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Papers
61,005 resultsShowing papers similar to High Throughput FTIR Analysis of Macro and Microplastics with Plate Readers
ClearData and Code for High Throughput FTIR Analysis of Macro and Microplastics with Plate Readers
This dataset and code support a method using FTIR plate readers to analyze large numbers of macro- and microplastic samples simultaneously. High-throughput FTIR analysis dramatically increases the speed of plastic identification compared to traditional one-sample approaches. The open-source tools make large-scale environmental microplastic monitoring more accessible.
Generation of macro- and microplastic databases by high-throughput FTIR analysis with microplate readers
Researchers developed a high-throughput FTIR analysis technique using microplate readers to rapidly characterize large microplastics and macroplastics, which have traditionally required slower manual methods. They created a reference database of over 6,000 spectra covering more than 600 plastic, organic, and mineral materials across multiple measurement modes. The study addresses key analytical bottlenecks in plastic pollution research by enabling faster, non-destructive identification of larger plastic particles.
Data and Code for High Throughput FTIR Analysis of Macro and Microplastics with Plate Readers
This is a dataset and source code repository supporting research on high-throughput FTIR spectroscopy for identifying macro and microplastics using plate readers. Providing open-access analytical tools helps researchers detect and quantify plastic pollution more efficiently across large sample sets.
Data and Code for High Throughput FTIR Analysis of Macro and Microplastics with Plate Readers
This is a dataset and source code repository supporting research on high-throughput FTIR spectroscopy for identifying macro and microplastics using plate readers. Providing open-access analytical tools helps researchers detect and quantify plastic pollution more efficiently across large sample sets.
An automated approach for microplastics analysis using focal plane array (FPA) FTIR microscopy and image analysis
Researchers developed an automated approach using focal plane array FT-IR spectroscopy for microplastic analysis, enabling faster and more comprehensive identification of particles in environmental samples with less manual effort.
Development of a novel semi-automated analytical system of microplastics using reflectance-FTIR spectrometry: designed for the analysis of large microplastics
A semi-automated reflectance-FTIR spectrometry system was developed for microplastic analysis, designed specifically for large microplastics and capable of dramatically accelerating the otherwise labor-intensive identification process while maintaining accuracy in polymer type determination.
Automated identification and quantification of microfibres and microplastics
Researchers developed an automated method using FTIR imaging data analysis to simultaneously identify and quantify both microplastics and microfibers in environmental samples. Automation improves throughput and consistency compared to manual identification, addressing a key bottleneck in large-scale microplastic monitoring.
Development of a rapid detection protocol for microplastics using reflectance-FTIR spectroscopic imaging and multivariate classification
Reflectance-FTIR spectroscopy was evaluated as a faster and more automated detection method for microplastics in environmental samples, with results showing strong potential for high-throughput screening. The method could reduce the time and cost of routine microplastic monitoring programs.
Focal plane array detector-based micro-Fourier-transform infrared imaging for the analysis of microplastics in environmental samples
Researchers developed an automated protocol using focal plane array FT-IR imaging to identify and count microplastics on filters without manual sorting, dramatically increasing throughput compared to manual methods. The approach represents an important step toward standardized, high-throughput microplastic monitoring in aquatic environments.
Robust Automatic Identification of Microplastics in Environmental Samples Using FTIR Microscopy
Researchers developed a robust automated method for identifying microplastics in environmental samples using FTIR microscopy combined with machine learning-based spectral matching, improving the consistency and efficiency of microplastic identification compared to manual evaluation.
Automated rapid & intelligent microplastics mapping by FTIR microscopy: A Python–based workflow
An algorithm was developed for automated FTIR microscopy that skips empty areas and non-plastic particles on filters, dramatically reducing scan times while maintaining accuracy. Faster automated analysis makes it practical to screen more environmental microplastic samples, improving the quality of contamination assessments.
Automated Identification and Quantification of Microplastics by FTIR Imaging and Image Analysis
This research developed an automated system using FTIR imaging and chemometric analysis to identify and count microplastic particles smaller than 500 micrometers. Automating this step addresses a major bottleneck in microplastic research, allowing for faster and more consistent analysis of environmental samples.
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.
Comparison of two rapid automated analysis tools for large FTIR microplastic datasets
Researchers compared two automated analysis tools for large FTIR microplastic datasets and found significant differences in polymer identification results, highlighting the urgent need for standardized data analysis methods in microplastic research.
Direct µ-FTIR analysis of microplastics deposited on silicon in indoor air environments
Direct micro-FTIR analysis of microplastics deposited on silicon wafers was optimized for improved detection sensitivity and throughput. The refined protocol reduces sample preparation steps and improves the accuracy of polymer identification, advancing the standardization of microplastic analysis methods.
Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning
Researchers developed machine learning approaches for automated microplastic identification in environmental samples from micro-FTIR imaging data, demonstrating improved accuracy and speed compared to traditional spectral library search methods for scalable analysis.
High-Throughput Analyses of Microplastic Samples Using Fourier Transform Infrared and Raman Spectrometry
Researchers developed GEPARD, an open-source software package that combines optical particle analysis with automated FT-IR and Raman microspectroscopy to enable high-throughput identification and characterization of microplastics. The system steers spectroscopic measurements based on optically detected particles, enabling efficient polymer typing, size distribution measurement, and color classification.
Detecting small microplastics down to 1.3 μm using large area ATR-FTIR
Researchers introduced large-area ATR-FTIR spectroscopy as a new technique capable of detecting microplastics as small as 1.3 micrometers, outperforming conventional micro-FTIR for small particle detection in marine water samples.
Automated Machine-Learning-DrivenAnalysis of Microplasticsby TGA-FTIR for Enhanced Identification and Quantification
Researchers developed an automated machine-learning-driven analysis pipeline for characterizing microplastics using thermogravimetric analysis coupled with FTIR, achieving rapid polymer identification and quantification that could enable high-throughput environmental monitoring.
A New Chemometric Approach for Automatic Identification of Microplastics from Environmental Compartments Based on FT-IR Spectroscopy
Researchers developed a new chemometric approach for automatic identification of microplastics from environmental samples, designed to handle the challenges of biofilm contamination and surface aging that typically impede standard spectroscopic characterisation methods.
Classification and Quantification of Microplastics (<100 μm) Using a Focal Plane Array–Fourier Transform Infrared Imaging System and Machine Learning
Researchers developed a method using focal plane array Fourier transform infrared imaging to classify and quantify microplastics smaller than 100 micrometers. The technique allows simultaneous chemical identification and size measurement of individual particles across a filter sample, significantly improving throughput compared to manual analysis. The study demonstrates that automated spectroscopic imaging can reliably detect and categorize very small microplastics that are often missed by conventional methods.
A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea
Researchers developed a machine learning method to automatically identify the chemical composition of microplastics from FTIR spectroscopy data collected during the Tara Mediterranean expedition. The algorithm performed well for common polymers like polyethylene and was applied to classify over 4,000 unidentified microplastic spectra. The study demonstrates that automated identification tools can significantly speed up large-scale microplastic pollution surveys while maintaining acceptable accuracy.
Reference database design for the automated analysis of microplastic samples based on Fourier transform infrared (FTIR) spectroscopy
A reference database for automated FTIR-based microplastic identification was developed using hierarchical cluster analysis of reference spectra, enabling both single-particle identification and chemical imaging analysis. The database design improves the reproducibility and comparability of automated microplastic identification across different laboratories and instrumentation types.
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.