We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Development of a rapid detection protocol for microplastics using reflectance-FTIR spectroscopic imaging and multivariate classification
AI summary Read the abstract
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.
Reflectance-FTIR spectroscopy provides opportunities for faster, more automated, and cheaper detection of microplastics in the environment.
More Papers Like This
Automated identification and quantification of microfibres and microplastics
AI summary Read the abstract
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 novel semi-automated analytical system of microplastics using reflectance-FTIR spectrometry: designed for the analysis of large microplastics
AI summary Read the abstract
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.
Robust Automatic Identification of Microplastics in Environmental Samples Using FTIR Microscopy
AI summary Read the abstract
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.
Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning
AI summary Read the abstract
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.
μ-FTIR Reflectance Spectroscopy Coupled with Multivariate Analysis: A Rapid and Robust Method for Identifying the Extent of Photodegradation on Microplastics
AI summary Read the abstract
Researchers developed a faster, more sensitive method for identifying weathered microplastics using infrared reflectance spectroscopy combined with statistical analysis. The technique can classify different plastic types and assess their level of sun damage without complex data preprocessing. The approach could improve the speed and accuracy of environmental microplastic monitoring, particularly for particles that have been altered by exposure to sunlight.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.