Ultrathin Leaf-Shaped CuO Nanosheets Based Sensor Device for Enhanced Hydrogen Sulfide Gas Sensing Application

New Dynamic Fingerprint in Derivative-Based Phase Space: Rapid Gas Sensing in Seconds

Many studies have focused on smart electronic noses combining machine learning and gas sensor arrays, but feature extraction for training has generally relied on dimensionality reduction techniques based on raw time-series data. These methods do not reflect the principles of sensor responses, limiting their applicability in diverse gas environments. In this study, we propose a new phase space, expressed through the first and second derivatives of dynamic response signals, to effectively characterize the nonlinear responses between gas sensors and gases. Sensing data transformed into a phase space showed unique patterns depending on the type and concentration of gases, and these were investigated for alkanes with various chain lengths (CH4, C3H8, C4H10). By applying these patterns as a preprocessing method, CNN-based gas identification machine learning achieved a high classification accuracy of 99.1% and a low concentration prediction error of 2.23 ppm using only a single sensor. Additionally, the algorithm was trained and validated across various regions of the phase space, identifying the minimum time and region required for simultaneous gas classification and concentration prediction. This study presents a novel strategy for the fast and accurate identification of gases within seconds and is expected to have significant scalability in diverse gas environments.

p-CuO/n-ZnO Heterojunction Structure for the Selective Detection of Hydrogen Sulphide and Sulphur Dioxide Gases: A Theoretical Approach

DFT calculations at the B3LYP/LanL2DZ level of theory were utilized to investigate the adsorption of H2S and SO2 gases on the electronic properties of CuO-ZnO heterojunction structures. The results were demonstrated from the standpoint of adsorption energies (Eads), the density of states (DOS), and NBO atomic charges. The obtained values of the adsorption energies indicated the chemisorption of the investigated gases on CuO-ZnO heterojunction. The adsorption of H2S and SO2 gases reduced the HOMO-LUMO gap in the Cu2Zn10O12 cluster by 4.98% and 43.02%, respectively. This reveals that the Cu2Zn10O12 cluster is more sensitive to the H2S gas than the SO2 gas. The Eads values for SO2 and H2S were −2.64 and −1.58 eV, respectively. Therefore, the Cu2Zn10O12 cluster exhibits a higher and faster response-recovery time to H2S than SO2. Accordingly, our results revealed that CuO-ZnO heterojunction structures are promising candidates for H2S- and SO2-sensing applications.