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Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning

Environmental Science & Technology 2023 107 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 55 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.

Xuejun Ruan, Xuejun Ruan, Lifang Xie, Lifang Xie, Lifang Xie, Lifang Xie, Xuejun Ruan, Xuejun Ruan, Xuejun Ruan, Xuejun Ruan, Lifang Xie, Si-Heng Luo, Kedong Gong, Kejian Li, Lifang Xie, Yangyang Liu, Yangyang Liu, Lifang Xie, Yangyang Liu, Si-Heng Luo, Kejian Li, Liwu Zhang Xuejun Ruan, Liwu Zhang Yangyang Liu, Qiuyue Ge, Yangyang Liu, Qiuyue Ge, Xuejun Ruan, Xuejun Ruan, Qiuyue Ge, Xuejun Ruan, Liwu Zhang Lifang Xie, Xuejun Ruan, Yangyang Liu, Qiuyue Ge, Kejian Li, Xuejun Ruan, Xuejun Ruan, Xuejun Ruan, Qiuyue Ge, Qiuyue Ge, Qiuyue Ge, Liwu Zhang Liwu Zhang Kedong Gong, Yangyang Liu, Qiuyue Ge, Kedong Gong, Liwu Zhang Qiuyue Ge, Qiuyue Ge, Qiuyue Ge, Yangyang Liu, Qiuyue Ge, Yangyang Liu, Yangyang Liu, Kejian Li, Kejian Li, Kejian Li, Kedong Gong, Guokun Liu, Qiuyue Ge, Qiuyue Ge, Liwu Zhang Qiuyue Ge, Kejian Li, Kedong Gong, Qiuyue Ge, Xuejun Ruan, Xuejun Ruan, Qiuyue Ge, Liwu Zhang Yangyang Liu, Ventsislav K. Valev, Qiuyue Ge, Liwu Zhang Yangyang Liu, Liwu Zhang Yangyang Liu, Liwu Zhang Guokun Liu, Kejian Li, Ventsislav K. Valev, Liwu Zhang Liwu Zhang Liwu Zhang Lifang Xie, Ventsislav K. Valev, Kedong Gong, Lifang Xie, Xuejun Ruan, Xuejun Ruan, Ventsislav K. Valev, Liwu Zhang Liwu Zhang Ventsislav K. Valev, Liwu Zhang Liwu Zhang Ventsislav K. Valev, Liwu Zhang

Summary

Researchers combined highly reflective substrates with machine learning to accurately identify individual nanoplastic particles using Raman spectroscopy, a technique that traditionally struggles with particles this small. Their approach achieved over 97 percent accuracy in distinguishing between different types of nanoplastics including polystyrene, polymethyl methacrylate, and polyethylene. The method represents a significant advance in the ability to detect and monitor nanoplastic pollution at the individual particle level.

Polymers

PS PVC

The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics is challenging due to their small size and complex composition. In this work, we combined highly reflective substrates and machine learning to accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman spectroscopy data sets of nanoplastics, incorporated peak extraction and retention data processing, and constructed a random forest model that achieved an average accuracy of 98.8% in identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% identification accuracy, and demonstrated the applicability of our algorithm to real-world environmental samples through experiments on rainwater, detecting nanoscale polystyrene (PS) and polyvinyl chloride (PVC). Despite the challenges of processing low-quality nanoplastic Raman spectra and complex environmental samples, our study demonstrated the potential of using random forests to identify and distinguish nanoplastics from other environmental particles. Our results suggest that the combination of Raman spectroscopy and machine learning holds promise for developing effective nanoplastic particle detection and monitoring strategies.

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