Research progress of flexible nanocomposites for piezoresistive strain sensors
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摘要: 柔性纳米复合材料，由于其优异的传感性能、良好的延展性，在压阻式应变传感器方面，受到研究者们不断的关注，主要应用于智能可穿戴，结构健康监测等领域。本文主要从结构设计、制备方法、应用前景三方面，综述了近年来国内外对复合材料压阻式应变传感器在材料选取及结构设计下传感器性能的研究成果及研究进展。首先分析了压阻式传感器的性能参数和传感机制，其次围绕材料优化选取-工艺选择与结构设计-性能与具体应用的主线，进行了深入的讨论与性能分析。由于复合材料传感器的微观导电网络结构与介观层状结构协同主导着传感性能的演变，采用有限元模拟理论与人工智能算法，对传感器的导电网络结构(形成、搭接、演化)、多尺度层状结构设计等方面进行了传感性能的调控与机制分析。最后本文探讨了柔性压阻式传感器的多功能化、信号鉴别与分离、高稳定性与低迟滞性等依然亟需突破与提升的问题，并同时总结了目前复合材料压阻式应变传感器研究工作中的重点及未来的发展方向。Abstract: Flexible nanocomposite piezoresistive strain sensors are becoming more and more attractive owing to their unprecedent merits in sensing performance and stretchability, which can be feasibly employed in the fields of wearable electronics and structural health monitoring.Inthiswork, three aspects including structural design, preparation methods and application prospects are briefly reviewed, where recent progress of composite piezoresistive strain sensors is mainly discussed by centering on materials chosen and structure configuration towards the sensing improvement. Crucial parameters in affecting the sensing performance as well as sensing mechanism have been initially analyzed in composite piezoresistive strain sensors. Subsequently, detailed discussion and analysis were performed on the recent advances of composite strain sensors by focusing on the main guideline of materials optimization-structural design/processing-performance improvement and practical applications. Recent achievements of finite element analysis (FEA) and artificial intelligence algorithm are also introduced to conduct the controlling and mechanism analysis of conductive network design (formation, overlapping and evolution) and multi-scale layered structural configuration in composite strain sensors, where both conductive network and structural configuration are playing a significant role in changing the sensing performance. Consequently, a multi-modality sensor with distinct sensing signal, high stability and low hysteresis is urgently needed to fulfill the demands of flexible nanocomposites for piezoresistive strain sensors, where main points towards future developments and future directions in composites strains sensors are also highlighted.
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图 1 (a) 初始状态下多壁碳纳米管(MWCNTs)的空间关系和阻力类型的示意性截面图[25]；(b) rGO/CNFs/PDM复合物的准静态拉伸应力-应变曲线[28]；(c) 一种集成了8种功能的传感器阵列[29]；(d)传感器电阻变化(ΔR/R0)vs应变(蓝线表示实验数据，红线表示分段线性拟合)[30]；(e) 制备好的离子-液基波(ILBW)应变传感器的照片((i) 波浪形结构，(ii) 良好的透明度，(iii) 超拉伸性)；(f) 离子-液基波型传感器的电阻响应具有较强的确定性，且无迟滞现象(图1(f)插图)，与理论估计曲线吻合较好，可用于人体检测[16]
Figure 1. (a) Schematic of the initial state of the spatial relationship and resistance of multi-walled carbon nanotubes (MWCNTs) typed strain sensor[25]; (b) Quasi-static tensile stress-strain curves of rGO/CNFs/PDMS composite strain sensors[28]; (c) An integrated sensor array with multifunctionalities[29]; (d) Relative resistance (ΔR/R0) as a function of applied strain[30]; (e) Image of the prepared iron-liquid base wave (ILBW) strainsensor ((i) Wavy structure, (ii) Good transparency, (iii) Hypertension); (f) Resistance response of the ion-liquid based wave sensor has strong determinism and no hysteresis (Fig.1(f) illustration), which is in good agreement with the theoretical estimated curve and can be used for human body detection[16]
图 6 纳米复合物应变传感器的制备：(a) 氧化石墨烯(GO)与多碳纳米管混合制备纤维素/还原氧化石墨烯(RGO应变传感器的示意图[28]；(b) 静电纺丝制备碳纳米管(CNTs)包覆的聚氨酯，接着用聚二甲基硅氧烷(PDMS)进行修饰，得到应变达500%的高应变传感器[30]；(c) 基于棉氨纶混纺织物的化学镀银传感器，所得织物具有较高的灵敏度，灵敏因子可达26.11[49]；(d) 使用压塑成型法制备的MWCNTs/硅流体(SF)/PDMS传感器示意图[25]；(e) 纳米碳素/聚氨酯海绵组装而成的应变传感器结构说明图[50]；(f) 通过挤压成型法制备的MWCNTs/PDMS复合薄膜的制备工艺[51]
