Gradual conductance modulation by defect reorganization in amorphous oxide memristors.

Amorphous oxides show great prospects in revolutionizing memristors benefiting from their abundant non-stoichiometric composition. However, an in-depth investigation of the memristive characteristics in amorphous oxides is inadequate and the resistive switching mechanism is still controversial. In this study, aiming to clearly understand the gradual conductance modulation that is deeply bound to the evolution of defects-mainly oxygen vacancies, forming-free memristors based on amorphous ZnAlSnO are fabricated, which exhibit high reproducibility with an initial low-resistance state. Pulse depression reveals the logarithmic-exponential mixed relaxation during RESET owing to the diffusion of oxygen vacancies in orthogonal directions. The remnants of conductive filaments formed through aggregation of oxygen vacancies induced by high-electric-field are identified using ex situ TEM. Especially, the conductance of the filament, including the remnant filament, is larger than that of the hopping conductive channel derived from the diffusion of oxygen vacancies. The Fermi level in the conduction band rationalizes the decay of the high resistance state. Rare oxidation-migration of Au occurs upon device failure, resulting in numerous gold nanoclusters in the functional layer. These comprehensive revelations on the reorganization of oxygen vacancies could provide original ideas for the design of memristors.

Machine Learning-Assisted Sensor Based on CsPbBr3@ZnO Nanocrystals for Identifying Methanol in Mixed Environments.

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.