Passive and Space-Discriminative Ionic Sensors Based on Durable

Among these, the H-RGO/CNTs/Ag electrode-based sensor definitely exhibited the best electromechanical properties because of its convenient ion transmi...
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Passive and Space-Discriminative Ionic Sensors Based on Durable Nanocomposite Electrodes toward Sign Language Recognition Jingjing Zhao,†,#,‡ Song Han,†,#,‡ Ying Yang,† Ruoping Fu,† Yue Ming,†,⊥ Chao Lu,† Hao Liu,⊥ Hongwei Gu,§ and Wei Chen*,† †

i-Lab, Suzhou Institute of Nano-tech and Nano-bionics, Chinese Academy of Sciences, Suzhou 215123, P. R. China University of Chinese Academy of Sciences, Beijing 100049, P. R. China ⊥ School of Textiles, Tianjin Polytechnic University, Tianjin 300387, P. R. China § College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou 215123, P. R. China #

S Supporting Information *

ABSTRACT: This work developed an ionic sensor for human motion monitoring by employing durable H-reduced graphene oxide (RGO)/carbon nanotubes (CNTs)/Ag electrodes and an ionic polymer interlayer. The sensor functions as a result of unbalanced ion transport and accumulation between two electrodes stimulated by applied deformation. The networking structure and stable electrodes provide convenient ion-transport channels and a large ion accumulation space, resulting in a sensitivity of 2.6 mV in the strain range below 1% and high stability over 6000 bending cycles. Ionic sensors are of intense interest motivated by detecting human activities, which usually associate with a large strain or deformation change. More importantly, direction identification and spatial deformation recognition are feasible in this research, which is beneficial for the detection of complex multidimensional activities. Here, an integrated smart glove with several sensors mounted on the hand joints displays a distinguished ability in the complex geometry of hand configurations. Based on its superior performance, the potential applications of this passive ionic sensor in sign language recognition and human−computer interaction are demonstrated. KEYWORDS: passive device, ionic sensor, large-scale detection, spatial monitoring, sign language recognition

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with convenient equipment that could conformally attach to the soft human body and quantify real-time physical signals are critical requirements.20−27 Flexible sensors in wearable systems could solve this problem as they have a lot of advantages, such as lightweight, compatible, low cost, and easily integrated.28−32 While most research into wearable sensors focuses on tiny strain or stress change detection for health monitoring, very few works lay emphasis on large-scale, complex, changeable human motions, such as wrist and finger movements. It is worth noting that the strain or deformational change degree by these activities is mainly distributed over a great variation.33−36 These characteristics require wearable sensor systems with high flexibility and good reliability and robustness, which will accommodate large-scale complicated changing activities.

egular human activities generate various physical stimuli, such as body and skin heat, blood pressure, the pulse, skin strain, and infrared radiation, all of which produce tiny strain or stress change but are significant health indicators.1−4 On the other side, some activities are capable of reflecting physical actions, such as muscle movement, joint motion, and bodily motion, which result in large strain or stress change.5−9 Measuring and quantifying physical signals produced by the human activities provide an opportunity for disease diagnosis, healthy monitoring, motion control, and human−computer interactive activity.10−17 However, real-time detection is still a challenge in these activities monitoring. To realize this goal, the detection equipment should be lightweight, a mechanical match for soft biological tissues, easy to integrate, and can withstand repeated mechanical deformation. Conventional monitoring settings, such as electrocardiography, electromyography, and electroencephalography are generally rigid, large, expensive, and difficult to operate.18,19 Therefore, they are not suitable to solve this problem. Wearable sensor systems © 2017 American Chemical Society

Received: April 21, 2017 Accepted: July 31, 2017 Published: July 31, 2017 8590

DOI: 10.1021/acsnano.7b02767 ACS Nano 2017, 11, 8590−8599

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demonstrated through an integrated smart glove with the ionic sensors. By gathering data from sensors on the joints of fingers and the wrist, a signal contrast mapped with the different joint deformation is formed, which is consistent with the hand configuration. Our sensor displays distinguishable ability in complex geometry of hand configurations. In the same way, this detection approach can be applied in sports injury rehabilitation for athletes or soldiers, position control, and abnormal attitude monitoring. On the other side, after normalizing testing data to computer language through binary conversion, sensor or sensor arrays can be applied as a smart controller easily. The ionic sensor would provide important academic value as well as broad application aspects to human−computer interaction. Sensor Device Preparation and Characterization. Here, we introduce the flexible ionic sensor composed of HRGO/CNTs/Ag electrodes and ionic polymer membrane which can simultaneously quantify the mechanical deformation induced by normal human motions. Figure 1a presented a schematic illustration of the fabrication procedure of the ionic sensor.

