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基于RSM和SVR-IDBO的柔性压力传感单元灵敏度性能预测与优化

唐颖 陈垲 何宋西莹 严少秋 雷建勇 林远长 何国田

唐颖, 陈垲, 何宋西莹, 等. 基于RSM和SVR-IDBO的柔性压力传感单元灵敏度性能预测与优化[J]. 复合材料学报, 2024, 42(0): 1-15.
引用本文: 唐颖, 陈垲, 何宋西莹, 等. 基于RSM和SVR-IDBO的柔性压力传感单元灵敏度性能预测与优化[J]. 复合材料学报, 2024, 42(0): 1-15.
TANG Ying, CHEN Kai, HE Songxiying, et al. Sensitivity performance prediction and optimisation of flexible pressure sensing unit based on RSM and SVR-IDBO[J]. Acta Materiae Compositae Sinica.
Citation: TANG Ying, CHEN Kai, HE Songxiying, et al. Sensitivity performance prediction and optimisation of flexible pressure sensing unit based on RSM and SVR-IDBO[J]. Acta Materiae Compositae Sinica.

基于RSM和SVR-IDBO的柔性压力传感单元灵敏度性能预测与优化

基金项目: 国家重点研发计划(2018 YFB1306601);重庆市在渝高校与中科院所属院所合作项目(HZ2021011);重庆市技术创新与应用发展专项(cstc2021 jscx-cylhX0009)
详细信息
    通讯作者:

    何国田,博士,二级教授、正高级工程师,博士生导师,研究方向为智能机器人、触觉传感器、复合材料 E-mail: heguotian@cigit.ac.cn

  • 中图分类号: TP3-05

Sensitivity performance prediction and optimisation of flexible pressure sensing unit based on RSM and SVR-IDBO

Funds: National Key R&D Program (2018 YFB1306601); Chongqing Municipal Cooperation Project between Chongqing Universities and Institutes Affiliated to the Chinese Academy of Sciences (HZ2021011); Chongqing Municipal Special Project for Technological Innovation and Application Development (cstc2021 jscx-cylhX0009)
  • 摘要: 为优化柔性压力传感单元制备工艺,提升传感器灵敏度特性。本研究通过建立灵敏度解析模型,确定了影响其性能表现的主要因素,采用机械共混的方式,通过调节碳纳米管(CNT)、多层石墨烯(MLG)、搅拌时间(Mixing time)、成型温度(Molding temperature)等参数优化了柔性传感单元的灵敏度性能。首先在单因素分析的基础上,应用实验设计(DOE)中的中心复合实验方法(CCD)进行多因素实验设计,通过响应面法(RSM)和支持向量机(SVR)对多因素的交互影响进行了分析,并分别建立了灵敏度预测模型。其次根据决定系数($ {R}^{2} $)、均方根误差(Rmse)和平均误差率(Mae)对两种模型进行评估与定型,模型性能对比结果表明,通过超参优化后的SVR模型表现出更高水平的准确性和可预测性。然后基于改进的蜣螂优化算法(IDBO)对模型进行迭代优化,得到了比早期实验更好的灵敏度性能。仿真结果显示在0~30kPa的单轴压力下,当CNTs含量为2.3wt%、MLG含量为1.9wt%、混合时间15 min、成型温度78℃时,灵敏度达到0.5512 kPa−1,经过实验验证,与实际灵敏度(0.5371 kPa−1)的相对误差为2.625%,且与同类型研究相比较,本研究的传感单元灵敏度性能也处较高水平。证明该方法有助于寻找最佳的传感器含量配比与制备工艺,提升实验效率,节约实验成本,为快速制备高性能电容式柔性压力传感单元提供了新思路。

     

  • 图  1  触觉敏感单元制备流程图

    Figure  1.  Flow Chart for Preparation of Pressure Sensor

    图  2  柔性触觉敏感单元样品

    Figure  2.  Samples of Flexible Tactile Sensitive Units

    图  3  触觉敏感单元改性前后对比

    Figure  3.  Comparison between before and after modification of the tactile sensitivity unit

