Organic–inorganic hybrid perovskite solar cells have been developing rapidly in the past several years, and their power conversion efficiency has reached over 20%, nearing that of polycrystalline silicon solar cells. Because the diffusion length of the hole in perovskites is longer than that of the electron, the performance of the device can be improved by using an electron transporting layer, e.g., TiO₂, ZnO and TiO₂/Al₂O₃. Nano-structured electron transporting materials facilitate not only electron collection but also morphology control of the perovskites. The properties, morphology and preparation methods of perovskites are reviewed in the present article. A comprehensive understanding of the relationship between the structure and property will benefit the precise control of the electron transporting process and thus further improve the performance of perovskite solar cells.

Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.