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Papers
61,005 resultsShowing papers similar to High-throughput Raman platform for microplastics detection on filtration membranes
ClearRapid MicroplasticDetection Using High-ThroughputScreening Raman Spectroscopy
Researchers developed a high-throughput screening Raman spectroscopy system for rapid microplastic detection, overcoming the traditional tradeoff between spatial resolution, field of view, and analytical throughput to enable faster identification of plastic particles across environmental samples with low concentrations.
Rapid Microplastic Detection Using High-Throughput Screening Raman Spectroscopy
Researchers developed a high-throughput Raman spectroscopy platform combining a 3.15 × 2.10 mm field of view with 1.4 µm spatial resolution for rapid label-free detection of microplastics. The system integrates automated particle recognition, autofocus correction, and spectral acquisition, significantly reducing analysis time compared to conventional micro-Raman approaches.
Rapid identification of micro and nanoplastics by line scan Raman micro-spectroscopy
Researchers developed a faster Raman spectroscopy tool for identifying microplastic particles by scanning a line rather than a single point at a time, improving imaging speed by 10 to 100 times over conventional methods. This allows the same chemical identification and size characterization of microplastics across large sample areas in a fraction of the time. Faster analysis methods are critical for processing the large numbers of samples needed in environmental monitoring programs.
Engineered Approaches to Facile Identification of Tiny Microplastics in Polymeric and Ceramic Membrane Filtrations for Wastewater Treatment
Researchers developed engineered approaches using Raman spectroscopy and automated image analysis to identify tiny microplastics retained in polymeric and ceramic membranes from wastewater treatment, enabling more precise assessment of membrane performance and microplastic removal efficiency.
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.
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.
Fast compressive Raman micro-spectroscopy to image and classify microplastics from natural marine environment
Researchers developed a fast compressive Raman micro-spectroscopy system for imaging and classifying microplastics on filters, achieving significant speed improvements over conventional point-scanning Raman methods. The system correctly identified polymer types in heterogeneous real-world samples, offering a practical tool for routine microplastic monitoring in water and sediment samples.
Fast microplastics identification with stimulated Raman scattering microscopy
Stimulated Raman scattering microscopy was applied to rapidly identify and image microplastic particles in complex environmental samples at speeds dramatically faster than conventional Raman spectroscopy. The technique has potential to enable high-throughput microplastic analysis that could make large-scale environmental monitoring more feasible.
Development of a low-cost Raman spectroscopy platform for high-throughput analysis
Researchers developed a low-cost, high-throughput Raman spectroscopy platform combining a CNC stage, USB microscope, and Raman probe for automated microplastic analysis, validating the system by scanning polypropylene microplastics scattered on glass filters across defined sample areas.
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.
Raman spectroscopy: Recent advances in fast and reliable microplastic analysis
This review summarized recent advances in Raman spectroscopy for fast and reliable microplastic identification, covering improvements in speed, sensitivity, and automation that are making the technique more practical for routine environmental monitoring. Raman-based methods are increasingly able to identify microplastics in complex environmental matrices including biological tissues.
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.
Feasibility study for simple on-line Raman spectroscopic detection of microplastic particles in water using perfluorocarbon as a particle-capturing medium
Researchers developed a simplified Raman spectroscopy setup using an oil-based medium to capture and identify microplastic particles directly from water. The approach offers a cost-effective, on-line method for detecting microplastic contamination without the need for complex filtration equipment.
Fast Detection and Classification of Microplastics by a Wide-Field Fourier Transform Raman Microscope
Researchers developed a new wide-field Raman microscope that can rapidly detect and identify microplastic particles with high spatial and chemical accuracy. The instrument can image a large sample area in about 15 minutes and identify particles down to roughly one micrometer in size. The technology was validated on microplastics from seawater and biological samples, offering a faster alternative to existing detection methods.
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.
Development of a machine learning-based method for the analysis of microplastics in environmental samples using µ-Raman spectroscopy
Researchers developed a machine learning system to identify microplastics in environmental samples using Raman spectroscopy — a technique that identifies materials by how they scatter light — training it on over 64,000 spectra and achieving recall above 99% and precision above 97%. Combining the AI with human review reduced analysis time from several hours to under one hour per sample, making microplastic monitoring far more practical at scale.
Fast Detection andClassification of Microplasticsby a Wide-Field Fourier Transform Raman Microscope
Researchers developed a wide-field hyperspectral Fourier transform Raman microscope for rapid detection and classification of microplastics extracted from environmental matrices. The instrument achieved high spatial resolution and chemical specificity across a large field of view, enabling faster throughput for microplastic identification compared to conventional point-scanning Raman approaches.
Fast Detection and Classification of Microplastics below 10 μm Using CNN with Raman Spectroscopy
Researchers combined artificial intelligence with Raman spectroscopy to rapidly detect and classify microplastic particles smaller than 10 micrometers -- a size range that is especially concerning because these tiny particles can penetrate human tissues. The AI-based method dramatically reduced the time needed to identify plastic types compared to traditional approaches, making it more practical to monitor the smallest and most potentially harmful microplastics.
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.
Trapping and chemical characterization of sub-microplastics using Raman optical tweezers with machine learning
Researchers combined optical tweezers Raman spectroscopy with machine learning-driven PCA to identify and classify individual sub-micron plastic particles (PE, PP, PS) from environmental matrices. By analyzing 35 Raman spectra, the platform demonstrated accurate single-particle identification without complex sample preparation.
Visualization and characterisation of microplastics in aquatic environment using a home-built micro-Raman spectroscopic set up
Researchers built an affordable micro-Raman spectroscopy system capable of identifying microplastics in water samples, offering a low-cost alternative to expensive commercial equipment. The system could visualize, measure, and chemically identify different types of microplastic particles. This kind of accessible detection technology is important, especially for developing countries, because widespread monitoring of microplastic pollution in water sources is essential for protecting public health.
Improved Reliability of Raman Spectroscopic Imaging of Low-Micrometer Microplastic Mixtures in Lake Water by Fractionated Membrane Filtration
Researchers developed an analytical method coupling fractionated membrane filtration with Raman microspectroscopy to reliably quantify low-micrometer microplastics (1-10 micrometers) in lake water, achieving over 90% recovery of polystyrene and PMMA particles and improving image quality by separating particles into distinct size fractions.
Raman Spectroscopy Enhanced By Machine Learning For Effective Microplastic Detection In Aquatic Systems
Researchers explored combining Raman spectroscopy with machine learning techniques to improve microplastic detection and classification in aquatic systems. The study found that deep learning models, particularly convolutional neural networks, achieved high classification accuracy and significantly reduced reliance on labor-intensive manual spectral analysis for real-time environmental monitoring.
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.