    图  4  压力传感器性能测试系统

    Figure  4.  pressure sensor performance test system

    图  5  渗滤复合材料作为介电层的柔性电容式压力传感器的工作原理图

    Figure  5.  Working principle diagram of flexible capacitive pressure sensor with percolation composite as dielectric layer

    图  6  输入参数的单因素影响:(a) CNTs单因素影响;(b) MLGs单因素影响;(c) 搅拌时间单因素影响;(d) 成型温度单因素影响

    Figure  6.  Single factor effect of input parameter:(a) single-factor effect of CNTs; (b) single-factor effect of MLGs; (c) single-factor effect of mixing time; (d) single-factor effect of molding temperature

    图  7  SVR模型示意图

    Figure  7.  SVR model diagram

    图  8  K折交叉验证示意图

    Figure  8.  K sketch map of cross validation

    图  9  蜣螂优化算法流程图

    Figure  9.  Dung beetle optimization algorithm flowchart

    图  10  改进蜣螂优化算法流程图

    Figure  10.  Improved dung beetle optimization algorithmflowchart

    图  11  算法性能对比收敛曲线图

    Figure  11.  Algorithm Performance Comparison Convergence Plot

    图  12  灵敏度三维响应面图:(a)碳纳米管(CNT)/石墨烯(MLG)相互作用;(b)CNT/搅拌时间相互作用;(c)CNT/成型温度相互作用;(d)MLG/搅拌时间相互作用;(e)MLG/成型温度相互作用;(f)搅拌/成型温度相互作用

    Figure  12.  Three-dimensional response surface plots of sensitivity: (a) Interaction between CNT/MLG content; (b) Interaction between CNT/mixing time; (c) Interaction between CNT/molding temperature; (d) Interaction between MLG/mixing time; (e) Interaction between MLG/molding temperature; (f) Interaction between mixing time/molding temperature

    图  13  SVR模型最佳参数优化结果

    Figure  13.  Optimal parameter optimization results of SVR model

    图  14  SVR和RSM模型的5折交叉验证的雷达图

    Figure  14.  Radar diagrams of SVR and RSM models with 5 fold cross validation

    图  15  改进蜣螂优化算法(IDBO)收敛过程

    Figure  15.  Dung beetle optimization algorithm (IDBO) convergence process

    图  16  灵敏度测试电容输出响应曲线

    Figure  16.  Sensitivity test capacitor output response curve

    图  17  触觉单元相关性能测试图

    Figure  17.  Haptic unit related performance graphs

    表  1  单因素实验方案

    Table  1.   Single-factor protocol

    IndexContent of CNTs/gContent of MLGs/gMixing time/minMolding temperature/℃Analysis of factors affecting
    10.4,0.5,0.6,0.7,0.8,0.9,1.0,1.10.61580CNT
    20.70.4,0.5,0.6,0.7,0.8,0.9,1.0,1.11580MLG
    30.70.610,15,20,25,3080Mixing time
    40.70.61540,60,80,100,120Molding temperature
    下载: 导出CSV

    表  2  四个因素的水平和编码

    Table  2.   Level and code of four factors

    Code Levels Factor
    CNTs/g MLGs/g Mixing time/min Molding temperature/℃
    −2 0.7 0.6 10 40
    −1 0.8 0.7 15 60
    0 0.9 0.8 20 80
    1 1.0 0.9 25 100
    2 1.1 1.0 30 120
    下载: 导出CSV

    表  3  多因素中心组合实验设计(CCD)与结果

    Table  3.   Multifactorial central combinatorial experimental design (CCD) and results

