Application of neural network based regression model to gas concentration analysis of TiO2 nanotube-type gas sensors

We performed a gas analysis of TiO 2 nanotube (NT)-type integrated gas sensors using a machine learning (ML) algorithm and neural network-based regression. We fabricated a TiO 2 -NT integrated gas sensor with multiple sensing elements with different response characteristics, and we measured the output signals of each sensing element exposed to a gas mixture, where the main components were nitrogen and oxygen gas with a small amount of carbon monoxide. We analyzed the output signals of the sensor elements using the ML technique to predict the concentrations of CO and O 2 , to which the TiO 2 -NT gas sensors were sensitive. Sensor output data were collected for seven sets of mixed gas concentrations with different concentrations of each component gas. Four or five of the seven datasets were used as ML training data for the neural network method, and the concentrations of CO and O 2 in the remaining three or two datasets were predicted. Consequently, we confirmed that increasing the number of sensor elements significantly improved the prediction accuracy of the gas concentration. When the output signals from 10 sensor elements were used, the gas concentration could be predicted with an accuracy of less than 0.001% for a carbon monoxide concentration of 0.02%. This accuracy was sufficient for practical application. • We performed a gas analysis of TiO 2 nanotube (NT)-based integrated gas sensors using a machine learning (ML) technique. • We fabricated a TiO2-NT integrated gas sensor with sensing elements with different response characteristics. • We measured the output signals of each sensing element exposed to a four-component gas mixture. • The gas concentration could be predicted with an accuracy of less than 0.001% for a carbon monoxide concentration of 0.02%. • Increasing the number of sensor elements significantly improved the prediction accuracy of the gas concentration.