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Guo X, Zhang T, Wang Z, Zhang H, Yan Z, Li X, Hong W, Zhang A, Qian Z, Zhang X, Shu Y, Wang J, Hua L, Hong Q, Zhao Y. Tactile corpuscle-inspired piezoresistive sensors based on (3-aminopropyl) triethoxysilane-enhanced CNPs/carboxylated MWCNTs/cellulosic fiber composites for textile electronics. J Colloid Interface Sci 2024; 660:203-214. [PMID: 38244489 DOI: 10.1016/j.jcis.2024.01.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/20/2023] [Accepted: 01/08/2024] [Indexed: 01/22/2024]
Abstract
Recently, wearable electronic products and gadgets have developed quickly with the aim of catching up to or perhaps surpassing the ability of human skin to perceive information from the external world, such as pressure and strain. In this study, by first treating the cellulosic fiber (modal textile) substrate with (3-aminopropyl) triethoxysilane (APTES) and then covering it with conductive nanocomposites, a bionic corpuscle layer is produced. The sandwich structure of tactile corpuscle-inspired bionic (TCB) piezoresistive sensors created with the layer-by-layer (LBL) technology consists of a pressure-sensitive module (a bionic corpuscle), interdigital electrodes (a bionic sensory nerve), and a PU membrane (a bionic epidermis). The synergistic mechanism of hydrogen bond and coupling agent helps to improve the adhesive properties of conductive materials, and thus improve the pressure sensitive properties. The TCB sensor possesses favorable sensitivity (1.0005 kPa-1), a wide linear sensing range (1700 kPa), and a rapid response time (40 ms). The sensor is expected to be applied in a wide range of possible applications including human movement tracking, wearable detection system, and textile electronics.
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Affiliation(s)
- Xiaohui Guo
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Integrated Circuits, Anhui University, Hefei 230601, PR China.
| | - Tianxu Zhang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Integrated Circuits, Anhui University, Hefei 230601, PR China
| | - Ziang Wang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Integrated Circuits, Anhui University, Hefei 230601, PR China
| | - Huishan Zhang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Integrated Circuits, Anhui University, Hefei 230601, PR China
| | - Zihao Yan
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Integrated Circuits, Anhui University, Hefei 230601, PR China
| | - Xianghui Li
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Integrated Circuits, Anhui University, Hefei 230601, PR China
| | - Weiqiang Hong
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Integrated Circuits, Anhui University, Hefei 230601, PR China; State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian 116024, PR China; Key Laboratory for Micro/Nano Technology and System of Liaoning Province, Dalian University of Technology, Dalian 116024, PR China.
| | - Anqi Zhang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Integrated Circuits, Anhui University, Hefei 230601, PR China
| | - Zhibin Qian
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Integrated Circuits, Anhui University, Hefei 230601, PR China
| | - Xinyi Zhang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Integrated Circuits, Anhui University, Hefei 230601, PR China
| | - Yuxin Shu
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Integrated Circuits, Anhui University, Hefei 230601, PR China
| | - Jiahao Wang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Integrated Circuits, Anhui University, Hefei 230601, PR China
| | - Liangping Hua
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Integrated Circuits, Anhui University, Hefei 230601, PR China
| | - Qi Hong
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Integrated Circuits, Anhui University, Hefei 230601, PR China
| | - Ynong Zhao
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Integrated Circuits, Anhui University, Hefei 230601, PR China.
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Tai G, Wei D, Su M, Li P, Xie L, Yang J. Force-Sensitive Interface Engineering in Flexible Pressure Sensors: A Review. SENSORS 2022; 22:s22072652. [PMID: 35408265 PMCID: PMC9002484 DOI: 10.3390/s22072652] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/16/2022] [Accepted: 03/25/2022] [Indexed: 02/07/2023]
Abstract
Flexible pressure sensors have received extensive attention in recent years due to their great importance in intelligent electronic devices. In order to improve the sensing performance of flexible pressure sensors, researchers are committed to making improvements in device materials, force-sensitive interfaces, and device structures. This paper focuses on the force-sensitive interface engineering of the device, which listing the main preparation methods of various force-sensitive interface microstructures and describing their respective advantages and disadvantages from the working mechanisms and practical applications of the flexible pressure sensor. What is more, the device structures of the flexible pressure sensor are investigated with the regular and irregular force-sensitive interface and accordingly the influences of different device structures on the performance are discussed. Finally, we not only summarize diverse practical applications of the existing flexible pressure sensors controlled by the force-sensitive interface but also briefly discuss some existing problems and future prospects of how to improve the device performance through the adjustment of the force-sensitive interface.