    Run Factors Sensitivity
    CNT MLG Mixing time Molding temperature
    1 1 −1 −1 1 0.227
    2 0 0 2 0 0.13
    3* 0 0 0 0 0.118
    4 −1 −1 −1 1 0.058
    5 1 1 1 1 0.191
    6 1 −1 −1 −1 0.208
    7 −1 1 1 1 0.025
    8* 0 0 0 0 0.145
    9 −1 1 −1 1 0.05
    10 −1 −1 1 −1 0.125
    11 −2 0 0 0 0.05
    12 0 −2 0 0 0.185
    13 0 0 −2 0 0.193
    14 −1 1 1 −1 0.164
    15 1 1 −1 −1 0.375
    16 1 −1 1 −1 0.18
    17* 0 0 0 0 0.135
    18 1 1 1 −1 0.31
    19 0 1 0 0 0.198
    20 2 0 0 0 0.478
    21 −1 1 −1 −1 0.178
    22 0 0 0 0 0.154
    23 1 1 −1 1 0.326
    24 0 0 0 −2 0.124
    25* 0 0 0 0 0.117
    26* 0 0 0 0 0.112
    27 −1 −1 −1 −1 0.078
    28 1 −1 1 1 0.248
    29 −1 −1 1 1 0.09
    30 0 0 0 2 0.073
    Note: *indicates the central repeated trials.
    下载: 导出CSV

    表  4  灵敏度响应的方差分析

    Table  4.   Analysis of variance for sensitivity response

    SourceSum ‍of ‍squaresdfMean‍ squareF-valueP-valueStatus
    Model0.2877140.020532.91< 0.0001Significant
    $ {x}_{1} $-CNT0.193110.1931309.36< 0.0001
    $ {x}_{2} $-MLG0.011710.011718.7890.0006
    $ {x}_{3} $-Mixing time0.003610.00365.730.0302
    $ {x}_{4} $-Temperature0.010610.010617.020.0009
    $ {x}_{1}{x}_{2} $0.004710.00477.460.0155
    $ {x}_{1}{x}_{3} $0.003810.00386.110.0259
    $ {x}_{1}{x}_{4} $0.003610.00365.810.0292
    $ {x}_{2}{x}_{3} $0.006010.00609.680.0071
    $ {x}_{2}{x}_{4} $0.013610.013621.830.0003
    $ {x}_{3}{x}_{4} $0.000110.00010.22110.6449
    $ {{x}_{1}}^{2} $0.026710.026742.79< 0.0001
    $ {{x}_{2}}^{2} $0.008510.008513.650.0022
    $ {{x}_{3}}^{2} $0.000810.00081.260.2795
    $ {{x}_{4}}^{2} $0.003010.00304.880.0432
    Residual0.0094150.0006
    Lack of fit0.0079100.00082.700.1422Not significant
    Pure error0.001550.0003
    Cor total0.297029
    $ {R}^{2} $0.9685
    $ {R}_{\mathrm{A}\mathrm{d}\mathrm{j}}^{2} $0.9390
    $ {R}_{\mathrm{P}\mathrm{r}\mathrm{e}\mathrm{d}}^{2} $0.8311
    C.V%14.86
    Adeq precision23.8126
    Notes: Cor total refers to the total number of correlation coefficients;$ {R}_{\mathrm{A}\mathrm{d}\mathrm{j}}^{2} $indicates adjusted coefficient of determination;$ {R}_{\mathrm{P}\mathrm{r}\mathrm{e}\mathrm{d}}^{2} $ indicates predicted R-squared; Adeq precision is an important statistical metric for evaluating the predictive ability of response surface models.
    下载: 导出CSV

    表  5  SVR模型的5折交叉验证预测性能评价指标

    Table  5.   Performance evaluation index of SVR model by cross validation

    Index 1 2 3 4 5 Average
    $ {R}^{2} $ 0.9893 0.9841 0.9735 0.9769 0.9882 0.9824
    Rmse 0.0253 0.0328 0.0354 0.0389 0.0188 0.0320
    Mae 0.0193 0.0301 0.0231 0.0321 0.0151 0.0239
    下载: 导出CSV

    表  6  RSM模型的5折交叉验证预测性能评价指标

    Table  6.   Performance evaluation index of RSM model by cross validation

    Index 1 2 3 4 5 Average
    R2 0.9563 0.9642 0.9778 0.9669 0.9782 0.96868
    Rmse 0.04002 0.02701 0.03103 0.04731 0.03312 0.035698
    Mae 0.03623 0.02443 0.02671 0.03113 0.02608 0.028916
    下载: 导出CSV

