Self-powered sensor based on compressible ionic gel electrolyte for simultaneous determination of temperature and pressure

IF 22.7 1区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Infomat Pub Date : 2024-04-29 DOI:10.1002/inf2.12545
Junjie Zou, Yanan Ma, Chenxu Liu, Yimei Xie, Xingyao Dai, Xinhui Li, Shuxuan Li, Shaohui Peng, Yang Yue, Shuo Wang, Ce-Wen Nan, Xin Zhang
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引用次数: 0

Abstract

The simultaneous detection of multiple stimuli, such as pressure and temperature, has long been a persistent challenge for developing electronic skin (e-skin) to emulate the functionality of human skin. Meanwhile, the demand for integrated power supply units is an additional pressing concern to achieve its lightweightness and flexibility. Herein, we propose a self-powered dual temperature–pressure (SPDM) sensor, which utilizes a compressible ionic gel electrolyte driven by the potential difference between MXene and Al electrodes. The SPDM sensor exhibits a rapid and timely response to changes in pressure-induced deformation, while exhibiting a slow and hysteretic response to temperature variations. These distinct response characteristics enable the differentiation of current signals generated by different stimuli through machine learning, resulting in an impressive accuracy rate of 99.1%. Furthermore, the developed SPDM sensor exhibits a wide pressure detection range of 0–800 kPa and a broad temperature detection range of 5–75°C, encompassing the environmental conditions encountered in daily human life. The dual-mode coupled strategy by machine learning provides an effective approach for temperature and pressure detection and discrimination, showcasing its potential applications in wearable electronics, intelligent robots, human–machine interactions, and so on.

Abstract Image

Abstract Image

基于可压缩离子凝胶电解质的自供电传感器,用于同时测定温度和压力
长期以来,如何同时检测压力和温度等多种刺激一直是开发电子皮肤(e-skin)以模拟人体皮肤功能的难题。同时,要实现电子皮肤的轻便性和灵活性,对集成电源装置的需求也是一个迫切的问题。在此,我们提出了一种自供电双温压(SPDM)传感器,它利用 MXene 和 Al 电极之间的电位差驱动可压缩离子凝胶电解质。SPDM 传感器对压力引起的变形变化做出快速及时的响应,同时对温度变化做出缓慢滞后的响应。这些不同的响应特性使得通过机器学习来区分不同刺激产生的电流信号成为可能,从而使准确率达到令人印象深刻的 99.1%。此外,所开发的 SPDM 传感器具有 0-800 kPa 的宽压力检测范围和 5-75°C 的宽温度检测范围,涵盖了人类日常生活中遇到的各种环境条件。机器学习的双模耦合策略为温度和压力的检测和判别提供了一种有效的方法,展示了其在可穿戴电子设备、智能机器人、人机交互等领域的潜在应用。
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来源期刊
Infomat
Infomat MATERIALS SCIENCE, MULTIDISCIPLINARY-
CiteScore
37.70
自引率
3.10%
发文量
111
审稿时长
8 weeks
期刊介绍: InfoMat, an interdisciplinary and open-access journal, caters to the growing scientific interest in novel materials with unique electrical, optical, and magnetic properties, focusing on their applications in the rapid advancement of information technology. The journal serves as a high-quality platform for researchers across diverse scientific areas to share their findings, critical opinions, and foster collaboration between the materials science and information technology communities.
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