We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Automated rapid & intelligent microplastics mapping by FTIR microscopy: A Python–based workflow
AI summary Read the abstract
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.
The analysis of environmental microplastic particles using FTIR microscopy is a challenging task, due to the very high number of individual particles within a single sample. Therefore, automatable, fast and robust approaches are highly requested. Micro particles were commonly enriched on filters, and sub- or the whole filter area was investigated, which took more than 20h and produced millions of data, which had to be evaluated. This paper presents a new approach of such filter area analysis using an intelligent algorithm to measure only those spots on a filter that would produce evaluable FTIR data. Empty spaces or IR absorbers like carbon black particles were not measured which successfully reduced the total analysis time from 50h to 7h. The presented method is based on system independent Python workflow and can easily be implemented on other FTIR systems. •••.
More Papers Like This
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.
An automated approach for microplastics analysis using focal plane array (FPA) FTIR microscopy and image analysis
AI summary Read the abstract
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.
Automated analysis of microplastics in the ocean
AI summary Read the abstract
Researchers developed an automated system for analyzing microplastics in ocean water samples using micro-FTIR microscopy and machine learning-assisted data interpretation, greatly reducing the manual analysis time required for large-scale monitoring of ocean plastic pollution.
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.
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.
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.