    表  7  传感单元灵敏度性能对比

    Table  7.   Comparison of sensitivity performance of sensing units

    ReferenceMethodSensitivity /k$ \mathrm{P}{\mathrm{a}}^{-1} $Pressure range /$ \mathrm{k}\mathrm{P}\mathrm{a} $Comparison /k$ \mathrm{P}{\mathrm{a}}^{-1} $
    [12]Microstructure design0.4510-250.7665
    [42]Microstructure design0.690-101.7531
    [43]Microstructure design1.010-250.7665
    [44]mechanical blending0.38500-300.5371
    [45]Microstructure design0.3410-51.9698
    This studymechanical blending0.53710-30-
    下载: 导出CSV
  • [1] GILIOPOULOS D, ZAMBOULIS A, GIANNAKOUDAKIS D, et al. Polymer/Metal Organic Framework (MOF) Nanocomposites for Biomedical Applications[J]. Molecules, 2020, 25(1): 185. doi: 10.3390/molecules25010185
    [2] SHAH V, BHALIYA J, PATEL G M, et al. Advances in polymeric nanocomposites for automotive applications: A review[J]. Polymers for Advanced Technologies, 2022, 33(10): 3023-3048. doi: 10.1002/pat.5771
    [3] CHEN S, YANG K, LENG X, et al. Perspectives in the design and application of composites based on graphene derivatives and bio-based polymers[J]. Polymer International, 2020, 69(12): 1173-1186. doi: 10.1002/pi.6080
    [4] LIU J, HUI D, LAU D. Two-dimensional nanomaterial-based polymer composites: Fundamentals and applications[J]. Nanotechnology Reviews, 2022, 11(1): 770-792. doi: 10.1515/ntrev-2022-0041
    [5] ZHU Y, LIU B, WANG S, et al. Development of a FBG Stress Sensor for Geostress Measurement Using RSR Method in Deep Soft Fractured Rock Mass[J]. Applied Sciences, 2022, 12(4): 1781. doi: 10.3390/app12041781
    [6] ASYRAF M R M, ISHAK M R, SYAMSIRA, et al. Mechanical properties of oil palm fibre-reinforced polymer composites: A review[J]. Journal of Materials Research and Technology, 2022, 17: 33-65. doi: 10.1016/j.jmrt.2021.12.122
    [7] DU H, FANG C, ZHANG J, et al. Segregated carbon nanotube networks in CNT-polymer nanocomposites for higher electrical conductivity and dielectric permittivity, and lower percolation threshold[J]. International Journal of Engineering Science, 2022, 173: 103650. doi: 10.1016/j.ijengsci.2022.103650
    [8] ZHANG F, FENG Y, QIN M, et al. Stress controllability in thermal and electrical conductivity of 3D elastic graphene-crosslinked carbon nanotube sponge/polyimide nanocomposite[J]. Advanced Functional Materials, 2019, 29(25): 1901383. doi: 10.1002/adfm.201901383
    [9] MOUSSA M, EL-KADY M F, ABDEL-AZEMS, et al. Compact, flexible conducting polymer/graphene nanocomposites for supercapacitors of high volumetric energy density[J]. Composites Science and Technology, 2018, 160: 50-59. doi: 10.1016/j.compscitech.2018.02.033
    [10] LI X, XU W, XIN Y, et al. Supramolecular polymer nanocomposites for biomedical applications[J]. Polymers, 2021, 13(4): 513. doi: 10.3390/polym13040513
    [11] 赵梁成, 李斌, 武思蕊, 等. 功能三维石墨烯-多壁碳纳米管/热塑性聚氨酯复合材料的制备及性能[J]. 复合材料学报, 2020, 37(2): 242-251.