In this respect, electronic skin is a significant research direction because it can mimic human skin to sense various stimuli, such as deformation, pressure, light, and temperature.8,27,37−43 As an important kind of electronic skin, the pressure or strain sensors have been thoroughly investigated with different applications including personal healthcare and muscle or movement monitoring.44−46 However, it has several drawbacks, such as monotonous testing signal31,47 and indispensability of additional power supply. Although some of these disadvantages can be made up by means of sensor arrays testing and portable battery supply, these means will also increase the burden of data postprocessing and destroy the entire integration with wearable systems, particularly in complex motion detection.48 Ionic skin, which conducts signals using ions, provides another opportunity for these requirements. Ionic polymer metal composites are a class of ionic skin because of their potential generating from mobile charged ions in deformation.49 Nevertheless, they are not stable under a long-term deformation process due to the mud crack structures of metallic electrodes formed through bending management.50,51 Additionally, large-scale activity detections require sensors with extreme mechanical stability. Consequently, durable, flexible materials that possess robustness in long-term and extreme mechanical deformation conditions are desired for motion monitoring. In this work, we explore a flexible deformation sensor based on ionic conductionan ionic polymer sandwiched between two composite conductors. The designed sensor has several features: (i) The electrodes are optimized to satisfy convenient ion-transport channels, large ion accumulation space, flexible properties, and mechanical robustness that provide good sensitivity and stable motion detection. Reduced graphene oxide (RGO) and carbon nanotubes (CNTs) with high-performance electrical and mechanical properties are promising electrode candidates for deformation sensors.52 Composite electrodes consisting of layered holey graphene (H-RGO) flakes and randomly entangled CNTs can fulfill the flexible requirement of detection conditions. The networking architecture displays a convenient ion channel and high electrical and mechanical stability in deformation.53,54 Ag nanowire is considered as a high conductive material which can reduce the surface electrode resistance. The multilayered electrodes consist of H-RGO/CNTs/Ag lead to high conductivity, desired ion channel, and excellent mechanical robustness. Sensory sheets based on the electrodes can monitor large deformation, such as that generated by the bending of a wrist or a finger. (ii) Moreover, direction identification is feasible in our research. Opposite bending directions produce different signals, which is beneficial for the detection of complex multidimensional motions. Based on the properties, several representative deformations of joint can be monitored by only one sensor device, which means spatial deformation recognition ability is realized in our work. (iii) In addition, the detection of signals is due to ionic transmission and accumulation under deformation, making it self-powered and easy to be integrated with physical activity sensor systems. The sensor generates stable electrical voltage under the bending process without the requirement of a power supply. Our research motivation exists not only in high-performance measurement but also in its application in complex human motion measurements, especially in sign language recognition. Sign language recognition, which is used in manual communication and body language to convey meanings, is

Figure 1. Schematic of the fabrication process for the ionic sensor and structural properties of the sensor component units. (a) Schematic of the fabrication process for the ionic sensor. (b) Crosssectional SEM images of ionic sensor (Inset: zoomed SEM image of electrode). (c) SEM images of Ag nanowires layer. (d, e) Cross view and surface view images of the assembled H-RGO/CNTs layer.

Material component of the ionic sensor: Two dimensional holey graphene (H-RGO), with high-ion-accessible surface area, was selected as electrode candidate of the ionic senor. To prevent the restacking of H-RGO layers via van der Waals interactions, one-dimensional CNTs were used as a smart spacer. The holes on H-RGO and gap formed by CNTs will increase the accessible space of ion accumulation and supply an 8591

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Figure 2. Sensing performance of the ionic sensor. (a) Working mechanism of the ionic sensor. (b) The comparison of electrical responses of four kinds of electrode-based sensors under the same bending movement. (c) Potential change of the ionic sensor with the deformation displacement of an analytical model. (d) Strain change and the simultaneously generated voltage variation. (e) Potential response to the change of bending direction.