Figure 6. Fabrication of organic-inorganic nanocomposite strain sensors: (a) Schematic diagram of a cellulose/reduced graphene oxide (RGO) strain sensor made by mixing graphene oxide (GO) with multi-carbon nanotubes[28]; (b) Coating carbon nanotubes (CNTs) with polyurethane through electrostatic spinning, followed by polydimethylsiloxane (PDMS) modification to obtain a 500% high-strain sensor[30]; (c) Electroless silver-plated sensor based on cotton/spandex blended fabric, resulting in a high sensitivity with measured gauge factor up to 26.11[49]; (d) Schematic diagram of MWCNTs/silicon fluid (SF) /PDMS sensor prepared by compression molding method[25]; (e) Diagram to illustrate the structure of the strain sensor assembled from carbon nano/ polyurethane foam[50]; (f) MWCNTs/PDMS composite films prepared by extrusion[51]
图 7 (a) 制造单壁碳纳米管(SWCNT)应变传感器的关键制备步骤；(b) 应变传感器(定向排布的SWCNTs、随机取向SWCNTs和常规金属薄膜的电阻随应变的变化[53]；(c) CNF包覆糖模板制备的多孔纳米复合材料工艺示意图；(d) 低、高压缩应变下纳米复合材料的传感机制[54]
Figure 7. (a) Key preparation steps for fabrication of single-walled carbon nanotube (SWCNT) strain sensors; (b) Resistance of strain sensors (directionally arranged SWCNTs, randomly oriented SWCNTs, and conventional metal films as a function of strain[53]; (c) Process diagram of porous nanocomposites prepared by CNF coated sugar template; (d) Sensing mechanism of nanocomposites under low and high compressive strain[54]
图 8 (a) 多纤维应变传感器制备工艺；(b) 应力应变曲线图；(c) 传感器在20%应变下的SEM图像[79]；(d) 多孔纳米复合材料的合成结构示意图；(e) 多孔结构的微观形貌及SEM图像；(f) 多孔纳米复合材料的SEM横截面图像及扫SEM内表面放大图，显示CNTs的分布情况[80]
Figure 8. (a) Preparation process of multi-fiber strain sensor; (b) Stress-strain curves; (c) SEM images of the sensor under 20% strain[79]; (d) Synthesis structure diagram of porous nanocomposites; (e) Micromorphology of the porous structure and SEM image; (f) SEM cross-sectional images and enlarged images of the inner surface of porous nanocomposites, showing the distribution of CNTs[80]
图 10 有限元分析(FEA)揭示了石墨烯与交叉堆叠CNTs阵列杂交后提升了应力传递：(a) 方形CNT网络的代表性模型，以PDMS为支撑基底，拉伸方向用箭头标注，设置与底层平行；((b)~(c)) 在石墨烯掺杂前后，米塞斯应力在方形CNTs网络中的分布。石墨烯耦合杂化CNTs(CeG)于传感器中形成的导电网络机制探究[83]；(d) 石墨烯搭接结构的FEA((i) PDMS上等边三角形CNT网络单元的示意图，箭头表示拉伸应变的方向；(ii) CNTs的横截面示意图；(iii) CNTs网络与(iv)石墨烯杂化的CNT搭接在20%应变下，最大主应变在位置1处的空间分布)；(e) CNTs网络的微观结构演变示意图；(f) CeG在拉伸和释放下的结构变化[84]；((g)~(h)) 表示有限元分析得到单位电阻器拉伸前后I、II、III区域的最大主应变分布情况((i) 通过二维有限元分析，确定了多孔纳米复合材料在相同的应力下，不同的厚度(t=0.5 mm、t=1 mm、t=0.25 mm)最大主应变地分布状况)[82]；(j) 无孔纳米复合材料在60%拉伸应变下(箭头表示加载方向)的应力分布示意图[82]
Figure 10. Finite element analysis (FEA) revealed improved stress transfer after hybridization of graphene with cross-forked stacked CNTs arrays: (a) A representative model of a square CNT network, with PDMS as the supporting substrate, stretching direction marked by arrows, set parallel to the bottom layer; ((b)-(c)) Distribution of Mises stress in a square CNTs network before and after graphene hybridization; (c) Research on the mechanism of conductive network formed by graphene-coupled hybrid CNTs (CeG) in sensors[83]; (d) FEA of graphene-bonded structures ((i) Schematic diagram of equilateral triangular CNTs network units on PDMS with arrows indicating the direction of tensile strain; (ii) Schematic diagram of cross section of CNTs; (iii) Spatial distribution of the maximum principal strain at position 1 when the network of CNTs overlaps with the graphene hybrid CNTs at 20% strain); (e) Schematic diagram of microstructure evolution of the CNTs network; (f) Structural changes of CeG under tension and release[84]; （(g)-(h)） Represent the maximum principal strain distribution in regions I, II and III before and after unit resistor stretching obtained by finite element analysis ((i) Distribution of maximum principal strain of porous nanocomposites with different thickness (t=0.5 mm, t=1 mm, t=0.25 mm) under the same stress was determined by two-dimensional finite element analysis)[82]; (j) Schematic diagram of stress distribution of nonporous nanocomposites under 60% tensile strain (arrow indicates loading direction)[82]
