Microplastics are sub-millimeter-sized fragments of plastics, which have been found in environments to a great extent. They are relatively new pollutants that are difficult to be degraded. They not only cause irreversible adverse effects on microorganisms, animals, and plants but also enter the human body through the food chain and affect human health. However, due to their small size, variety, and differences in physical and chemical properties of microplastics, traditional detection and identification still face challenges. This work provides a method for detecting and classifying microplastics in liquids using a liquid-solid triboelectric nanogenerator (LS-TENG) in combination with a deep learning model. The experiment showed that the type and content of microplastics in the liquid had a great effect on the contact electrification between the liquid and the perfluoroethylene-propylene copolymer. After adding polyethylene, polypropylene, polyvinyl chloride, polyethylene terephthalate, and polystyrene microplastics to the liquids, it was found that the type and content of different microplastics have a significant impact on the output voltage signal of the LS-TENG sensor. When the mass fraction of microplastics ranged from 0.025 to 0.25 wt %, the voltage output of the LS-TENG sensor had a linear relationship with the mass fraction of microplastics. Therefore, a method for quantitatively detecting the content of microplastics using the LS-TENG sensor has been established. Based on the LS-TENG output voltage signal, a convolutional neural network deep learning model was used to identify different types of labels, and high recognition accuracy was achieved. These are of great significance for expanding the application prospect of LS-TENG and realizing the detection of microplastics in liquids.
Keywords: deep learning; liquid−solid triboelectric nanogenerator; microplastic detection; rectangular structure; self-powered sensor.