    ZHAO Liangcheng, LI Bin, WU Sirui, et al. Preparation and properties of functional three-dimensional graphene-multi-walled carbon nanotubes/thermoplastic polyurethane composites[J]. Journal of Composite Materials, 2020, 37(2): 242-251(in Chinese).
    [12] 药芳萍, 黎相孟, 石强盛杰, 等. 石墨烯-聚合物柔性电容传感器制备及性能研究[J]. 中国机械工程, 2021, 32(24): 2909-2914. doi: 10.3969/j.issn.1004-132X.2021.24.002

    YAO Fangping, LI Xiangmeng, SHI Qiangshengjie, et al. Preparation and performance of graphene-polymer flexible capacitive sensors[J]. China Mechanical Engineering, 2021, 32(24): 2909-2914(in Chinese). doi: 10.3969/j.issn.1004-132X.2021.24.002
    [13] XU B, YE F, CHEN R, et al. A wide sensing range and high sensitivity flexible strain sensor based on carbon nanotubes and MXene[J]. Ceramics International, 2022, 48(7): 10220-10226. doi: 10.1016/j.ceramint.2021.12.235
    [14] 吴其皓, 廖鹏飞, 茅寅, 等. 高灵敏度电容式柔性压力传感器的设计与优化[J]. 传感器与微系统, 2023, 42(4): 107-110.

    WU Qihao, LIAO Pengfei, MAO Yin, et al. Design and Optimization of High Sensitivity Capacitive Flexible Pressure Sensors[J]. Sensors and Microsystems, 2023, 42(4): 107-110(in Chinese).
    [15] MA Z, ZHANG K, YANG S, et al. High-performancecapacitive pressure sensors Fabricated by introducing dielectric filler and conductive filler into a porous dielectric layer through a Biomimic strategy[J]. Composites Science and Technology, 2022, 227: 109595. doi: 10.1016/j.compscitech.2022.109595
    [16] KE K, MCMASTER M, CHRISTOPHERSON W, et al. Highly sensitive capacitive pressure sensors basedon elastomer composites with carbon filler hybrids[J]. Composites Part A: Applied Science and Manufacturing, 2019, 126: 105614. doi: 10.1016/j.compositesa.2019.105614
    [17] BI G, XIAO B, LIN Y, et al. Modeling and Optimization of Sensitivity and Creep for Multi-Component Sensing Materials[J]. Nanomaterials, 2023, 13(2): 298. doi: 10.3390/nano13020298
    [18] 王苏, 白元元, 王书琪, 等. 基于MXene/多壁碳纳米管的柔性压力传感器[J]. 传感器与微系统, 2022, 41(7): 5-8.

    WANG Su, BAI Yuanyuan, WANG Shuqi, et al. Flexible pressure sensor based on MXene/multi-walled carbon nanotubes[J]. Sensors and Microsystems, 2022, 41(7): 5-8(in Chinese).
    [19] CHEN X, WEI Z, HE K. An Estimation of the Discharge Exponent of a Drip Irrigation Emitter by Response Surface Methodology and Machine Learning[J]. Water, 2022, 14(7): 1034. doi: 10.3390/w14071034
    [20] 胡欣颖, 李洪军, 李少博, 等. 对比研究响应面法和BP神经网络-粒子群算法优化调理松板肉加工工艺[J]. 食品与发酵工业, 2019, 45(24): 179-187.

    HU Xinying, LI Hongjun, LI Shaobo, et al. Comparative study of response surface methodology and BP neural network-particle swarm algorithm for optimization of seasoned pine board meat processing process[J]. Food and Fermentation Industry, 2019, 45(24): 179-187(in Chinese).
    [21] LI X, CHEN S, ZHANG J, et al. Optimization of Ultrasonic-Assisted Extraction of Active Components and Antioxidant Activity from Polygala tenuifolia: A Comparative Study of the Response Surface Methodology and Least Squares Support Vector Machine[J]. Molecules, 2022, 27(10): 3069. doi: 10.3390/molecules27103069
    [22] XUE J, SHEN B. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization[J]. The Journal of Supercomputing, 2023, 79(7): 7305-7336. doi: 10.1007/s11227-022-04959-6
    [23] MIRJALILI S. Genetic Algorithm[M/OL]//MIRJALILI S. Evolutionary Algorithms and Neural Networks: Theory and Applications. Cham: Springer International Publishing, 2019: 43-55.
    [24] KENNEDY J, EBERHART R. Particle swarm optimization[C/OL]//Proceedings of ICNN’95 - International Conference on Neural Networks, 1995: 1942-1948.
    [25] 隋东, 杨振宇, 丁松滨, 等. 基于EMSDBO算法的无人机三维航迹规划[J]. 系统工程与电子技术, 2024: 1-13.

