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Papers
61,005 resultsShowing papers similar to Raman Microspectroscopy: Improvement in Signal Generation and Collection to Facilitate Raman Spectroscopy
ClearIdentification 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.
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.
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.
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.
Design of a confocal micro-Raman spectroscopy system and research on microplastics detection
Researchers built a custom confocal micro-Raman spectroscopy system designed to detect microplastics more cost-effectively than commercial instruments. The improved signal quality enables more accurate identification of plastic polymer types in environmental samples.
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.
Rapid 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.
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.
A review of recent progress in the application of Raman spectroscopy and SERS detection of microplastics and derivatives
This review covers advances in using Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) to detect and identify microplastics in the environment. These techniques offer high resolution and sensitive detection that can identify specific plastic types even at very small sizes. Better detection methods are essential for understanding the true extent of microplastic contamination and its potential risks to human health.
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.
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.
How to Identify and Quantify Microplastics and Nanoplastics Using Raman Imaging?
This paper reviews advances in Raman imaging as a method for identifying and quantifying microplastics and nanoplastics in environmental samples, discussing current protocols, analytical challenges, and the need for standardization.
Raman spectroscopy: Recent advances in fast and reliable microplastic analysis
This review covered recent advances in Raman spectroscopy for fast and reliable microplastic analysis, with emphasis on its ability to characterize small particles that can penetrate living tissues. The authors highlight improvements in speed and sensitivity that are making Raman techniques more practical for environmental monitoring.
Applications of Raman spectroscopy for microplastic detection and characterization: a comprehensive spectral reference
This review evaluates Raman spectroscopy as a tool for detecting and identifying microplastics across water, soil, air, and biological samples. The study consolidates reference spectra for common plastic polymers and discusses recent innovations like surface-enhanced Raman techniques that improve detection sensitivity, while also addressing challenges like fluorescence interference in complex samples.
Raman Imaging Spectroscopy: History, Fundamentals and Current Scenario of the Technique
This review covers the history and principles of Raman imaging spectroscopy, a technique increasingly used to identify and map the chemical composition of microplastics in environmental samples. The review provides technical context for one of the most important tools in microplastic analysis.
Detection of microplastics based on spatial heterodyne Raman spectroscopy
Researchers developed a spatial heterodyne Raman spectroscopy method for detecting microplastics, offering advantages over existing techniques by reducing detection time, lowering false detection rates, and using more affordable equipment.
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.
Machine learning assisted Raman spectroscopy: A viable approach for the detection of microplastics
This review covers how machine learning combined with Raman spectroscopy can improve the detection and identification of microplastics in environmental samples. Traditional detection methods are slow and have limitations in resolution and particle size analysis, but AI algorithms can process spectral data more quickly and accurately. Better detection tools are essential for understanding the true scale of microplastic contamination in our water, food, and environment.
The assessment of particle selection and blank correction to enhance the analysis of microplastics with Raman microspectroscopy
Researchers improved the efficiency of Raman microspectroscopy analysis for microplastics by implementing automated particle selection and optimized blank correction methods, significantly reducing analysis time while maintaining accuracy for complex environmental samples.
Flow Raman Spectroscopy for the Detection and Identification of Small Microplastics
Researchers developed a new method using flow Raman spectroscopy to detect and identify individual microplastic particles as small as 4 micrometers while they move through water. Unlike current methods that require complex sample preparation, this technique could work in real time for monitoring food and drinking water quality. The method can distinguish between different plastic types even after they have been weathered by the environment.
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.
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.
Advanced microplastic monitoring using Raman spectroscopy with a combination of nanostructure-based substrates
Researchers reviewed advances in Raman spectroscopy and surface-enhanced Raman scattering (SERS) — a technique that amplifies light signals using metallic nanostructures — for detecting micro- and nanoplastics at trace concentrations in environmental samples, highlighting new plasmonic materials, 3D substrates, and microfluidic chip platforms that enable on-site monitoring.
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.