Research Article Cite This: ACS Appl. Mater. Interfaces 2019, 11, 23527−23534
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Scalable Manufactured Self-Healing Strain Sensors Based on IonIntercalated Graphene Nanosheets and Interfacial Coordination Yumeng Tang,† Quanquan Guo,† Zhenming Chen,‡ Xinxing Zhang,*,† Canhui Lu,*,† Jie Cao,† and Zhuo Zheng† †
State Key Laboratory of Polymer Materials and Engineering, Sichuan University, Chengdu 610065, China Guangxi Key Laboratory of Calcium Carbonate Resources Comprehensive Utilization, College of Materials & Environmental Engineering, Hezhou University, Hezhou 542899, China
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ABSTRACT: Desirable mechanical strength and self-healing performance are very important to highly sensitive and stretchable sensors to meet their practical applications. However, balancing these two key performance parameters is still a great challenge. Herein, we present a simple, large-scale, and cost-efficient route to fabricate autonomously self-healing strain sensors with satisfactory mechanical properties. Specifically, ion-intercalated mechanical milling was utilized to realize the large-scale preparation of graphene nanosheets (GNs). Then, a wellorganized GN-nanostructured network was constructed in a rubber matrix based on interfacial metal−ligand coordination. The resultant nanocomposites show desirable mechanical properties (∼5 times higher than that of control sample without interfacial coordination), excellent self-healing performance (even healable in various harsh conditions, for example, underwater, at subzero temperature or exposed in acidic and alkaline conditions), and ultrahigh sensitivity (gauge factor ≈ 45 573.1). The elaborately designed strain sensors offer a feasible approach for the scalable production of self-healing strain-sensing devices, making it promising for further applications, including artificial skin, smart robotics, and other electrical devices. KEYWORDS: self-healing, strain sensors, graphene nanosheets, ion intercalation, metal−ligand coordination
1. INTRODUCTION Flexible strain sensors with high sensitivity have been of considerable scientific interest recently owing to their broad promising applications in many fields, such as human motion detection, health care, smart robotics, and wearable electronics.1−11 However, traditional flexible strain sensors suffer from poor electric signal reliabilities and mechanical stability problems under repeated bending or stretching because of the inefficient interfacial bonding between conductive fillers and polymer matrix. After inevitable mechanical fractures or scratches, it is hard to restore the conductive network and its functionality, resulting in signal instability and even the entire component breakdown. Thus, endowing flexible strain sensors with self-healing ability is of great significance to improve their long-term stability, reduce the maintenance costs, and prolong their lifetimes. A mass of reversible bonds, such as hydrogen bonding,12−18 π−π stacking,19,20 host−guest recognition,21,22 metal−ligand interaction,23−27 and dynamic covalent bond,28,29 was introduced to endow strain sensors with desirable self-healing abilities. In general, strain sensors constituted by solely dynamic reversible bonds exhibit excellent self-healing capability while possessing poor mechanical properties because the strength of these bonds are relatively weak.30−32 Covalently cross-linked © 2019 American Chemical Society
network is an alternative way to improve the mechanical properties of these self-healing materials. Nevertheless, it usually impairs their mechanical healing efficiency owing to the irreversible permanent fracture of covalent bonds.33 Until now, despite brilliant advances in self-healing materials, it still remains a great challenge to balance the contradiction between mechanical properties and self-healing performance. In addition, preparation of supramolecular self-healing sensors usually involves sophisticated, noxious manufacturing procedures, which inevitably required environmentally hazardous organic reagents and massive energy cost. Therefore, it is highly desirable to fabricate self-healing strain sensors in a scalable and environmentally friendly way. In this work, we present a large-scale and eco-friendly approach to fabricate an autonomously self-healing strain sensor with ultrahigh sensitivity and desirable mechanical properties. Specifically, graphene nanosheets (GNs) were massively produced via ion-intercalated mechanical milling method and then utilized to construct a well-organized conductive nanostructure in epoxidized natural rubber (ENR) Received: April 11, 2019 Accepted: June 6, 2019 Published: June 6, 2019 23527
DOI: 10.1021/acsami.9b06208 ACS Appl. Mater. Interfaces 2019, 11, 23527−23534
Research Article
ACS Applied Materials & Interfaces
Figure 1. Schematic diagram for preparing GN−Fe3+−GA@ENR nanocomposites.