easy channel for ion transport.55,56 Meanwhile, a networking structure is constructed which could enhance the electrical and mechanical stability by providing more link channels in the electrode. Here, CNTs also act as efficient electrical conducting paths and mechanical bridging ligands for the electrodes. For another, an Ag nanowire layer was used as a low surface resistance of the electrodes. Ag nanowire could form a highly conductive and stable surface electrode due to its excellent conductivity and cross-assemble structure. In other words, Ag nanowire is considered as an outer electrode, and H-RGO/ CNT works as the inside interface electrode. Consequently, the

special materials and structures of electrodes provide free paths of ion transport, large volume in ion accumulation, and robust networking structure in sensing process, so that they will have a positive effect on achieving high performance. On the other hand, thermoplastic polyurethane (TPU) was used as the middle layer, because of its good mechanical properties in bending and elongation process. Ionic liquid was added as electrolyte due to their high voltages stability and nonvolatility. Fabrication of the ionic sensor: The procedure involved five main steps: (i) filtration of Ag nanowires on the PEFE filter as the outer conductive layer, (ii) filtration of self-assembly H8592

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stretched side, resulting in a positive potential. The electrode on the compressed side of had a negative charge. According to the working mechanism, the voltage signal was totally generated from ions movement and accumulation in the deformation process, so the sensor could operate without the requirement of a power supply. The sensor potential under the maintained extended state was also detected (Figure S4). The output signal declined around 8% slowly as the sensor maintained a bending state, which implied that the potential generated from the ions transmission and accumulation. To estimate the operation of the ionic sensor, an analytical model (Figure S5a) for the coupling of mechanical deformation and voltage response was formed by establishing electrical contacts to the ends of a ribbon-shaped sample on a flexible paper support. Figure S5b provided the corresponding electrical response to a 5 mm bending deformation (Δx = 5 mm), showing a timely response to an external deformation. Considering the ions response mechanism, the sizes of ions would had an effect on the sensing properties, and we further conducted the same bending test with EMIBF4 (BF4− has a smaller anion diameter, 0.454 nm), while the diameters of positive ion (EMI+) and negative ion (TFSI−) were 0.606 and 0.652 nm, respectively. The difference of output potential is demonstrated in Figure S6, and the response potential showed that smaller ions produced bigger output signals. Small ions (BF4−) have a smaller resistance in movement than big ions (TFSI−), resulting in more ion accumulation in the bending process. In order to further evaluate and understand the electrode structure of sensor properties, we compared four kinds of electrodes with different structures to a fixed deformation (Δx = 5 mm, Figure 2b). Among these, the H-RGO/CNTs/Ag electrode-based sensor definitely exhibited the best electromechanical properties because of its convenient ion transmit path and large ion storage volume (Figure 2b). The cross sectional schematic in Figure 2a (right) and Figure S4c would explain the high performance more clearly. The H-RGO and HRGO/CNTs inner electrodes have an average pore size of 15.1 and 14.3 nm, respectively, which are larger than the pore size of RGO (9.7 nm) and RGO/CNTs (9.3 nm) electrodes. Furthermore, the H-RGO/CNTs electrode has a larger pore volume of 0.512 cm3 g−1, compared with the pore volume of RGO (0.464 cm3 g−1), RGO/CNTs (0.484 cm3 g−1), H-RGO (0.486 cm3 g−1) electrodes. The H-RGO/CNTs electrode not only has pores on these graphene flakes but also has carbon nanotubes as a spacer, so the H-RGO/CNT electrode has the largest pore volume. On the contrary, the RGO electrode has the smallest pore volume. RGO/CNTs and H-RGO electrodes have a spacer (carbon nanotubes) and nanoscale pores on graphene flakes, respectively, so they have similar and medium pore volume. The holes on holey graphene and interspace between H-RGO and CNTs provided an easy passageway for ion insertion and emigration and extra ion accumulation space in the H-RGO/CNTs/Ag electrode. From these data, our multilayered electrode was the most optimized candidate for higher performance than other electrodes. The response time was tested by applying a transient bending on the sensor, and the output signal is recorded in Figure S7. Through the potential−time curve, the response time was determined to be around 200 ms. Data in Figure 2c were highlighted as wellbehaved in the output voltage with the fixed deformation. There was no obvious hysteresis in output signals because of the low-frequency bending process and short response time.

