Room-Temperature Sub-ppm Detection and Machine Learning-Based High-Accuracy Selective Analysis of Ammonia Gas Using Silicon Vertical Microwire Arrays

Abstract

The potential applications of silicon microwire materials in monitoring gases have not been fully exploited. Uniform silicon vertical microwire arrays (Si VMWA) are fabricated using a metal-assisted chemical etching process after optimizing the conditions. The characteristics and responses of Si VMWA-based sensors with different diameters to ammonia gas (NH3) are investigated in both air and nitrogen environments. The sensing mechanism of the sensor to NH3 is discussed to clarify the response in different environments. The sensor exhibits a linear response to a wide range of NH3 concentrations (4%@2 ppm-122%@500 ppm) at room temperature and even shows a distinct response at 200 ppb of NH3. In addition, it demonstrates great repeatability/reversibility and moderate selectivity to ammonia gas against other gases (nitrogen dioxide, toluene, and isobutane). Furthermore, machine learning-based principal component analysis and random forest algorithms enable us to discriminate NH3 from other possible interfering gases and predict gas concentration with an accuracy of over 95%. Thus, our approach using the Si VMWA-based sensor with machine learning-based data analysis represents a significant step toward the environmental sensing of specific chemical analytes in the household and industries.