Figure 2. (a,b) TEM images of GNs; (c) Raman images, and (d) XRD patterns of original EG and GNs.
matrix. Benefit from the reversible interfacial metal−ligand coordination, the resulted material possesses excellent selfhealing performance and satisfactory mechanical properties at the same time. In addition, the obtained strain sensors with elaborate conductive network design show excellent electrical conductivity and strain sensitivity. These characteristics enable the nanocomposites to act as high-performance strain sensors for monitoring multiscale human activities. This delicately designed self-healing strain sensor provides a feasible approach for the large-scale fabrication and widespread application of artificial skin, smart robotics, and other electrical devices.
strain sensors relies heavily on the production approach of welldispersed conductive fillers in an affordable way. Graphene materials with large surface area, electrical conductivity, and mechanical and chemical stability are regarded as the desirable conductive fillers for high-performance strain sensors. However, the common methods for the preparation of graphene mainly include specific organic solvents, strong acids or oxidants, and toxic reducing agents.34−38 In contrast, mechanical exfoliation of graphite in aqueous solution offers a facile and cost-efficient route to graphene preparation.39 Here, Fe3+ ions and Kevlar fiber pulp were chosen as the intercalator to assist the exfoliation of expanded graphite (EG) in aqueous suspension through co-milling in a grinder, as schematically shown in Figure S1. Owing to the intensive shear stressing in the mill, the Fe3+ ions can intercalate into the edge of EG, thus facilitating the exfoliation progress. The TEM
2. RESULTS AND DISCUSSION 2.1. Ion Intercalation and Exfoliation of Expanded Graphite. The preparation process of this ENR nanocomposite is shown in Figure 1. Large-scale fabrication of 23528
DOI: 10.1021/acsami.9b06208 ACS Appl. Mater. Interfaces 2019, 11, 23527−23534
Research Article
ACS Applied Materials & Interfaces
Figure 3. FTIR (a) and Raman (b) spectra of GA and GN−Fe3+−GA. (c) Comparative mechanical tests of the GN−Fe3+−GA@ENR and EG/ENR nanocomposites.
Figure 4. SEM (a,b) and TEM (c,d) images of GN−Fe3+−GA@ENR. (e) Schematic illustration of the strain-sensing mechanism. (f−h) Corresponding current signals of the original sample at different tensile strains during cyclic tests. (i−k) Corresponding current signals of the selfhealing sample at different tensile strains during cyclic tests. (l) Corresponding GF variation of original sample. (m) Comparison of GF and maximum sensing range of different sensors reported in the literature.
images at high magnification show that the stacked layers of EG were efficiently delaminated and a transparent laminar structure was obtained (Figure 2a,b), suggesting the successful exfoliation of single- or few-layer GNs. The exfoliated GN sample exhibits a thickness of 2 nm and a lateral size of 200− 400 nm. Raman spectroscopy was performed to study the chemical structure change of pristine EG and the as-prepared GN sample (Figure 2c). The G-band ascribed to the sp2 vibration of carbon atoms was observed at 1580 cm−1, and the D-band related to disordered sp3 vibration of carbon atoms was observed at 1347 cm−1.40 The intensity of G peak and the ID/IG ratio increases after ion-intercalated mechanical milling, suggesting that more two-dimensional hexagonal lattice structures and more defects were generated. In addition, the shape of 2D′ peak at around 2700 cm−1 serves as an indicator of the exfoliation degree of graphite. The inset in Figure 2d shows that a sharper 2D′ peak is found after mechanical milling, which further proved that the as-prepared GNs are of characteristic single layer or few layers. Figure 2d displays the X-ray diffraction (XRD) patterns of EG and GNs. Two main peaks, which are ascribed to the graphite2H (002) and (004) faces, can be found in EG, respectively.41
