optical Line Tweezers
Researchers developed a novel system using Raman spectroscopy combined with optical line tweezers to simultaneously analyze the shape and chemical composition of microplastics flowing through a channel. The technique can capture and characterize particles as small as 500 nanometers, offering a potential tool for real-time monitoring of microplastics in water environments.
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.