Anti-interference detection of mixed NOX via In2O3-based sensor array combining with neural network model at room temperature

Herein, we report a noble metal doped In2O3-based sensor array applying back propagation neural network (BPNN) combined with whale optimization algorithm (WOA) toward anti-interference detection of mixed NOX. The synthesis (simple hydrothermal methods) and characterization (XRD, SEM, EDS and XPS) of Pt, Au and Pd doped In2O3 with different morphologies were reported. The three of In2O3-based sensors were systematically tested at room temperature to investigate the performance of sensitivity, response-recovery time, repeatability and selectivity to NO and NOX. Based on the sensor array composed of Pt, Au, and Pd doped In2O3 sensors combining WOA-BPNN prediction model, this work finally achieved the quantitative prediction of the components in the mixed NO and NOX under the influence of cross interference. The topic of this paper is to develop an In2O3-based gas sensor array for monitoring NOX. It is well known that NOX is a class of highly toxic and hazardous gases to human health and environmental safety, and therefore the strict control and accurate detection of NOX emissions have significant implication on the environment. In this paper, a sensor array combined with neural network modeling for anti-interference detection of mixed NOX at room temperature is proposed, which provides a new idea for monitoring mixed NOX gases and thus has a significant impact on the environment.