Designing Highly Selective NO2 and SO3 Sensors via Doped and Substituted Polythiophene: A DFT, NEGF, and Microkinetic Study

Abstract

Sensing and purifying toxic gases, particularly NOx and SOx from flue gases, is essential for sustainable industrial development. Developing highly efficient gas sensors based on conducting polymers (CPs) is promising due to their low operating temperatures and excellent processability. However, CP-based gas sensors often suffer from low selectivity, highlighting the need for the rational design of novel sensing materials. In this work, we employed density functional theory (DFT) and non-equilibrium Green’s function (NEGF) methods to explore the current–voltage (I–V) and resistance–voltage (R–V) characteristics of gas-adsorbed polythiophene (PT). Li- and Cl-doped PT demonstrate enhanced electrical conductivity, whereas NH2-substituted PT (PT–NH2/Li) further increases the concentration of excess electrons and forms hydrogen bonds with NO2 (SO3). A synergistic effect was observed in the PT–NH2/Li system, involving cyclic electron flow: electrons transfer from PT to NO2 (SO3), then to the amine groups, and back to PT, leading to notably high adsorption energies for these gases on PT–NH2/Li. By integrating microkinetic (MK) simulations with NEGF calculations, we effectively modeled the sensing process and derived the time–resistance curves of the material under gas exposure. Our findings show that PT/Li and PT–NH2/Li exhibit high sensitivity, high selectivity, short recovery times, and low detection limits for NO2 and SO3, respectively. This computational study underscores the rational design of new gas sensors through the combined application of DFT, NEGF, and MK simulations.