In comparison, the intensities of the (002) peak and the (004) plane considerably increase after exfoliation, demonstrating the successful exfoliation of EG by wet-milling procedure with decreasing the two-dimensional size of C−C layers.42 2.2. Fe3+−Ligand Coordination between GN/Rubber Interface. Fourier transform infrared (FTIR) analysis was utilized to elucidate the interfacial metal−ligand coordination interaction. Figure 3a shows the FTIR spectrum of raw gelatin (GA) and the GN−Fe3+−GA nanocomposite. The raw GA exhibits typical bands of the amide group stretching vibrations within the range of 3200−3600 cm−1 and the characteristic peak of C−H stretching vibrations at 2931 cm−1. The C−O stretching vibrations and the N−H deformation vibrations attributed to amide are observed at ∼1642 and ∼1539 cm−1, respectively.43 In comparison, the spectrum of GN−Fe3+−GA shows another absorption peak at 582 cm−1, which is characteristic of metal−oxygen stretching absorbance, indicating the successful formation of the Fe3+−ligand coordination.44 Raman spectroscopy is a powerful experimental test to identify the structural features of iron oxides. As shown in Figure 3b, resonance peaks are observed in the GA spectra in the 500−700 cm−1 region. However, these peaks are disappeared in GN− 23529
DOI: 10.1021/acsami.9b06208 ACS Appl. Mater. Interfaces 2019, 11, 23527−23534
Research Article
ACS Applied Materials & Interfaces
Figure 5. (a) Schematic illustration of the self-healing process. (b) Stress−strain curves for the original, first, and second self-healed samples. (c) Electrical self-healing performance of the strain sensor. Pictures giving the cutting/healing capability of GN−Fe3+−GA@ENR nanocomposites under different harsh conditions: in water (d) and at subzero (f). Stress−strain curves for samples with healing for 1 h in water (e) and at subzero (g).
Fe3+−GA nanocomposites, suggesting the strong metal−ligand coordination between Fe3+ ions and oxygen-containing groups on GA molecular chains.45 2.3. GN-Assembled Nanostructured Conductive Network. Benefitting from the interfacial metal−ligand coordination between GNs and ENR, a well-arranged graphene network was obtained based on the excluded volume effect of ENR latex microspheres. In order to intuitively observe the organized GN nanostructure in the ENR matrix assisted by the elaborate surface chemistry design, the nanocomposite was etched with toluene via Soxhlet extraction. Figure 4a,b shows the scanning electron microscopy (SEM) images of the residual GN skeleton. A porous GN network was observed, in which the pore diameters range from hundreds of nanometers to several microns. As further proved by the frozen section transmission electron microscopy (TEM) images of the GN−Fe3+−GA@ ENR nanocomposite (Figure 4c,d), the interconnecting GNs are uniformly located in the ENR latex microspheres, possessing an apparent continuous and latticed nanostructure. This well-organized conductive network endows our GN− Fe3+−GA@ENR nanocomposites with excellent conductivity and strain sensitivity. Schematic illustration of the strain-sensing mechanism of the electronic sensors is depicted in Figure 4e. The well-organized conductive network suffers from a disruption of conductive pathways under external strains, giving rise to the output electrical signal variation. Tests of strain detection were conducted to qualitatively evaluate the sensitivity. As shown in Figure 4h, the flexible sensor exhibits stable and repetitive response signals with a detection limit as low as 0.2% strain. In addition, the signal intensity enhances as the strain increases
from 0.2 to 1%, owing to more breakages generated during tensile stretching (Figure 4f,g). The electromechanical behaviors of the flexible sensor under tensile deformations have been measured, including the fractional resistance change (R − R0/R0) and the relative gauge factor (GF = ΔR/R0·ε) (Figure 4l). As the applied strain increased gradually (0−73.7%), the relative resistance (R − R 0 )/R 0 exhibited a relatively slow increase with the disconnection of conductive pathways generating in the GNs slowly. Once the applied strain exceeds 73.7%, the resistance began to increase quickly with an ultrahigh GF of 45573.1, which could be attributed to the increased disconnection of conductive pathways among GN layers, escalating the effect of the disruption of the whole conductive network. These results demonstrate that the strain sensor integrates high sensitivity with a broad sensing range, expanding its application to multiscale human motion monitoring. By comparison, the strain sensors in previously issued reports usually have low sensitivities or narrow sensing ranges (
