SnO2-Co3O4 nanocomposite sensor: Achieving ultra-selective hydrogen detection in mixed gas environments

Abstract

This paper addresses the long-standing challenge of poor selectivity in MOS sensors towards gases with similar properties, exemplified by the difficulty in distinguishing between H2 and CO in mixed environments. In this paper, n-SnO2/p-Co3O4 nanocomposites have been prepared as sensing materials by a straightforward pyrolytic bimetallic organic framework method. Optimal hydrogen detection is achieved with a Sn: Co molar ratio of 1:0.15, demonstrating a high response (S=Ra/Rg=22.75 for 300 ppm H2) and exceptional anti-interference capability against CO (SH2/SCO=11.67) at 325°C. Furthermore, the incorporation of Extreme Learning Machine (ELM) model for predicting hydrogen concentration significantly reduces the interference effect common in mixed gases, achieving a high prediction accuracy a remarkably low relative error of just 0.6 %. The mechanism behind the enhanced sensitivity and selectivity of the sensor is elucidated in the paper, offering both theoretical insight and practical solution to the issue of selectivity in MOS sensors.