RGO/CNTs as the inner electrode layer, (iii) cutting of the composite electrode layer into appropriate sizes, (iv) combination of two electrodes and TPU/EMITFSI interlayer, and and (v) hot-press of the blocks to form a multilayered ionic sensor prototype. The H-RGO was prepared via a gold nanoparticle (AuNP)-catalyzed reaction of reduced graphene oxide (RGO).57 Then the formed H-RGO was self-assembled with CNTs (see Experimental Section). The structural properties of relevant materials are shown in Supporting Information Figure S1. The original RGO had a smooth surface with no holes (Figure S1a), and the AuNPs as catalyst showed uniform size and shape (Figure S1b). After light irradiation with the presence of AuNPs and H2O2, nanoscale pores were created on these RGO sheets (Figure S1d). The Figure S1c was the image of RGO sheet with homogeneously dispersed AuNPs before light irradiation. Figure 1b displayed the integral construction of the sensor device after hot-press process. The inset picture clearly indicated the Ag nanowire layer and H-RGO/CNTs layer. A TPU/EMITFSI interlayer with an average thickness of 180 μm was sandwiched between two H-RGO/CNTs/Ag electrodes, forming the ionic sensor. The outer conductive layer (Ag nanowire layer) and inner electrode layer (H-RGO/CNTs layer) were represented by scanning electron microscope (SEM), respectively. A well-interconnected Ag nanowire layer was confirmed from Figure 1c, which showed homogeneous size in diameter. The cross-sectional image (Figure 1d) and top-view image (Figure 1e) revealed that CNTs were randomly dispersed into H-RGO sheets. In order to confirm that the multilayered H-RGO/CNTs/Ag composite is suitable for electrodes in long-term and large-scale motion detecting, we first analyzed the durable performance of the electrodes. The electrical conductivity of composite electrode was maintained due to its outstanding mechanical flexibility and robustness. Their electrical conductivities were plotted as a function of cycle number of repeatable bending test with a deformation displacement of 5 mm (Figure S2a). Electrical conductivity after a 10,000 cycle bending test was almost the same as the initial values. Figure S2b showed the conductivity changes of different electrodes in the bending test and electrodes with CNTs had more stable electrical performance. The networking structure in the composite electrode contributed to the excellent electrical stability. In the outer conductive layer, one-dimensional Ag nanowires crossed and formed a stable networking architecture. For the interface layer, as demonstrated in Figure S3, more mechanical connection channels were formed in the H-RGO/CNTs electrode, compared to the H-RGO electrode. The existence of connection channels will prevent the destroying of electrodes, resulting in outstanding mechanical robustness and electrical connection upon bending. Sensing Response of the Ionic Sensor. Potential is generated from the bending process through the redistribution of the ions (Figure 2a). When the sensor was in a flat state, anions (TFSI−) and cations (EMI+) uniformly dispersed in the TPU membrane. As the sensor was deformed to a degree, anions and cations on the compressed side of membrane moved toward to the stretched side of the membrane. The anions have a larger relevant volume, so they have a slower movement speed. Contrarily, cations are much smaller and faster. The imbalance in the number of ions contacting two electrodes generates output signals across the membrane.58,59 To be more specific, more cations accumulated on the 8593

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Figure 3. Stability of the ionic sensor. (a) Potential change of the ionic sensor with repetition of 6000 bending/restoration cycles with a bending deformation of 5 mm. (b, c) Enlarged view of (a), exhibiting highly reproducible and stable sensor performance. (d) Repetitive measurements of the potential change of the sensor with variations of the bending deformation in a sequence of 3, 5, and 7 mm. (e) Sensitivity (ΔV/ε) and relative potential change (ΔV = V − V0) of the sensor versus strain (ε).

These characteristics make H-RGO/CNTs/Ag film promising as building blocks for sensitive strain sensors. Figure 2d showed the instant change in the potential upon the strain change. The deformation strain under different displacement was calculated by using Matlab based on Euler−Bernoulli beam theory, as we hypothesized the neural axis of the device keeps its length during the deformation. The potential was increased with the increasing of strain (bending degree), indicating more ion accumulation from bending deformation. The ability to sense different directions is presented in Figure 2e. As the sensor block bend to opposite directions (inset pictures), positive and negative relative potential change appeared, respectively. The direction identification was the basis of spatial recognition and provided potential application in sign language recognition, which we detail in the latter half of this paper. The response and restoration curves of the sensor measured for 6000 cycles of 5 mm displacement were shown in Figure 3a. The output signals of the ionic sensor were stably maintained with a small variation (about 6%) in the relative change of potential. As mentioned above, this robust response was associated with the networking structures of H-RGO/CNTs/ Ag electrodes that can prevent the destroying of electrical connections even under the extreme bending process. We attributed the variation to the change of external environment, such as temperature, humidity, considering the long time of (about 10 h) bending detection. However, there was no difference in hysteresis, as clearly shown in Figure 3b and 3c. To investigate the reliability of deformation sensor, we applied a series of deformation displacements of 3, 5, and 7 mm, and the same measurements were obtained three times (Figure 3d). Figure 3e indicated the relative change in voltage (ΔV = V − V0) at a series of strain, which exhibited positive dependence on the strain (ε). Small error bars were standard deviations taken from 10 time measurements, showing a very high reproduci-

