We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
An Open-Set Raman Spectroscopy Framework for Rapid Profiling of Microplastic Types and Aging States in Complex Seawater.
Summary
Scientists developed a fast, AI-powered laser scanning tool that can identify tiny plastic fragments in ocean water and tell how long they've been breaking down, with over 90% accuracy, even amid other ocean debris. This matters because microplastics are already working their way up the food chain into the fish we eat, and knowing what types are out there and how degraded they are helps researchers better understand the risks they pose to our health.
Microplastics (MPs), as ubiquitous environmental pollutants, pose significant ecotoxicological risks due to their accumulation in marine food chains and role as contaminant carriers. However, their rapid identification and aging assessment within complex marine matrices remain challenging. We established a Raman spectral dataset encompassing common polymer types across multiple artificial and natural aging stages. Using this data set, an attentional neural network (aNN) trained under controlled laboratory conditions achieved high-precision classification of polymer types and aging states of natural aging MPs in seawater, reaching over 97% and 93% accuracy for polymer and aging state identification. To reduce interference from complex environmental matrices during MP identification in real seawater, an open-set deep learning (OSDL) framework was employed. This approach achieved an average identification accuracy of 94% for naturally aged MPs and 97% for nontarget particles and impurities in seawater, outperforming conventional closed-set algorithms, while field-based validation using real microplastic fragments from coastal seawater yielded 90% accuracy. Collectively, these results demonstrate that integrating Raman spectroscopy with the OSDL algorithm establishes a foundational approach for marine MP identification and aging characterization under semicontrolled conditions. Meanwhile, further development is required for diverse polymer formulations and geographic settings to enhance the universality of the approach.