Preparation and force sensitivity of flexible layered graphene sensor

doi:10.3785/j.issn.1008-973X.2024.01.016

Journal of ZheJiang University (Engineering Science)

2024, Vol. 58

Issue (1): 150-160 DOI: 10.3785/j.issn.1008-973X.2024.01.016

Preparation and force sensitivity of flexible layered graphene sensor

Zhiqiang WU1(

),Jun WEI2,Rongzhen DONG2

1. College of Architecture and Civil Engineering, Xinyang Normal University, Xinyang 464000, China
2. School of Civil Engineering, Central South University, Changsha 410075, China

Download:

HTML

PDF(2378KB) HTML
Export: BibTeX | EndNote (RIS)

Abstract

A PDMS/RGO-CNF/PDMS layered sensor was designed and prepared with reduced graphene oxide-cellulose nanofiber (RGO-CNF) as the sensing layer and polydimethylsiloxane (PDMS) as the substrate by using a special configuration method in order to make up for the shortcomings of existing sensors in monitoring discontinuous deformation of concrete. The mechanical, electrical properties and force sensitivity of the sensor were tested and analyzed. Results show that CNF can effectively assist RGO to uniformly disperse in isopropanol. The sensing layer constructed through chemical swelling and pore filling was tightly bonded to the substrate layer, and could withstand over 100% tensile strain. The strain resistance response of the sensor reaches a stable state after about 10 cycles of stretching, exhibiting good recoverability and repeatability. The resistance change rate of the sensor shows an approximate linear variation within the range of 0-10% strain, with a sensitivity coefficient of up to 15. The resistance change rate increases exponentially as the strain continues to increase. The strain resistance response strength increases with the increase of strain rate, and the established response model considering strain rate effect can better predict the force sensitive behavior of the sensor.

Key words： graphene layered sensor force sensitivity strain resistance response model cellulose nanofiber

Received: 09 January 2023 Published: 07 November 2023

CLC:

TU 599

Fund: 国家自然科学基金资助项目（51778628）；河南省科技攻关计划资助项目（222102210241）

	Service
	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Zhiqiang WU
	Jun WEI
	Rongzhen DONG

Cite this article:

Zhiqiang WU,Jun WEI,Rongzhen DONG. Preparation and force sensitivity of flexible layered graphene sensor. Journal of ZheJiang University (Engineering Science), 2024, 58(1): 150-160.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.01.016 OR https://www.zjujournals.com/eng/Y2024/V58/I1/150

柔性层状石墨烯感应元件制备及其力敏特性

为了弥补现有传感器在混凝土非连续变形监测上的不足，采用特殊构型法，以还原氧化石墨烯-纳米纤维素(RGO-CNF)为传感层、聚二甲基硅氧烷(PDMS)为基层，设计和制备了PDMS/RGO-CNF/PDMS层状感应元件，对其力学、电学和力敏性能进行测试和分析. 结果表明，CNF能够有效地协助RGO在异丙醇中均匀分散，经化学溶胀和孔隙填充构建的传感层与基底层紧密结合，能够承受超过100%的拉伸应变. 感应元件经过约10次循环拉伸后，应变电阻响应达到稳定状态，表现出良好的可回复性和可重复性. 在0~10%应变下，感应元件的电阻变化率近似呈线性变化，灵敏系数可达15，随应变继续增大，电阻变化率呈指数型增长. 应变电阻响应强度随应变速率的增大而提高，利用建立的考虑应变率效应的应变电阻响应模型，能够较好地预测感应元件的力敏行为.

关键词： 石墨烯, 层状感应元件, 力敏性, 应变电阻响应模型, 纳米纤维素

Fig.1 Structural composition of graphene sensor

Fig.2 Diagrammatic sketch of strain resistance response of graphene sensor

Tab.1 Grouping of RGO dispersion test

Fig.3 Absorbance of RGO dispersion solution

Fig.4 Absorbance variation of R2 and R4 dispersion solution with ultrasonic time

Tab.2 Average grain diameter of R2 and R4 dispersion solution

Fig.5 Apparent state variation of R2 and R4 dispersion solution with standing time

Fig.6 Preparation process of PDMS/RGO-CNF/PDMS sensor

Fig.7 SEM images of various parts of PRCP sensor during preparation process

Fig.8 Stress-strain and resistance test of PRCP sensor

Fig.9 Stress-strain curves of PRCP sensors with different graphene distribution density

Fig.10 Initial resistance versus graphene distribution density curve of PRCP sensors

Fig.11 Resistance change rate versus strain curves of PⅠ, PⅡ and PⅢ under first tension

Fig.12 Resistance change rate versus strain curves of PⅠ, PⅡ and PⅢ under cyclic tension

Fig.13 Slope of fitting straight line versus cycle number curves

Fig.14 Strain resistance response curves of PⅠ, PⅡ and PⅢ within 100% strain range

Fig.15 Resistance change rate versus standing time curves of PⅡ after first tension at different speeds