bility, and the strain coefficient (sensitivity, S) was defined as S = ΔV/ε, indicating a sensitivity range from 2.2 to 2.6 mV. Therefore, it can be concluded that the ionic sensor was highly reproducible and repeatable against repeated deformation. Potential change at different bending frequencies is presented in Figure S8, and the sensor produced similar output signals with different bending frequency. With the increasing bending rate, more measure noise appeared, as there was bending instability at higher frequency tests. But judged from the measured response time of 200 ms, the sensor should have the ability to monitor motion frequency up to 5 Hz. Currently, our sensor focus is on human motion monitoring, which experiences low frequency and small strain rate changes. Therefore, the ionic sensor fulfilled the requirement of human motion testing. Large-Scale, Complex Human Spatial Motion Monitoring and Sign Language Recognition. In order to observe the monitoring capabilities of ionic sensor to large-scale human motions, the ribbon-like sensor was adhered to the joint of hand fingers, as shown in Figure S9. To protect the sensor against unintentional damage and comfortably contact with the hand joints, it was encapsulated with two pieces of acrylic elastomer (VHB 4905, 3M). The potential change was recorded in real time during the motion processes of the hand fingers and wrist. Figure S9a shows that the potential decreased with the bending of the index finger three times. In the same method, a potential change map with five fingers during fist clenching was formed in Figure S9c, as another four sensors were mounted on the finger joints (Figure S9b). We also attempted to measure the bending of the arm, shoulder, and knee, which could undergo a large bending change. Figure S10 showed the relevant potential change of arm bending, shoulder motion, and knee bending processes. Clearly, these 8594

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Figure 4. Large-scale and spatial movements monitoring. Relevant potential change of (a) down bending, up bending (inset: optical image of the sensor placed on the top of a human wrist) and (b) pronation and extorsion (inset: optical image of the sensor placed on the inside of a human wrist). (c, d) Signal shape and their corresponding wrist change. (e) Schematic illustration of wrist movements from front and side views.

Figure 5. Sign recognition of the ionic sensor arrays with smart glove. (a) Schematic for situation of sensors. (b) Smart glove with six sensors (wrist top, thumb, index, middle, ring, little) and (c) eight sensors (wrist top, wrist outside, wrist inside, thumb, index, middle, ring, little). (d, e) Potential mapping on sensors of the gloves for different sign language.

posture and moving of the whole body could be fully identified by mounting the ionic sensors on each joint of the human body. Detecting and recognition of human sign language has an important academic value as well as broad application aspect, such as communication with deaf-mute people, human− computer interaction, and intelligent control. In order to demonstrate the sign language detecting properties of the ionic sensor, a smart glove (right-hand) with sensor unit array was fabricated, as shown in Figure 5b (6 sensors) and Figure 5c (8 sensors). To characterize the configuration of movements on hand more accurately, we captured movement from several target points (Figure 5a) on the hand. An acrylic elastomer was used to help the sensor units tightly adhere to the five finger joints and wrist tightly. Each strain sensor on the glove corresponded to different joint deformations. Consequently, the glove was able to detect the whole hand configurations during spatial deformations in sign language successfully. The potential response of the sensor arrays on the glove was presented in Figure 5d as making sign language actions of “I love you”. When a hand joint was bent, the bending strain was accommodated by the sensors, causing change in the potential

signals indicated the sensor had the ability to detect a large range of human motions. These joints activities only represented one side of the bending process, while the hand wrist could provide four representative deformations, including down bending, up bending, pronation, and extorsion. These complex movements can be detected with the ionic sensors. Figure 4a shows a sensor mounted on the top of the wrist of a human (inset optical picture) and potential change in bending process with different directions. An opposite signal appeared with down bending and up bending, which illustrated that our sensor could distinguish bending directions again. The signal shape in Figure 4c indicated individual down bending and up bending of wrist. When the sensor was fixed on the inner side of the wrist (inset of Figure 4b), wrist movements such as pronation (360°) and extorsion (360°) can be detected, as shown in Figure 4b. As a result, the signal shape in Figure 4d indicated a successive pronation and bending process. Consequently, the ionic sensor could identify four kinds of spatial movements unambiguously. Considering the spatial recognition ability, our ionic strain sensors had strong advantages in complex geometry recognition. Using the multiple sensing properties of the sensor, the 8595

