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31 results on '"Li, Zhou"'

25. Tunable synaptic behavior realized in C3N composite based memristor

Author

Jing-Yu Mao, Yi Ren, Jia-Qin Yang, Li Zhou, Yucheng Yang, Ye Zhou, Guqiao Ding, Su-Ting Han, Siwei Yang, and Shi-Rui Zhang

Subjects
Materials science, Renewable Energy, Sustainability and the Environment, business.industry, Composite number, Ionic bonding, Long-term potentiation, 02 engineering and technology, Memristor, 010402 general chemistry, 021001 nanoscience & nanotechnology, 01 natural sciences, 0104 chemical sciences, law.invention, Synapse, law, Synaptic device, Excitatory postsynaptic potential, Optoelectronics, General Materials Science, Electrical and Electronic Engineering, 0210 nano-technology, business
Abstract

Inspired by the parallel performing of storage and processing information in biological synapse, artificial synapse that can control ionic and protonic currents are ideal means for attracted tremendous attention as a promising option to break the von Neumann bottleneck. Here we demonstrate an artificial synapse with tunable synaptic behavior based on solution-processed two-dimensional (2D) C3N/polyvinylpyrrolidone (PVPy). The proton modulated memristive characteristics of synaptic device are verified by ambient pressure X-ray photoelectron spectroscopy under different H2O gas atmosphere. The highly proton conducting of C3N/PVPy matrix stems from the hydrogen bonding network between C3N and PVPy, as well as the large amount of ordered nitrogen atoms in C3N. The artificial synapse ensure a direct imitation of short-term and long-term plasticity in biological synapses including excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), paired-pulse depression (PPD), PPF following PPD and post-tetanic potentiation (PTP). The C3N/PVPy matrix-based memristor which can mimic the synapse cleft based on proton conducting mechanism may find further applications in artificial intelligence.

Published
2019

28. A self-powered artificial retina perception system for image preprocessing based on photovoltaic devices and memristive arrays

Author

Yi Ren, Xiaoyang Yang, Li Zhou, Ziyu Xiong, Ye Zhou, Yongji Chen, Huilin Li, Feng Pan, and Su-Ting Han

Subjects
Materials science, genetic structures, Artificial neural network, Renewable Energy, Sustainability and the Environment, Photovoltaic system, Feature extraction, 02 engineering and technology, Memristor, Energy consumption, 010402 general chemistry, 021001 nanoscience & nanotechnology, 01 natural sciences, Convolutional neural network, Signal, 0104 chemical sciences, law.invention, Neuromorphic engineering, law, Electronic engineering, General Materials Science, sense organs, Electrical and Electronic Engineering, 0210 nano-technology
Abstract

Artificial retina perception system is significant to pattern recognition and visual function emulation. However, the recent artificial retina system is mainly reported on the integration of three-terminal transistors, whose structural limitations may result in low processing speeds and high energy consumption due to a low array density and complex line design. Furthermore, the external power source is required to drive devices so that the power consumption of the system would increase. Here we present a self-powered artificial retina perception system by utilizing two-terminal solar cells as artificial neurons and perovskite-based memristors as artificial synapses, ensuring the bio-inspired retina system with extendable crossbar array structure for high-density and low power consumption neural networks. By a light stimulation with various wavelengths and intensities, the electrical pre-synaptic signal is generated in the solar cell and subsequently transferred to the perovskite-based memristor to perform further information preprocessing. Especially, the applicability of the artificial retina system to neuromorphic computing is demonstrated for contrast enhancement and noise reduction. The retina perception system is capable of feature extraction by to implement partial functions of convolutional neural networks (CNNs) on the hardware level with improved recognition rate, boosted recognition speed, and reduced energy consumption.

Published
2020

31. A wearable system based on core-shell structured peptide-Co9S8supercapacitor and triboelectric nanogenerator

Author

Xiong, Wei, Hu, Kuan, Li, Zhe, Jiang, Yixiang, Li, Zigang, Li, Zhou, and Wang, Xinwei

Abstract

We report a new core-shell structure of peptide-Co9S8nanobricks for supercapacitor electrode. The nanostructured peptide is intrinsically flexible and biocompatible, which is highly suited for wearable supercapacitor electrodes. However, the application of the nanostructured peptide is often limited by its generally low power density and energy density, and its long-term stability is also a concern. Herein, the core-shell structure of peptide-Co9S8nanobricks is synthesized by conformally coating a thin shell layer of Co9S8via atomic layer deposition (ALD) onto self-assembled peptide nanobricks. The shell layer can not only protect the peptide material from being attacked by the electrolyte but also contribute extra capacitance to the supercapacitor. The supercapacitors made of the peptide-Co9S8nanobricks exhibit a high capacitance of 1.3 F/cm2at 0.7 mA/cm2and a much improved cycling stability of 96% capacitance retention after 5000 charge-discharge cycling. High-performance flexible solid-state asymmetric supercapacitor (SC) can be also made from the core-shell peptide-Co9S8nanobricks with activated carbon. The flexible asymmetric supercapacitor can also be coupled with a triboelectric nanogenerator (TENG) to afford a flexible self-powered TENG/SC system. The TENG/SC system with 2.7-h continuously charging by TENG can power a red LED for 21 min, which demonstrates its excellent performance of self-charging and energy-supplying, and therefore it is of great promise for future wearable electronics applications.

Published
2019
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