Abstract

Triethylamine, an extensively used material in industrial organic synthesis, is hazardous to the human respiratory and nervous systems, but its accurate detection and prediction has been a long-standing challenge. Herein, a machine learning-motivated chemiresistive sensor that can predict ppm-level triethylamine is designed. The zero-dimensional (0D) bismuth vanadate (BiVO₄) nanoparticles were anchored on the surface of three-dimensional (3D) tungsten oxide (WO₃) architectures to form hierarchical BiVO₄/WO₃ heterostructures, which demonstrates remarkable triethylamine-sensing performance such as high response of 21 (4 times higher than pristine WO₃) at optimal temperature of 190?°C, low detection limit of 57?ppb, long-term stability, reproducibility and good anti-interference property. Furthermore, an intelligent framework with good visibility was developed to identify ppm-level triethylamine and predict its definite concentration. Using feature parameters extracted from the sensor responses, the machine learning-based classifier provides a decision boundary with 92.3?% accuracy, and the prediction of unknown gas concentration was successfully achieved by linear regression model after training a series of as-known concentrations. This work not only provides a fundamental understanding of BiVO₄-based heterostructures in gas sensors but also offers an intelligent strategy to identify and predict trace triethylamine under an interfering atmosphere.