Methanol is a respiratory biomarker for pulmonary diseases,
including
COVID-19, and is a common chemical that may harm people if they are
accidentally exposed to it. It is significant to effectively identify
methanol in complex environments, yet few sensors can do so. In this
work, the strategy of coating perovskites with metal oxides is proposed
to synthesize core–shell CsPbBr3@ZnO nanocrystals.
The CsPbBr3@ZnO sensor displays a response/recovery time
of 3.27/3.11 s to 10 ppm methanol at room temperature, with a detection
limit of 1 ppm. Using machine learning algorithms, the sensor can
effectively identify methanol from an unknown gas mixture with 94%
accuracy. Meanwhile, density functional theory is used to reveal the
formation process of the core–shell structure and the target
gas identification mechanism. The strong adsorption between CsPbBr3 and the ligand zinc acetylacetonate lays the foundation for
the formation of the core–shell structure. The crystal structure,
density of states, and band structure were influenced by different
gases, which results in different response/recovery behaviors and
makes it possible to identify methanol from mixed environments. Furthermore,
due to the formation of type II band alignment, the gas response performance
of the sensor is further improved under UV light irradiation.