    SUI Dong, YANG Zhenyu, DING Songbin, et al. Three-dimensional trajectory planning for unmanned aircraft based on EMSDBO algorithm[J]. Systems Engineering and Electronics, 2024: 1-13. (in Chinese).
    [26] 周亚中, 何怡刚, 邢致恺, 等. 基于IDBO-ARIMA的电力变压器振动信号预测[J]. 电子测量与仪器学报, 2023, 37(8): 11-20.

    ZHOU Yazhong, HE Yigang, XING Zhikai, et al. Vibration signal prediction of power transformer based on IDBO-ARIMA[J]. Journal of Electronic Measurement and Instrumentation, 2023, 37(8): 11-20 (in Chinese).
    [27] KHAN M Z, YOUSUF R I, SHOAIB M H, et al. A hybrid framework of artificial intelligence-based neural network model (ANN) and central composite design (CCD) in quality by design formulation development of orodispersible moxifloxacin tablets: Physicochemical evaluation, compaction analysis, and its in-silico PBPK modeling[J]. Journal of Drug Delivery Science and Technology, 2023, 82: 104323. doi: 10.1016/j.jddst.2023.104323
    [28] 王建军, 白鹏翔, 董杰, 等. 基于硅烷偶联剂改性的炭黑-石墨烯在天然胶中的性能研究[J]. 橡塑技术与装备, 2023, 49(4): 16-20.

    WANG Jianjun, BAI Pengxiang, DONG Jie, et al. Study on the properties of carbon black-graphene in natural rubber based on silane coupling agent modification[J]. Rubber and Plastic Technology and Equipment, 2023, 49(4): 16-20.
    [29] 田玉玉, 何韧, 吴菊英, 等. 电容式柔性压力传感器的性能优化原理及研究进展[J]. 材料导报, 2023, (16): 1-23.

    TIAN Yuyu, HE Ren, WU Juying, et al. Principles of performance optimization and research progress of capacitive flexible pressure sensors[J]. Materials Herald, 2023, (16): 1-23(in Chinese).
    [30] XU J, WONG C P. Low-loss percolative dielectric composite[J]. Applied Physics Letters, 2005, 87(8): 082907. doi: 10.1063/1.2032597
    [31] SHEN Y, LIN Y H, NAN C W. Interfacial Effect on Dielectric Properties of Polymer Nanocomposites Filled with Core/Shell-Structured Particles[J]. Advanced Functional Materials, 2007, 17(14): 2405-2410. doi: 10.1002/adfm.200700200
    [32] YANG M, LI W, ZHAO Z, et al. Micromechanical modeling for the temperature-dependent yield strength of polymer-matrix nanocomposites[J]. Composites Science and Technology, 2022, 220: 109265. doi: 10.1016/j.compscitech.2022.109265
    [33] KESHTEGAR B, HEDDAM S, SEBBAR A, et al. SVR-RSM: a hybrid heuristic method for modeling monthly pan evaporation[J]. Environmental Science and Pollution Research, 2019, 26(35): 35807-35826. doi: 10.1007/s11356-019-06596-8
    [34] 张娜, 赵泽丹, 包晓安, 等. 基于改进的Tent混沌万有引力搜索算法[J]. 控制与决策, 2020, 35(4): 893900.

    ZHANG Na, ZHAO Zedan, BAO Xiao‘an, et al. Improved Tent-based chaotic universal gravity search algorithm[J]. Control and Decision Making, 2020, 35(4): 893900 (in Chinese).
    [35] 宁杰琼, 何庆. t-分布扰动策略和变异策略的花授粉算法[J]. 小型微型计算机系统, 2021, 42(1): 64-70. doi: 10.3969/j.issn.1000-1220.2021.01.012

    NING Jieqiong, HE Qing. Flower pollination algorithms with t-distribution perturbation strategy and variation strategy[J]. Small Microcomputer Systems, 2021, 42(1): 64-70(in Chinese). doi: 10.3969/j.issn.1000-1220.2021.01.012
    [36] 郭琴, 郑巧仙. 多策略改进的蜣螂优化算法及其应用[J]. 计算机科学与探索, 2023: 1-22.