L—Pristine length; △L—Elongation length; εmax—Maximum principal strain
图 11 (a) 智能电子皮肤[85]；(b) 人工智能中的机电一体化；(c) 盲文图智能识别交互演示，显示Gr-GO异质结构薄膜压力传感器阵列的点字式压力分布，压力阵列通过蓝牙与外部设备进行无线通信；(d) 智能电子机器手，可用于人机交互[88]
Figure 11. (a) Intelligent skin electronics[85]; (b) Mechatronics in artificial intelligence; (c) Interactive demonstration of intelligent recognition of braille pictures, the right shows the braille pressure distribution of Gr-GO heterogeneous film pressure sensor array, the pressure array communicates wirelessly with an external device via Bluetooth; (d) Intelligent electronic prosthetic hands used for human-computer interaction[88]
图 12 柔性纳米复合材料应变传感器的设计
Figure 12. Configurated design of flexible nanocomposite strain sensors
表 1 不同种类导电纳米材料的传感器性能传感器材料的种类选择
Table 1. Sensing properties of different sensors with a variety of conductive nanomaterials
Material type Structure characteristics Dimensionality Distribution status Impact on performance Ref Metal nanowire High aspect ratio, easy to lap 1 Most of them exist in the form of overlapping Easy to form a stable conductive network, but its sensitivity is low [41-42] Conducting polymer Light weight, good flexibility, easy to process 1 The conductive network is stable and the bonding with the substrate is general Structure of conductive network is simple and has good linearity, but there is a certain hysteresis [43] CNT/carbon fiber High specific suface area 1 Arrangement is scattered and easy to be wound One-dimensional layered overlapping, low threshold value, good sensor stability [44-45] Graphene Layered structure, good electrical conductivity 2 Not easy to be dispersed, often overlapping in the laminated form/state Composed of lamellar conductive network, good linearity,high seansitivity [46] Note: CNT—Carbon nanotube. 
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表 2 不同材料制备的柔性纳米复合材料压阻式应变传感器传感性能对比
Table 2. Comparison of sensing behavior in flexible organic-inorganic composite strain sensors prepared via a variety of materials
Composite materials Synthetic method Strain gauge/% Gauge factor Cyclic performance Ref SWCNT/CB/silicone rubber/PDMS Spin coating 120 1.25 1100 [62] CNT/Epoxy resin Ultrasonic dispersion/casting method <2.83 4.45 5 [63] MWCNT/silicone rubber 3D printing 300 1.5 5 [64] MWCNT/TPU 3D printing fusion deposition model/Twin screw extrusion method 100 176 10 [65] Graphene/PTFE Solvent casting 1.5 – – [66] Graphene/TPU Ultrasonic mixing method 300 17.7 20 [67] Graphene/CNT/TPU Coacervation method/
compression molding30 35.78 20 [68] Graphene/rubber Electrostatic assembly 100 82.5 300 [69] CB/
Fluorocarbon elastomerHot melt mixing method 10 14 5 [70] CB/SBS Molding method 15 3.3 6 [71] CNT/spandex Weave 0.4 80 1000 [72] Graphene oxide aerogel Dry spinning 20 5 500 [73] Graphene organic fabric/PDMS CVD/dry −400 13.68 500 [74] Graphene/PI Freeze drying method 50 0.18 2 000 [75] GO/fiber glass Dip-coating method 16400 1 - [76] PU/cotton/CNT Multilayer coating method 40 - 3000 [77] CNT/copolyester CVD 900 64 10000 [78] Notes: CB—Carbon black; PTFE—Polytetrafluoroethylene; PDMS—Polydimethylsiloxane; TPU—Thermoplastic polyurethane; GO—
Graphene oxide; PU—Polyurethane; CVD—Chemical vapor deposition.
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