Fig.16 Resistance change rate versus strain curves of PⅡ tensioned at different speeds

Tab.3 Parameter values of fitting curves of PⅡ test results tensioned at different speeds


[1]	杨静, 李大鹏, 岳清瑞, 等建筑与基础设施全寿命周期智能化的研究现状及关键科学问题[J]. 中国科学基金, 2021, 35 (4): 620- 626 YANG Jing, LI Dapeng, YUE Qingrui, et al Key scientific issues and state-of-art in whole-lifecycle intelligentization of buildings and infrastructures[J]. Bulletin of National Natural Science Foundation of China, 2021, 35 (4): 620- 626 doi: 10.16262/j.cnki.1000-8217.2021.04.012

[2]	丁思齐, 韩宝国, 欧进萍本征自感知混凝土及其智能结构[J]. 工程力学, 2022, 39 (3): 1- 10 DING Siqi, HAN Baoguo, OU Jinping Intrinsic self-sensing concrete for smart structures[J]. Engineering Mechanics, 2022, 39 (3): 1- 10 doi: 10.6052/j.issn.1000-4750.2021.06.ST02

[3]	鲍跃全, 李惠人工智能时代的土木工程[J]. 土木工程学报, 2019, 52 (5): 1- 11 BAO Yuequan, LI Hui Artificial intelligence for civil engineering[J]. China Civil Engineering Journal, 2019, 52 (5): 1- 11 doi: 10.15951/j.tmgcxb.2019.05.001

[4]	CHAU F W Effect of transverse sensitivity of strain gage on the accuracy of computed principle strain[J]. Experimental Techniques, 2010, 11 (7): 22- 23

[5]	RODRIGUEZ G, CASAS J R, VILLALBA S Cracking assessment in concrete structures by distributed optical fiber[J]. Smart Materials and Structures, 2015, 24 (3): 035005 doi: 10.1088/0964-1726/24/3/035005

[6]	RODRIGUEZ G, CASAS J R, VILLALBA S Shear crack width assessment in concrete structures by 2D distributed optical fiber[J]. Engineering Structures, 2019, 195: 508- 523 doi: 10.1016/j.engstruct.2019.05.079

[7]	BISWAL S, RAMASWAMY A Measurement of existing prestressing force in concrete structures through an embedded vibrating beam strain gauge[J]. Measurement, 2016, 83: 10- 19 doi: 10.1016/j.measurement.2016.01.031

[8]	FENG Q, CUI J, WANG Q, et al A feasibility study on real-time evaluation of concrete surface crack repairing using embedded piezoceramic transducers[J]. Measurement, 2018, 122: 591- 596 doi: 10.1016/j.measurement.2017.09.015

[9]	LIAO W I, CHIU C K Seismic health monitoring of a space reinforced concrete frame structure using piezoceramic-based sensors[J]. Journal of Aerospace Engineering, 2019, 32 (3): 04019015 doi: 10.1061/(ASCE)AS.1943-5525.0000999

[10]	LIU Y F, CHO S, SPENCER B F, et al Concrete crack assessment using digital image processing and 3D scene reconstruction[J]. Journal of Computing in Civil Engineering, 2016, 30 (1): 04014124 doi: 10.1061/(ASCE)CP.1943-5487.0000446

[11]	KIM H, AHN E, SHIN M, et al Crack and noncrack classification from concrete surface images using machine learning[J]. Structural Health Monitoring, 2019, 18 (3): 725- 738 doi: 10.1177/1475921718768747

[12]	高军礼, 周华, 宋海涛, 等基于残差注意力和金字塔上采样的图像语义分割[J]. 信阳师范学院学报: 自然科学版, 2022, 35 (1): 134- 140 GAO Junli, ZHOU Hua, SONG Haitao, et al Image semantic segmentation based on residual attention and pyramid upsampling[J]. Journal of Xinyang Normal University: Natural Science Edition, 2022, 35 (1): 134- 140

[13]	ROUCHIER S, FORAY G, GODIN N, et al Damage monitoring in fibre reinforced mortar by combined digital image correlation and acoustic emission[J]. Construction and Building Materials, 2013, 38: 371- 380 doi: 10.1016/j.conbuildmat.2012.07.106

[14]	PERRY M, MCKEEMAN I, SAAFI M, et al Wireless surface acoustic wave sensors for displacement and crack monitoring in concrete structures[J]. Smart Materials and Structures, 2016, 25 (3): 035035 doi: 10.1088/0964-1726/25/3/035035

[15]	LIU H, DONG M, HUANG W, et al Lightweight conductive graphene/thermoplastic polyurethane foams with ultrahigh compressibility for piezoresistive sensing[J]. Journal of Materials Chemistry C, 2017, 5 (1): 73- 83 doi: 10.1039/C6TC03713E

[16]	LU S, TIAN C, WANG X, et al Strain sensing behaviors of GnPs/epoxy sensor and health monitoring for composite materials under monotonic tensile and cyclic deformation[J]. Composites Science and Technology, 2018, 158 (5): 94- 100

