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Identification of Polymeric Nanoparticles Using Strategic Peptide Sensor Configurations and Machine Learning
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Researchers created a sensor system using specially designed peptides combined with machine learning to identify different types of plastic nanoparticles dispersed in water. The peptide sensors produced distinct fluorescence patterns for each polymer type, and the AI algorithms could accurately distinguish between plastics with very similar chemical structures. This approach could help fill a critical gap in environmental monitoring, since detecting nanoplastics in water remains a major challenge with current technology.
Environmental pollution by miniaturized plastics such as micro- and nanoplastics continues to escalate, posing serious risks to ecosystems and human health. Therefore, there is an urgent need to detect or identify the plastics. Although the techniques for microplastics have been advanced, those for nanoplastics remain challenging owing to the difficulty of sample collection and sensing reliability. In this study, the identification of polymeric nanoparticles dispersed in water was demonstrated using peptide sensors with a microenvironment-sensitive fluorophore. The fluorescence spectra obtained from peptide sensors were different depending on the polymer species of polymeric nanoparticles. Supervised and unsupervised machine learning on the signal patterns of fluorescence intensities obtained from the spectra successfully identified polymeric nanoparticles with slightly different chemical structures. Systematic evaluation revealed the critical role of both the number and combination of peptide sensors in achieving the precise identification of polymeric nanoparticles. Our approach offers new and foundational insights into the forthcoming identification of nanoplastics dispersed in water.
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