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20 resultsShowing papers similar to Raman spectroscopy: Recent advances in fast and reliable microplastic analysis
ClearRaman 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.
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.
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.
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.
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.
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.
Raman Microspectroscopy: Improvement in Signal Generation and Collection to Facilitate Raman Spectroscopy
Advances in Raman microspectroscopy were reviewed for improving signal generation and analysis in microplastic identification, including new detector designs and data processing algorithms. Enhanced Raman spectroscopy capabilities enable faster and more accurate polymer identification at smaller particle sizes.
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.
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.
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.
Fluorescence-Guided Raman Spectroscopy with an Integrated Adapter for Faster and Cost-Effective Microplastic Detection
A fluorescence-guided Raman spectroscopy system with integrated adaptive optics was developed to improve detection of microplastics in complex environmental matrices. The instrument advances the sensitivity and speed of microplastic identification, supporting more thorough environmental monitoring.
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.
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.
A Review of Spectroscopic Techniques used for the Quantification and Classification of Microplastics and Nanoplastics in the Environment
This review evaluates spectroscopic techniques — including Raman, FTIR, NIR, ICP-MS, fluorescence, X-ray, and NMR — for identifying and quantifying microplastics and nanoplastics in environmental and biological matrices, covering methodologies, sample handling, and applications.
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.
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.
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.
A comparison of spectroscopic analysis methods for microplastics: Manual, semi-automated, and automated Fourier transform infrared and Raman techniques
Researchers compared manual, semi-automated, and fully automated methods for identifying microplastics using FTIR and Raman spectroscopy. They found that the semi-automated approach was the best balance of accuracy and efficiency, detecting 22% more microplastic particles than manual analysis while taking less time. The fully automated method was fastest but had an 80% false positive rate, while Raman microscopy was better for very small particles but took nine times longer.