We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Thermodegradation characterization of microplastics: Dispersion effect and pyrolysis kinetics by artificial intelligence
No summary available — this paper's abstract is not included in the open metadata provided by the publisher. Learn why →
More Papers Like This
A Review on the Fate of Microplastics: Their Degradation and Advanced Analytical Characterization
AI summary Read the abstract
This review covers current methods for extracting, degrading, and analytically characterizing microplastics from environmental and biological samples, highlighting major gaps in photo-oxidation, biodegradation, and photo-thermal degradation approaches. Establishing effective microplastic degradation methodologies is essential for reducing their prolonged environmental persistence and preventing ongoing bioaccumulation of plastic fragments and associated toxic chemicals in food chains.
Microplastics Detection Using Pyrolysis-GC/MS-Based Methods
AI summary Read the abstract
Researchers reviewed pyrolysis-GC/MS, a technique that burns plastic particles and identifies them by their chemical breakdown products, finding it a reliable method for confirming whether environmental particles are truly microplastics — an important step toward standardizing detection.
Microplastics Detection Using Pyrolysis-GC/MS-Based Methods
AI summary Read the abstract
Pyrolysis coupled with gas chromatography and mass spectrometry (Py-GC/MS) enables reliable identification of polymer composition in suspected microplastic samples, providing the chemical confirmation required to classify particles accurately. Robust detection methods are foundational to monitoring microplastic pollution levels and understanding human exposure through food and water.
Application of Artificial Intelligence to Improve Microplastic Identification
AI summary Read the abstract
This review examines how artificial intelligence -- including machine learning and computer vision approaches -- can improve the identification, classification, and measurement of microplastics during environmental sampling and analysis. AI-assisted identification addresses a major bottleneck in microplastic research by reducing the time and expertise required to process large sample volumes, enabling faster and more consistent data generation needed to assess human health exposure at scale.
FTIR and SEM Study on the Degradation of Microplastics
AI summary Read the abstract
Laboratory experiments using FTIR and SEM analysis showed that temperature, humidity, and UV light exposure all accelerate the surface degradation of microplastics, causing fragmentation into smaller particles and chemical changes to polymer structure. Understanding these degradation pathways matters because smaller plastic fragments and altered surface chemistry affect how microplastics interact with biological tissues and accumulate toxic compounds.
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.