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shortcut to realizing ion transport and accumulation for ion absorption. On the other side, forming networking connections between H-RGO and CNTs guaranteed a highly robust electrode structure upon large-scale deformation. Consequently, high performance was achieved with the nanostructure hybrid-based electrode. The imbalance of distribution between two electrodes from bending deformation produced output voltage, and this sensing mechanism proved the sensor with self-powered properties. In addition to an excellent sensitivity range from 2.2 to 2.6 mV, the ionic sensor exhibited outstanding stability against repeated external deformations (6000 cycles). Furthermore, the ionic sensor could distinguish deformations with different directions, and the spatial resolution ability was also proved in this work. By using these sensors, an ionic conduction-based smart glove was fabricated, which featured a multidimensional detection of hand movements. Through sensor arrays mounted on hand joints, different hand configurations of sign language can be recorded and analyzed by the output potential signal. With the help of sign normalization by computer binary mode flag, the smart glove can be demonstrated to control a robot or mobile phone wirelessly, which indicates a promising candidate for human− computer interfaces. Based on the remarkable performance of the integrated smart glove, we believe that the sensor will provide opportunities for the development of gesture recognition, an intelligent control in the next generation of real human−computer interaction.

signal. Benefiting from the spatial resolution ability, very close hand configurations could be distinguished. For example, we could not tell the difference between the words “I” and “you” by the finger data only. But the wrist data distinguish these two words by different bending directions. By gathering these movement data, we could read the similar sign language correctly. In the same manner, the smart glove with 8 sensor arrays was applied to sense sign language actions of “Hi, how are you”. The sensor arrays mounted on the glove are displayed in Figure 5c. Two opposite sides of the wrist were mounted with sensors to detect more detailed movements. As shown in Figure 5d, signal word “how” induced distortion of the hand wrist. The potential change data were gathered and plotted in Figure 5e. The different data shapes clearly indicated different movement patterns, which were highly consistent with the hand deformation of different sign languages, demonstrating the exact response of our smart glove to hand movements. In total, the smart glove could acquire three-dimensional movements with a simple output potential shape. Complex and similar configurations could be detected and distinguished, indicating a monitoring application in complicated actions recognition. Besides signal sensing of hand movements, signal transduction was also an essential link for sign language recognition, especially for data record, data management, and humancomputer recognition. Based on the performance as we had mentioned before (Figure 4), a sensor unit could distinguish four representative joint movements, such as down bending, up bending, pronation, and extorsion. Here, we normalized each joint movement to binary digit array ‘1’, ‘10’, ‘11’, and ‘100’, respectively. In this case, sign language words of “I love you” and “Hi, how are you” were recorded and converted into computer language (Table S1). This method could bridge the gap between deformation detection and computer recognition with a simple binary mode flag, which is an advantage for autonomous gesture recognition with a computer. Furthermore, the spatial discrimination ability would enable us to express a hand deformation with smaller amounts of data than other sensors. It should be noted that a simple algorithm or less mathematical calculation will be used in the sign language recognition with our ionic sensors than image recognition in subsequent processing with a computer. Since the sensor signal could be normalized to computer language, it also can be applied as a smart controller. For instance, the gesture-based glove can be integrated with a custom-made data acquisition system to remotely actuate a robot with different actions and convey instructions to a mobile phone without touching it. More works will follow from further development of signal transduction, processing, and transmission to facilitate gesture recognition. Subtle and multiplexed body movements can also be obtained by changing the number of ionic sensors in the smart glove. Wireless transmission is very useful and convenient to use in practice. Incorporating a custom-developed mobile application also represents an important segment because it is a powerful tool for real-time physiological or physical monitoring, gesture-based identification, and human−computer interaction.

