Scalable-Manufactured Self-Healing Strain Sensors Based on Ions

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Functional Nanostructured Materials (including low-D carbon)

Scalable-Manufactured Self-Healing Strain Sensors Based on IonsIntercalated Graphene Nanosheets and Interfacial Coordination Yumeng Tang, Quanquan Guo, Zhenming Chen, Xinxing Zhang, Canhui Lu, Jie Cao, and Zhuo Zheng ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.9b06208 • Publication Date (Web): 06 Jun 2019 Downloaded from http://pubs.acs.org on June 6, 2019

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ACS Applied Materials & Interfaces

Scalable-Manufactured Self-Healing Strain Sensors Based on Ions-Intercalated Graphene Nanosheets and Interfacial Coordination Yumeng Tang, ‡1 Quanquan Guo, ‡1 Zhenming Chen,2 Xinxing Zhang,1 Canhui Lu,1 Jie Cao1 and Zhuo Zheng1 1

State Key Laboratory of Polymer Materials and Engineering, Sichuan University,

Chengdu 610065, China 2

Guangxi Key Laboratory of Calcium Carbonate Resources Comprehensive

Utilization, College of Materials & Environmental Engineering, Hezhou University, Hezhou 542899, China ‡ Yumeng Tang and Quanquan Guo contributed equally to this work.

Keywords: self-healing, strain sensors, graphene nanosheets, ions-intercalation, metal-ligand coordination

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, ions-intercalated mechanical milling was utilized to realize the large-scale preparation of graphene nanosheets (GNs). Then, a well-organized GNs nanostructured network was constructed in rubber matrix based on interfacial metal-ligand coordination. The resulted 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 ~45573.1). 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

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artificial skin, smart robotics and other electrical devices.

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, healthcare, smart robotics, and wearable electronics.1-11 However, traditional flexible strain sensors suffer from poor electric signal reliabilities and mechanical stabilities problems under repeated bending or stretching due to 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 were 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 possess poor mechanical properties, since the strength of these bonds are relatively weak.30-32 Covalently crosslinked 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 Up to 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 involve 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 ions-intercalated mechanical milling method, and then utilized to construct a well-organized conductive nanostructure in epoxidized natural rubber (ENR) matrix. Benefit from the reversible interfacial metal-ligand coordination, the resulted material possesses excellent self-healing 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 multi-scale 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.


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2. Result and Discussion 2.1. Ions-Intercalation and Exfoliation of Expanded Graphite The preparation process of this ENR nanocomposite is shown in Figure 1. Large-scale fabrication of strain sensors relies heavily on the production approach of well-dispersed conductive fillers in an affordable way. Graphene materials with a large surface area, electrical conductivity, 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

Figure 1. Schematic diagram for preparing GNs-Fe3+-GA@ENR nanocomposites.

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 images at a high magnification show that the stacked layers of EG was efficiently delaminated and a transparent laminar structure was obtained (Figure 2a and b), suggesting the successful exfoliation of single- or few-layer GNs. The exfoliated GNs sample exhibits a thickness of 2 nm and lateral size of 200~400 nm.



Raman spectroscopy was performed to study the chemical structure change of pristine EG and the as-prepared GNs 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 of was observed at 1347 cm-1.40 The intensity of G peak and the ID/IG ratio increases after ions-intercalated mechanical milling, suggesting that more two-dimensional hexagonal lattice structure and more defects generated. In addition, the shape of 2D' peak at around 2700 cm-1 serves as an indicator of the exfoliation degree of graphite. The inserted zoom in Fig. 2c 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 XRD patterns of EG and GNs. Two main peaks, which are ascribed to the Graphite-2H (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

Figure 2. (a and b) TEM images of GNs; (c) Raman images and (d) XRD patterns of original EG and GNs.

2.2. Fe3+-Ligand Coordination Between GNs/Rubber Interface FTIR analysis was utilized to elucidate the interfacial metal-ligand coordination interaction. Figure 3a shows the FTIR spectrum of raw gelatin (GA) and the GNs-Fe3+-GA nanocomposite. The raw gelatin exhibits typical bands of the amide group stretching vibrations within the range of 3200–3600 cm-1 and characteristic peak of C-H stretching vibrations at 2931 cm-1. The C-O stretching vibrations and N-H deformation vibrations attributed to amide is observed at ~1642 and ~1539 cm-1, respectively.43 In comparison, the spectrum of GNs-Fe3+-GA shows another


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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 GNs-Fe3+-GA nanocomposite, suggesting the strong metal-ligand coordination between Fe3+ ions and oxygen-containing groups on GA molecular chains.45

Figure 3. FTIR (a) and Raman (b) spectra of GA and GNs-Fe3+-GA. (c) The comparative mechanical tests of the GNs-Fe3+-GA@ENR and GNs/ENR nanocomposites.

2.3. GNs Assembled Nanostructured Conductive Network Benefit 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 GNs nanostructure in the ENR matrix assisted by the elaborate surface chemistry design, the nanocomposite was etched with toluene via Soxhlet extraction. Figure 4a and b are SEM images of the residual GNs skeleton. A porous GNs network was observed, of which the pore diameters range from hundreds of nanometers to several micro. As further proved by the frozen section TEM images of the GNs-Fe3+-GA@ENR nanocomposite (Figure 4c and 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 GNs-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. And the signal intensity enhances as the strain increasing 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-R0)/R0 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 GNs layers, escalating the effect of the disruption of the whole conductive network. These results demonstrate that the strain sensor integrates high sensitivity with broad sensing range, expanding its application to multi-scale human motion monitoring. By comparison, the strain sensors in previously issued reports usually have low sensitivities or narrow sensing ranges (

Scalable-Manufactured Self-Healing Strain Sensors Based on Ions

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