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Affiliation(s)
- Guojun Tai
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; (G.T.); (D.W.); (M.S.); (P.L.)
- Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China
| | - Dapeng Wei
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; (G.T.); (D.W.); (M.S.); (P.L.)
| | - Min Su
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; (G.T.); (D.W.); (M.S.); (P.L.)
| | - Pei Li
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; (G.T.); (D.W.); (M.S.); (P.L.)
- Department of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China;
| | - Lei Xie
- Department of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China;
| | - Jun Yang
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; (G.T.); (D.W.); (M.S.); (P.L.)
- Correspondence:
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Dong S, Li J, Zhang S, Li N, Li B, Ge L. Preparation of Polyacrylonitrile hollow carbon spheres and its application. JOURNAL OF POLYMER RESEARCH 2021. [DOI: 10.1007/s10965-021-02658-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Sun P, Wu D, Liu C. High-sensitivity tactile sensor based on Ti 2C-PDMS sponge for wireless human-computer interaction. NANOTECHNOLOGY 2021; 32:295506. [PMID: 33827054 DOI: 10.1088/1361-6528/abf59e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 04/07/2021] [Indexed: 05/27/2023]
Abstract
Tremendous attention has been paid to high-performance flexible tactile sensors owing to their potential applications in bionic skin, wearable electronics, the Internet of Things, etc.However, the majority of pressure sensors require an intricately designed nanostructure requiring a high-cost complex manufacturing process. Therefore, the high-throughput and low-cost technology to produce high-sensitivity, flexible, pressure-sensitive materials with a large responding range is urgently needed. Herein, a novel flexible piezoresistive tactile sensor is fabricated based on the Ti2C-PDMS sponge as the conductive elastomer. The sensor exhibits a high sensitivity of 279 kPa-1in a wide pressure range (0-34.4 kPa). The response time is as fast as 0.45 s with excellent durability over 4,000 cycles. Moreover, a 16-pixel wireless sensor system is fabricated and a series of applications have been demonstrated, including real-time force perception and pressure morphology feedback, which promote the potential applications in the visualizing of pressure distribution, human-machine communication and wearable devices.
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Affiliation(s)
- Peng Sun
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Dongping Wu
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Chaoran Liu
- Ministry of Education Key Lab. of RF Circuits and Systems, College of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, People's Republic of China
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A Novel Bilinear Feature and Multi-Layer Fused Convolutional Neural Network for Tactile Shape Recognition. SENSORS 2020; 20:s20205822. [PMID: 33076258 PMCID: PMC7602476 DOI: 10.3390/s20205822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 09/25/2020] [Accepted: 10/04/2020] [Indexed: 11/16/2022]
Abstract
Convolutional neural networks (CNNs) can automatically learn features from pressure information, and some studies have applied CNNs for tactile shape recognition. However, the limited density of the sensor and its flexibility requirement lead the obtained tactile images to have a low-resolution and blurred. To address this issue, we propose a bilinear feature and multi-layer fused convolutional neural network (BMF-CNN). The bilinear calculation of the feature improves the feature extraction capability of the network. Meanwhile, the multi-layer fusion strategy exploits the complementarity of different layers to enhance the feature utilization efficiency. To validate the proposed method, a 26 class letter-shape tactile image dataset with complex edges was constructed. The BMF-CNN model achieved a 98.64% average accuracy of tactile shape. The results show that BMF-CNN can deal with tactile shapes more effectively than traditional CNN and artificial feature methods.
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