Enhanced Room Temperature Sensing Properties of Tin Oxide Gas Sensors Exploiting Carbon Nanotubes: High-Accuracy Ammonia Gas Classification via Supervised Learning Regression Algorithms.

The sensing properties of tin oxide (SnO) gas sensors, enhanced by the exploitation of carbon nanotubes (CNTs), were explored at room temperature. The CNT/tin oxide hybrid sensors demonstrated superior performance at room temperature compared to single-material sensors, particularly, showing a high response to ammonia gas. A sensor array was utilized for gas classification tests using PCA and various supervised learning regression algorithms. Results indicated that the CNTs/tin oxide hybrid sensors significantly outperformed the CNT sensor, offering lower detection limits and higher classification accuracy, making them highly suitable for practical ammonia gas monitoring applications. These findings indicate the high potential of CNTs/tin oxide hybrid sensors for reliable and efficient gas monitoring in various environments.