Optimizing ethanol gas sensor with rGO layer addition on LaFeO3–Pd doped material using density functional theory

Abstract

LaFeO₃ is widely applied in ethanol gas sensors due to material composition and the stability of its sensing parameters. However, LaFeO₃ is still limited in gas sensing properties, necessitating doping to enhance the performance of gas sensors like palladium (Pd). In this study, the optimization of the ethanol gas sensor was explored by adding reduced graphene oxide (rGO) to Pd–LaFeO₃, focusing on adsorption energy and band gap energy using density functional theory (DFT) with the generalized gradient approximation (GGA-PBEsol) simulation method. The results show that Pd–LaFeO₃ adsorption energy of −2.01 eV, which increases to −2.29 eV with the addition of rGO, indicates stronger ethanol adsorption. The band gap of Pd–LaFeO₃ was 2.34 eV before exposure to ethanol gas and decreased to 2.06 eV upon exposure. After incorporating rGO, the band gap further narrowed, from 0.11 eV before exposure to 0.05 eV after exposure. The narrowing of the energy band potentially enhances the sensor's response. Those results indicate that adding rGO to Pd–LaFeO₃ shows promising potential for ethanol gas sensor applications.