Perovskite CsPbBr3 quantum dots capped with zinc acetylacetonate: Gas sensing of ethanol in humidity with aid of machine-learning

Abstract

Ethanol and water are sufficiently miscible, so it is challenging to distinguish ethanol from a mixed atmosphere of ethanol and water. In this work, gas-sensitive materials were prepared with a high response to ethanol, and combined with intelligent algorithms, which can effectively identify ethanol from the mixture of ethanol and water. Specifically, a route was developed to improve the stability of perovskite CsPbBr3 quantum dots (QDs) by passivating its surface with ligands, leading to excellent gas sensing performance at room temperature (RT). Zinc acetylacetonate (Zn(acac)2) was introduced into the reaction mixture to stabilize the surface of thin QDs and purify them without losing their perovskite structure or quantum confinement effect. The as-obtained perovskite QDs with capping Zn(acac)2 displayed an average size of about 9.35 nm, as well as a gas sensing response of 0.275 at 1300 ppm towards ethanol, response/recovery time of 8.5/13.9 s. Such an excellent gas sensing performance was attributed to an interaction of the ethanol molecules with CsPbBr3 QDs through a metal-organic Zn(acac)2 molecule, leading to the enhancement of both the sensitivity and response towards ethanol. Furthermore, the accurate recognition of low-concentration ethanol under humidity conditions was also realized with an aid of machine learning methods. Totally, this work is not only helpful for the synthesis of perovskite QDs, but also develop a method for identifying low-concentration ethanol through machine learning under a wet environment.