    GUO Qin, ZHENG Qiaoxian. Multi-strategy improved dung beetle optimization algorithm and its applications[J]. Computer Science and Exploration, 2023: 1-22.
    [37] LI S, LI J. Chaotic dung beetle optimization algorithm based on adaptive t-Distribution[C/OL]//2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). Chongqing, China: IEEE, 2023: 925-933[2024-01-24].https://ieeexplore.ieee.org/document/10165106/. DOI: 10.1109/ICIBA56860.2023.10165106.
    [38] 苏国士, 梁阚, 李虓, 等. 石墨烯/碳纳米管复合导电剂制备[J]. 炭素技术, 2022, 41(1): 23-27+40.

    SU Guoshi, LIANG Kan, LI Xiao, et al. Preparation of graphene/carbon nanotube composite conductive agent[J]. Carbon Technology, 2022, 41(1): 23-27+40(in Chinese).
    [39] 苏志强, 张晓媛, 赵鑫, 等. 基于碳纳米管/石墨烯界面修饰的碳纤维增强复合材料中力-电耦合关系探究[J]. 长春工业大学学报, 2022, 43(Z1): 354-359.

    SU Zhiqiang, ZHANG Xiaoyuan, ZHAO Xin, et al. Investigation of force-electricity coupling relationship in carbon fiber reinforced composites based on carbon nanotube/graphene interfacial modification[J]. Journal of Changchun University of Technology, 2022, 43(Z1): 354-359(in Chinese).
    [40] COLONNA S, BERNAL M M, GAVOCI G, et al. Effect of processing conditions on the thermal and electrical conductivity of poly (butylene terephthalate) nanocomposites prepared via ring-opening polymerization[J]. Materials & Design, 2017, 119: 124-132.
    [41] JAHANI Y, BAENA M, BARRIS C, et al. Influence of curing, post-curing and testing temperatures on mechanical properties of a structural adhesive[J]. Construction and Building Materials, 2022, 324: 126698. doi: 10.1016/j.conbuildmat.2022.126698
    [42] 赵珂. 基于三维竖直石墨烯的微结构柔性压力传感器[D]. 山西大学, 2023.

    ZHAO Ke. Microstructured flexible pressure sensor based on 3D vertical graphene[D]. Shanxi University, 2023 (in Chinese).
    [43] 王菲菲, 彭海益, 姚晓刚. 基于多向冷冻法制备的高灵敏度柔性电容式压力传感器[J]. 复合材料学报, 2023, 40(5): 2680-2687.

    WANG Feifei, PENG Haiyi, YAO Xiaogang. Highly sensitive flexible capacitive pressure sensor prepared based on multidirectional freezing method[J]. Journal of Composite Materials, 2023, 40(5): 2680-2687(in Chinese).
    [44] YAN S, TANG Y, BI G, et al. Optimal Design of Carbon-Based Polymer Nanocomposites Preparation Based on Response Surface Methodology[J]. Polymers, 2023, 15(6): 1494. doi: 10.3390/polym15061494
    [45] 张鹏, 陈昱丞, 张建, 等. 基于双层微结构电极的柔性电容式压力传感器[J]. 仪表技术与传感器, 2020, (5): 11-13+17. doi: 10.3969/j.issn.1002-1841.2020.05.003

    ZHANG Peng, CHEN Yucheng, ZHANG Jian, et al. Flexible capacitive pressure sensor based on double-layer microstructured electrodes[J]. Instrumentation Technology and Sensors, 2020, (5): 11-13+17(in Chinese). doi: 10.3969/j.issn.1002-1841.2020.05.003
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出版历程
  • 收稿日期:  2024-02-26
  • 修回日期:  2024-04-07
  • 录用日期:  2024-04-19
  • 网络出版日期:  2024-05-28

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