High-Confidence Aptapipette Iontronic Sensor for Analysis of Environmental Polystyrene Nanoplastics with Machine Learning-Assisted Ionic Current Rectification

The pervasive accumulation of nanoplastics in ecosystems poses significant threats due to their bioaccumulation potential and ecotoxicity. Conventional detection methods suffer from complex pretreatment and limited on-site applicability. Here, we develop a DNA aptamer-functionalized borosilicate nanopipette, defined as aptapipette, for label-free detection of polystyrene (PS) nanoplastics by leveraging synergistic electrostatic interactions and steric hindrance effects. Stepwise modifications, including silicon nanowires/amination/aptamer, enable specific binding to PS nanoplastics, amplifying ionic current rectification (ICR) through enhanced surface charge density. The iontronic sensor achieves an ultralow detection limit down to 3.3 μg/L for aged PS nanoplastics with high selectivity and robustness. Impressively, a support vector machine-assisted method is integrated to decode ICR signals, establishing a quantitative contamination assessment model. This approach achieves 96.7% confidence in distinguishing pollution levels via principal component analysis and receiver operating characteristic curve (area under the curve = 0.998), transforming raw data into actionable environmental risk insights. This work integrates aptamer-based specific recognition, nanopipette-enabled iontronic sensing, and machine learning-assisted signal decoding, providing a promising tool for environmental nanoplastics monitoring and pollution stratification.