Review: Application of Bionic-Structured Materials in Solid-State Electrolytes for High-Performance Lithium Metal Batteries.

Solid-state lithium metal batteries (SSLMBs) have gained significant attention in energy storage research due to their high energy density and significantly improved safety. But there are still certain problems with lithium dendrite growth, interface stability, and room-temperature practicality. Nature continually inspires human development and intricate design strategies to achieve optimal structural applications. Innovative solid-state electrolytes (SSEs), inspired by diverse natural species, have demonstrated exceptional physical, chemical, and mechanical properties. This review provides an overview of typical bionic-structured materials in SSEs, particularly those mimicking plant and animal structures, with a focus on their latest advancements in applications of solid-state lithium metal batteries. Commencing from plant structures encompassing roots, trunks, leaves, flowers, fruits, and cellular levels, the detailed influence of biomimetic strategies on SSE design and electrochemical performance are presented in this review. Subsequently, the recent progress of animal-inspired nanostructures in SSEs is summarized, including layered structures, surface morphologies, and interface compatibility in both two-dimensional (2D) and three-dimensional (3D) aspects. Finally, we also evaluate the current challenges and provide a concise outlook on future research directions. We anticipate that the review will provide useful information for future reference regarding the design of bionic-structured materials in SSEs.

An Artificial Neural Network Based on Oxide Synaptic Transistor for Accurate and Robust Image Recognition.

Synaptic transistors with low-temperature, solution-processed dielectric films have demonstrated programmable conductance, and therefore potential applications in hardware artificial neural networks for recognizing noisy images. Here, we engineered AlOx/InOx synaptic transistors via a solution process to instantiate neural networks. The transistors show long-term potentiation under appropriate gate voltage pulses. The artificial neural network, consisting of one input layer and one output layer, was constructed using 9 × 3 synaptic transistors. By programming the calculated weight, the hardware network can recognize 3 × 3 pixel images of characters z, v and n with a high accuracy of 85%, even with 40% noise. This work demonstrates that metal-oxide transistors, which exhibit significant long-term potentiation of conductance, can be used for the accurate recognition of noisy images.