Heterojunction nanofluidic memristors based on peptide chain valves for neuromorphic applications

IF 10.7 1区生物学 Q1 BIOPHYSICS

Biosensors and Bioelectronics Pub Date : 2025-04-18 DOI:10.1016/j.bios.2025.117496

Honglin Lv , Yin Zhang

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引用次数: 0

Abstract

Memristors exhibit significant potential for neuromorphic computing due to their unique properties. This study introduces a heterojunction nanofluidic memristor (HJNFM) and explores its applications in simulating synapses and constructing neural networks. The HJNFM consists of a SnS₂ and MoS₂ heterojunction nanochannel with a peptide chain valve. The opening and closing dynamics of peptide chain valve alter ionic conductance of the nanochannel and realize the memristor characteristics. The sequence of the peptide chain also affects the electrical properties of HJNFM. Additionally, by setting up multi SnS₂ strips in the nanochannel, the multi-HJNFM can achieve permanent memory and emulate synaptic features including short-term and long-term memory. Notably, we construct a convolutional neural network from multi-HJNFMs, which achieves 94 % accuracy in a digit recognition task. This study presents a new approach to constructing nanofluidic memristors, which could be advantageous for developing new forms of neuromorphic computing in the future.

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神经形态应用中基于肽链阀的异质结纳米流控忆阻器

忆阻器由于其独特的特性，在神经形态计算中表现出巨大的潜力。介绍了一种异质结纳米流控忆阻器（HJNFM），并探讨了其在模拟突触和构建神经网络方面的应用。该HJNFM由SnS2和MoS2异质结纳米通道和肽链阀组成。肽链阀的开启和关闭动态改变了纳米通道的离子电导，实现了纳米通道的忆阻特性。肽链的顺序也会影响HJNFM的电学性质。此外，通过在纳米通道中设置多个SnS2条带，多hjnfm可以实现永久记忆，并模拟包括短期和长期记忆在内的突触特征。值得注意的是，我们构建了一个由多个hjnfm组成的卷积神经网络，在数字识别任务中达到了94%的准确率。本研究提出了一种构建纳米流体忆阻器的新方法，这将有利于未来发展新型神经形态计算。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Biosensors and Bioelectronics 工程技术-电化学

CiteScore

20.80

自引率

7.10%

发文量

1006

审稿时长

29 days

期刊介绍： Biosensors & Bioelectronics, along with its open access companion journal Biosensors & Bioelectronics: X, is the leading international publication in the field of biosensors and bioelectronics. It covers research, design, development, and application of biosensors, which are analytical devices incorporating biological materials with physicochemical transducers. These devices, including sensors, DNA chips, electronic noses, and lab-on-a-chip, produce digital signals proportional to specific analytes. Examples include immunosensors and enzyme-based biosensors, applied in various fields such as medicine, environmental monitoring, and food industry. The journal also focuses on molecular and supramolecular structures for enhancing device performance.