为突触模拟和生物传感器提供高性能和稳定的n型有机混合离子电子导体

IF 26.8 1区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Wanli Yang, Suxiang Ma, Sergio Gámez‐Valenzuela, Sang Young Jeong, Jin‐Woo Lee, Haihui Cai, Rongjin Zhu, Bin Liu, Han Young Woo, Bumjoon J. Kim, Shu‐Jen Wang, Paddy Kwok Leung Chan, Xugang Guo, Kui Feng
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引用次数: 0

摘要

高性能和稳定的n型有机混合离子电子导体(omiec)是推进基于有机电化学晶体管(OECTs)的低功耗互补电路和生物传感器的关键,但其发展仍然是一个巨大的挑战。本研究提出了一系列以双噻吩(BTI)和不同共轭主链长度的BTI衍生物为受体的供体-受体聚合物。中等尺寸的熔融BTI二聚体使聚合物PBTI2g - DTCN同时提高了离子吸收能力、膜结构秩序和离子/电子传输能力。因此,基于PBTI2g - DTCN的n型传统OECT在积累模式下获得了令人印象印象的0.84 cm2 V−1 s−1的电子迁移率和287.8 F cm−1 V−1 s−1的优异值(µC*),而垂直OECT (vOECTs)获得了71.8µsµm−2的最先进的区域归一化跨导(gm, a),具有显著的运行稳定性。通过精细地操纵通道元件,vOECTs表现出双模式工作,在非易失性和易失性状态之间切换。在非易失性模式下,基于vOECT的人工突触具有优异的环境稳定性,可以实现动态学习,并用于卷积神经网络的图像识别。在挥发模式下,它们擅长生物传感、监测心电图和肌电信号。这些显著的结果表明,骨干裁剪是开发用于突触和传感器应用的高性能n型omiec的有力策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Backbone Tailoring Enables High‐Performance and Stable n‐Type Organic Mixed Ionic‐Electronic Conductors for Synaptic Simulation and Biosensor
High‐performance and stable n‐type organic mixed ionic‐electronic conductors (OMIECs) are crucial for advancing organic electrochemical transistors (OECTs)‐based low‐power complementary circuits and biosensors, yet their development remains a great challenge. Herein, the study presents a series of donor‐acceptor polymers incorporating bithiophene (BTI) and fused BTI derivatives with varying conjugation backbone lengths as acceptors. The mid‐size fused BTI dimer enables polymer PBTI2g‐DTCN with simultaneously improved ion‐uptake capability, film structural order, and ion/electron transport capability. Consequently, an impressive electron mobility of 0.84 cm2 V−1 s−1 and a record figure‐of‐merit (µC*) of 287.8 F cm−1 V−1 s−1 are achieved for PBTI2g‐DTCN‐based n‐type conventional OECT in accumulation mode, while the vertical OECTs (vOECTs) attain a state‐of‐the‐art area‐normalized transconductance (gm,A) of 71.8 µS µm−2 with remarkable operational stability. Through finely manipulating the channel components, the vOECTs demonstrate dual‐mode operation, switching between non‐volatile and volatile states. In non‐volatile mode, vOECT‐based artificial synapses with excellent ambient stability enable dynamic learning and are employed in convolutional neural networks for image recognition. In volatile mode, they excel in biosensing, monitoring electrocardiography and electromyography signals. These remarkable results demonstrate that backbone tailoring is a powerful strategy for developing high‐performance n‐type OMIECs for synaptic and sensor applications.
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来源期刊
Advanced Materials
Advanced Materials 工程技术-材料科学:综合
CiteScore
43.00
自引率
4.10%
发文量
2182
审稿时长
2 months
期刊介绍: Advanced Materials, one of the world's most prestigious journals and the foundation of the Advanced portfolio, is the home of choice for best-in-class materials science for more than 30 years. Following this fast-growing and interdisciplinary field, we are considering and publishing the most important discoveries on any and all materials from materials scientists, chemists, physicists, engineers as well as health and life scientists and bringing you the latest results and trends in modern materials-related research every week.
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