Accelerated Surface Reconstruction through Regulating the Solid-Liquid Interface by Oxyanions in Perovskite Electrocatalysts for Enhanced Oxygen Evolution.

A comprehensive understanding of surface reconstruction was critical to developing high performance lattice oxygen oxidation mechanism (LOM) based perovskite electrocatalysts. Traditionally, the primary determining factor of the surface reconstruction process was believed to be the oxygen vacancy formation energy. Hence, most previous studies focused on optimizing composition to reduce the oxygen vacancy formation energy, which in turn facilitated the surface reconstruction process. Here, for the first time, we found that adding oxyanions (SO4 2- , CO3 2- , NO3 - ) into the electrolyte could effectively regulate the solid-liquid interface, significantly accelerating the surface reconstruction process and enhancing oxygen evolution reaction (OER) activities. Further studies indicated that the added oxyanions would adsorb onto the solid-liquid interface layer, disrupting the dynamic equilibrium between the adsorbed OH- ions and the OH- ions generated during surface reconstruction process. As such, the OH- ions generated during surface reconstruction process could be more readily released into the electrolyte, thereby leading to an acceleration of the surface reconstruction. Thus, it was expected that our finding would provide a new layer of understanding to the surface reconstruction process in LOM-based perovskite electrocatalysts.

In-Memory Computing using Memristor Arrays with Ultrathin 2D PdSeOx /PdSe2 Heterostructure.

In-memory computing based on memristor arrays holds promise to address the speed and energy issues of the classical von Neumann computing system. However, the stochasticity of ions' transport in conventional oxide-based memristors imposes severe intrinsic variability, which compromises learning accuracy and hinders the implementation of neural network hardware accelerators. Here, these challenges are addressed using a low-voltage memristor array based on an ultrathin PdSeOx /PdSe2 heterostructure switching medium realized by a controllable ultraviolet (UV)-ozone treatment. A distinctively different ions' transport mechanism is revealed in the heterostructure that can confine the formation of conductive filaments, leading to a remarkable uniform switching with low set and reset voltage variability values of 4.8% and -3.6%, respectively. Moreover, convolutional image processing is further implemented using various crossbar kernels that achieve a high recognition accuracy of ≈93.4% due to the highly linear and symmetric analog weight update as well as multiple conductance states, manifesting its potential beyond von Neumann computing.

Deciphering NH3 Adsorption Kinetics in Ternary Ni-Cu-Fe Oxyhydroxide toward Efficient Ammonia Oxidation Reaction.

Developing efficient catalysts for the ammonia oxidation reaction (AOR) is crucial for NH3 utilization as a large-scale energy carrier. This work reports a promising Ni-Cu-Fe-OOH material for ammonia oxidation, and density functional theory is used to investigate the AOR mechanism. It is revealed that the oxygen-atoms bonded with the metal-atom on the surface of electrode play an important role in AOR. By codoping Cu and Fe, the electron distribution around the oxygen-atom is affected, which helps to promote the occurrence of ammonia oxidation. The Ni-Cu-Fe-OOH material delivers one of the highest ammonia removal efficiency to date of ≈90% after 12 h. In addition, ≈55% of the initial ammonia is successfully degraded after 24 h in high ammonia concentration. Thus, this work reveals the mechanism of AOR that can provide new ideas to tailor more powerful and updated catalysts in the future.