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61,005 resultsShowing papers similar to Laser-based spectroscopic techniques: A novel approach for distinguishing aging processes and types of microplastics
ClearLaser-based spectroscopic techniques: A novel approach for distinguishing aging processes and types of microplastics
Researchers applied laser-based spectroscopic techniques as a novel approach to distinguish different aging processes and plastic types in microplastics, examining how biotic and abiotic degradation factors alter spectral signatures across particles ranging from 1 to 1000 microns.
Laser-based techniques: Novel tools for the identification and characterization of aged microplastics with developed biofilm
Researchers applied laser-based analytical techniques including Raman and LIBS spectroscopy to detect and characterize microplastics covered with environmental biofilm. The methods successfully identified five polymer types under real-world conditions without requiring biofilm removal, avoiding the particle loss associated with conventional pre-treatment steps.
Laser-Induced Breakdown Spectroscopy for direct analysis of pristine and environmentally aged microplastics: A PCA-based approach
Researchers combined a rapid laser analysis technique (LIBS) with statistical pattern recognition to distinguish between fresh and environmentally aged microplastics made of polystyrene, polyethylene, and PVC. They found that aging — especially biological aging with microbe growth — left distinct chemical fingerprints on particle surfaces, offering a faster way to monitor how microplastics change as they degrade in the environment.
Identifying microplastic litter with Laser Induced Breakdown Spectroscopy: A first approach
Researchers demonstrated that Laser Induced Breakdown Spectroscopy (LIBS) can identify microplastic particles by their spectral fingerprints, offering a first approach to a rapid analytical technique for distinguishing plastic litter types.
A critical comparison of the main characterization techniques for microplastics identification in an accelerated aging laboratory experiment
This paper critically compared the main spectroscopic and microscopic characterization techniques used to identify microplastics in environmental samples, evaluating their strengths, limitations, and suitability for different matrices. The review highlighted the need for standardized methods to improve comparability across microplastic studies.
Optimizing microplastic analysis through comparative FTIR and raman spectroscopy: Addressing challenges in environmental degradation studies
This study optimized microplastic analysis by comparing FTIR and Raman spectroscopy approaches for identifying degraded polymer particles in environmental samples where photooxidation and mechanical fragmentation have altered spectral signatures. A combined spectroscopy approach outperformed either technique alone for accurately identifying degraded microplastics in complex environmental matrices.
Online in situ detection of atmospheric microplastics based on laser-induced breakdown spectroscopy
Researchers developed a laser-based detection system combined with machine learning that can identify and classify different types of microplastics in the air in real time. The system achieved high accuracy in distinguishing between common plastic types like polyethylene, polystyrene, and PVC. Better tools for monitoring airborne microplastics are important because people inhale these particles daily, and understanding what types are present in the air is the first step toward assessing respiratory health risks.
Identification of marine microplastics based on laser-induced fluorescence and principal component analysis
Researchers developed a method to identify different types of marine microplastics using laser-induced fluorescence combined with principal component analysis. The technique successfully distinguished nine types of microplastics based on their fluorescence signatures and could detect microplastic concentrations as low as 0.03% by mass. The study suggests this approach could be a practical tool for rapid microplastic identification in marine environments.
A comparison of microscopic and spectroscopic identification methods for analysis of microplastics in environmental samples
Researchers compared microscopic and spectroscopic methods for analyzing microplastics in environmental samples, evaluating accuracy and efficiency and finding that spectroscopic confirmation substantially reduces misidentification errors.
Optimizing microplastic analysis through comparative FTIR and raman spectroscopy: Addressing challenges in environmental degradation studies
Researchers compared FTIR and Raman spectroscopy for analyzing degraded microplastic polymers in environmental samples, evaluating how polymer aging affects identification accuracy. The study found that spectral databases based on pristine polymers can misidentify weathered microplastics, calling for updated reference libraries.
Machine Learning of polymer types from the spectral signature of Raman spectroscopy microplastics data
Researchers applied machine learning to Raman spectroscopy data to classify microplastic polymer types, finding the approach particularly valuable for identifying environmentally weathered particles that are harder to analyze with standard methods. Machine learning tools could improve the speed and accuracy of microplastic identification in environmental monitoring.
Impact of weathering on the chemical identification of microplastics from usual packaging polymers in the marine environment
The impact of environmental weathering on the chemical identification of common microplastics was investigated, examining how UV radiation, mechanical abrasion, and microbial activity alter the spectroscopic signatures used for polymer identification. Weathered plastics were harder to correctly identify than pristine ones, highlighting the need for reference libraries that include aged material.
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.
Quantification and characterization of microplastics in coastal environments: Insights from laser direct infrared imaging
Researchers used laser direct infrared imaging to identify and quantify microplastics in sediment and seawater samples from coastal areas in Auckland, New Zealand. The study detected nine common plastic polymer types and demonstrated that this analytical technique provides efficient and accurate characterization of microplastic contamination in environmental samples.
Microplastic aging processes: Environmental relevance and analytical implications
Researchers reviewed how microplastics change physically and chemically over time in the environment — a process called 'aging' — and found that standard lab methods for detecting microplastics were mostly developed using fresh, unaged plastics, making it harder to accurately measure real-world contamination. Improved analytical methods that account for aged microplastics are needed for reliable environmental assessment.
Machine Learning of polymer types from the spectral signature of Raman spectroscopy microplastics data
Machine learning models were applied to Raman spectroscopy data to improve polymer type identification in environmentally weathered microplastics, which are harder to classify than pristine samples. The approach achieved better accuracy by accounting for spectral changes caused by UV exposure and physical degradation.
Identification of 20 polymer types by means of laser-induced breakdown spectroscopy (LIBS) and chemometrics
Researchers developed a laser-based identification technique that can distinguish among 20 different types of plastic using chemical analysis and machine learning, even in colored or additive-containing samples — a higher number than any previously published method. Rapid and reliable plastic identification is a critical step for improving plastic waste sorting and understanding the composition of environmental microplastic pollution.
Fast identification of microplastics in complex environmental samples by a thermal degradation method
Researchers developed a fast identification method for microplastics in complex environmental samples using thermal analysis, offering a high-throughput alternative to spectroscopic techniques for polymer identification.
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.
Laser ablation-based techniques for microplastic analysis
This review examined laser ablation-based analytical techniques for detecting, characterizing, and quantifying microplastics in environmental and biological samples. The methods offer high spatial resolution and material specificity, making them valuable tools for advancing microplastic research.
Analytical Techniques for the Detection and Characterization of Microplastics: an Overview
This overview reviews state-of-the-art analytical methods for identifying and characterizing microplastics, covering spectroscopic and microscopic approaches and their strengths and limitations for detecting plastic particles across environmental compartments including water, soil, and biological samples.
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.
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.
Identifying plastics with photoluminescence spectroscopy and machine learning
Researchers showed that combining photoluminescence spectroscopy (shining light on plastic and measuring what comes back) with machine learning can reliably identify different types of plastic materials. This low-cost, widely accessible approach could help scientists track and characterize plastic pollution in the environment at a global scale.