[17]	JEONG Y R, PARK H, JIN S W, et al Highly stretchable and sensitive strain sensors using fragmentized graphene foam[J]. Advanced Functional Materials, 2015, 25 (27): 4228- 4236 doi: 10.1002/adfm.201501000

[18]	LIU Q, CHEN J, LI Y, et al High-performance strain sensors with fish scale-like graphene sensing layers for full-range detection of human motions[J]. ACS Nano, 2016, 10 (8): 7901- 7906 doi: 10.1021/acsnano.6b03813

[19]	BONAVOLONTÀ C, CAMERLINGO C, CAROTENUTO G, et al Characterization of piezoresistive properties of graphene-supported polymer coating for strain sensor applications[J]. Sensors and Actuators A: Physical, 2016, 252: 26- 34 doi: 10.1016/j.sna.2016.11.002

[20]	吴志强, 卫军, 董荣珍石墨烯压阻复合材料及其在裂纹监测中的应用[J]. 浙江大学学报: 工学版, 2020, 54 (2): 233- 240 WU Zhiqiang, WEI Jun, DONG Rongzhen Graphene-based piezoresistive composite and application in crack monitoring[J]. Journal of Zhejiang University: Engineering Science, 2020, 54 (2): 233- 240

[21]	SIMMONS J G Incorporation of electric-field penetration of the electrodes in the theory of electron tunnelling through a dielectric layer[J]. British Journal of Applied Physics, 2002, 18 (3): 269

[22]	YANG X, QIU L, CHENG C, et al Ordered gelation of chemically converted graphene for next-generation electroconductive hydrogel films[J]. Angewandte Chemie-International Edition, 2011, 50 (32): 7325- 7328 doi: 10.1002/anie.201100723

[23]	JOHNSON D W, DOBSON B P, COLEMAN K S A manufacturing perspective on graphene dispersions[J]. Current Opinion in Colloid and Interface Science, 2015, 20 (5/6): 367- 382 doi: 10.1016/j.cocis.2015.11.004

[24]	王晨, 燕绍九, 南文争, 等表面活性剂对高浓度石墨烯水分散液制备的影响[J]. 材料工程, 2019, 47 (7): 50- 56 WANG Chen, YAN Shaojiu, NAN Wenzheng, et al Effect of surfactants on preparation of high concentration graphene aqueous dispersion[J]. Journal of Materials Engineering, 2019, 47 (7): 50- 56 doi: 10.11868/j.issn.1001-4381.2018.000566

[25]	POORSARGOL M, ALIMOHANNADIAN M, SOHRABI B, et al Dispersion of graphene using surfactant mixtures: experimental and molecular dynamics simulation studies[J]. Applied Surface Science, 2019, 464 (2): 440- 450

[26]	LIU X, KUANG W, GUO B Preparation of rubber/graphene oxide composites with in-situ interfacial design[J]. Polymer, 2015, 56 (1): 553- 562

[27]	郭玲梅, 汪洋, 徐伟芳硅橡胶拉伸行为的应变率相关性测试和表征[J]. 高压物理学报, 2019, 33 (5): 93- 99 GUO Lingmei, WANG Yang, XU Weifang Experimental investigation and modeling of strain-rate dependence on tensile behavior of silicone rubbers[J]. Chinese Journal of High Pressure Physics, 2019, 33 (5): 93- 99 doi: 10.11858/gywlxb.20180664

[1]	Meng GUO,Yu-ru ZHANG,Xing WEI,Wen-jing WANG. Graphene modification in amine-functionalized SBA-15 and its CO₂ adsorption capacity[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(8): 1588-1596.

[2]	Wang-yu REN,Shi-cheng HOU,Xiao-nan JIANG,Wei-xiang CHEN. MoS₂/B-doped graphene for electrochemical hydrogen evolution and lithium storage[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(8): 1628-1636.

[3]	Zhi-qiang WU,Jun WEI,Rong-zhen DONG. Graphene-based piezoresistive composite and application in crack monitoring[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(2): 233-240.

[4]	Qing ZHU,Wang-yu REN,Xiao-nan JIANG,Wei-xiang CHEN. Synthesis of Bi₂S₃-MoS₂/graphene hybrids and their electrochemical lithium storage performances[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(7): 1306-1314.

[5]	XIAO Bing-gang, XIE Zhi-yi, SUN Run-liang. Design and analysis of graphene-based isolator[J]. Journal of ZheJiang University (Engineering Science), 2015, 49(1): 42-47.

[6]	WANG Xiao,YAO Xiao-li,HOU Jian-feng,FAN Li-wu,XU Xu,YU Zi-tao,HU Ya-cai. Non-isothermal crystallization of aqueous graphene oxide suspensions[J]. Journal of ZheJiang University (Engineering Science), 2014, 48(7): 1272-1277.

Viewed

Full text

Abstract

Cited

Shared

Discussed