High ionic conductivity Li0.33La0.557TiO3nanofiber/polymer composite solid electrolyte for flexible transparent InZnO synaptic transistors

Nanomaterials for Flexible Neuromorphics

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.

Toward Intelligent Display with Neuromorphic Technology

In the era of the Internet and the Internet of Things, display technology has evolved significantly toward full-scene display and realistic display. Incorporating "intelligence" into displays is a crucial technical approach to meet the demands of this development. Traditional display technology relies on distributed hardware systems to achieve intelligent displays but encounters challenges stemming from the physical separation of sensing, processing, and light-emitting modules. The high energy consumption and data transformation delays limited the development of intelligence display, breaking the physical separation is crucial to overcoming the bottlenecks of intelligence display technology. Inspired by the biological neural system, neuromorphic technology with all-in-one features is widely employed across various fields. It proves effective in reducing system power consumption, facilitating frequent data transformation, and enabling cross-scene integration. Neuromorphic technology shows great potential to overcome display technology bottlenecks, realizing the full-scene display and realistic display with high efficiency and low power consumption. This review offers a comprehensive summary of recent advancements in the application of neuromorphic technology in displays, with a focus on interoperability. This work delves into its state-of-the-art designs and potential future developments aimed at revolutionizing display technology.

Oxide Ionic Neuro-Transistors for Bio-inspired Computing

Current computing systems rely on Boolean logic and von Neumann architecture, where computing cells are based on high-speed electron-conducting complementary metal-oxide-semiconductor (CMOS) transistors. In contrast, ions play an essential role in biological neural computing. Compared with CMOS units, the synapse/neuron computing speed is much lower, but the human brain performs much better in many tasks such as pattern recognition and decision-making. Recently, ionic dynamics in oxide electrolyte-gated transistors have attracted increasing attention in the field of neuromorphic computing, which is more similar to the computing modality in the biological brain. In this review article, we start with the introduction of some ionic processes in biological brain computing. Then, electrolyte-gated ionic transistors, especially oxide ionic transistors, are briefly introduced. Later, we review the state-of-the-art progress in oxide electrolyte-gated transistors for ionic neuromorphic computing including dynamic synaptic plasticity emulation, spatiotemporal information processing, and artificial sensory neuron function implementation. Finally, we will address the current challenges and offer recommendations along with potential research directions.

Bacteria cellulose framework-supported solid composite polymer electrolytes for ambient-temperature lithium metal batteries

Composite polymer electrolyte (CPE) films with high room temperature ionic conductivity are urgently needed for the practical application of high-safety solid-state batteries (SSBs). Here, a flexible polymer-polymer CPE thin film reinforced by a three-dimensional (3D) bacterial cellulose (BC) framework derived from natural BC hydrogel was prepared via thein situphoto-polymerization method. The BC film was utilized as the supporting matrix to ensure high flexibility and mechanical strength. The BC-CPE attained a high room temperature ionic conductivity of 1.3 × 10^-4S cm^-1. The Li∣BC-CPE∣Li symmetric cell manifested stable cycles of more than 1200 h. The LCO∣BC-CPE∣Li full cell attained an initial discharge specific capacity of 128.7 mAh g^-1with 82.6% discharge capacity retention after 150 cycles at 0.2 C under room temperature. The proposed polymer-polymer CPE configuration represents a promising route for manufacturing environmental SSBs, especially since cellulose biomaterials are abundant in nature.

Oxygen-Vacancy-Induced Synaptic Plasticity in an Electrospun InGdO Nanofiber Transistor for a Gas Sensory System with a Learning Function

The perceptual learning function of a simulating human body is very important for constructing a neural computing system and a brainlike computer in the future. The sense of smell is an important part of the human sensory nervous system. However, current gas sensors simply convert gas concentrations into electrical signals and do not have the same learning and memory function as synapses. To solve this problem, we propose a new sensing idea to induce and activate the synaptic properties of transistors by adjusting the oxygen vacancy in the active layer. This sensor combines gas detection with synaptic memory and learning and overcomes the disadvantage of the separation of synaptic transistors and sensors, thus greatly reducing the cost of production. This work combines the detection of N,N-dimethylformamide (DMF) gas with the synaptic mechanism of human olfactory nerves. We successfully fabricated an InGdO nanofiber field-effect transistor by electrostatic spinning and simulated the response of human olfactory synapses to target gas by regulating the oxygen vacancy of the InGdO nanofiber. The synaptic transistor response under different concentrations of unmodulated pulses is tested, and the pavlovian conditioned reflex experiment is simulated successfully. This work provides a new idea of a gas sensor device, which is very important for the development of high-performance gas sensors and bionic electronic devices in the future.