EXPERIMENTAL SECTION Materials. N-methyl-2-pyrrolidone (NMP), N,N-dimethylformamide (DMF), N,N-dimethylacetamide (DMAC), trisodium citrate dehydrate (N6H5Na3O7.2H2O), sodium borohydride (NaBH4), chloroauric acid (HAuCl4), thermoplastic polyurethanes (TPU), and polyvinylidene fluoride (PVDF) filters were purchased from Sinopharm Chemical Regent Co. Ltd. Reduced graphene oxide powder (RGO) and single-walled carbon nanotubes (CNTs) were purchased from Nanjing XFNANO Materials Tech Co., Ltd.; the diameter of CNTs was 1−2 nm, and the length was 5−30 μm. 1-Ethyl3-methylimidazolium bis (trifluoromethylsulfonyl) imide (EMITFSI) was purchased from SCJC Co. Ltd. Ag nanowire was donated by Professor Gu Hongwei from Soochow University. Synthesis of H-RGO. For the H-RGO synthesis, the AuNPcatalyzed method according to a modified previously described procedure was used.57 Using a sonication treatment with a power of 200 W, 8 mg of RGO powder was sonicated in 40 mL of DI water for 30 min. 100 μL of HAuCl4 (10 mg cm−3) was added into the water dispersion, followed by 172 μL of N6H5Na3O7·2H2O (5 mg cm−3) and 222 μL of NaBH4(5 mg cm−3). After that, 10 mL of 30% H2O2 was dropped into the mixture, and then the mixture was transferred to a culture dish and sealed with a quartz glass plate. A Xe lamp (PLSSXE300/300UV, 15A) was used with the culture dish positioned 4 cm from the light source for high-intensity irradiation. After 1 h, the reminder was washed with water for three times, obtaining H-RGO samples. Fabrication of H-RGO/CNTs/Ag Electrodes. At the beginning, a mixture of H-RGO (30 mg) and CNTs (10 mg) was dispersed in 30 mL of NMP solution for 30 min using a 200W horn sonication treatment (3 s on and 3 s off in an ice−water bath). Next, 3 mg of Ag nanowire was vacuum-filtered through a PVDF membrane (Millipore, 0.22 μm pore size). Then, 1 mL of H-RGO/CNTs mixture was vacuum-filtered on the top of a Ag nanowire layer. The resulting HRGO/CNTs/Ag electrode on PVDF membranes was dried in a vacuum oven at 60 °C for 6 h. Fabrication of TPU/EMITFSI Interlayer. A mixture of TPU (1.5 g) and EMITFSI (0.3 g) was dispersed in 10 mL of DMAC solution

CONCLUSION In summary, we have a developed flexible ionic sensor with durable H-RGO/CNTs/Ag electrode and ionic polymer, followed by fabricating a smart glove with the ionic sensor arrays. The compounded electrode with holey graphene (HRGO) and single-walled carbon nanotubes (CNTs) provided a 8596

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ACS Nano while stirring for 24 h. Then, 3 mL of the suspension was casted on a 7.5 × 2.5 cm2 area glass substrate and dried at 60 °C for 12 h. Construction of Sensors. Two pieces of the H-RGO/CNTs/Ag electrode layers were laminated on the TPU/EMITFSI layer through a hot press method (100 °C for 5 min). Then, we obtained an ionic sensor that was a TPU/EMITFSI layer sandwiched between two HRGO/CNTs/Ag electrodes (5 mm wide and 20 mm long flexible strip). The sensor based on a RGO electrode, HRGO electrode, and RGO/CNTs (3:1) electrode was fabricated by the same process that dispersed RGO, HRGO, and RGO/CNTs in DMAC under 200 W horn sonication treatments. Mechanical Motion Tests. The ionic sensor was pasted on a paper substrate first, and then the test sample was placed between the motorized translation stages (Beijing Optical Century Instrument Co.,Ltd. MTS121) with the two ends fixed. The free length of the sample was 30 mm (as demonstrated in Figure S4a). Displacement and the deformation times of the sample were controlled by a step motor. The output electrical signals were collected by an electrochemical workstation (CHI900D). The displacement of the movable stage under the different time was recorded by the laser displacement sensor (Keyence, LK-G80). The frequency test was built on a paper substrate (length, 120 mm), and the displacement of the bending reciprocating motion was 20 mm. Characterization. Surface morphology and the cross-sectional view of fabricated ionic sensor were investigated by using SEM Hitachi S-4800 and transmission electron microcopy (FEI, 200 kV). The electrode conductivity was tested by a multifunction digital four-probe tester (ST-2258C). The pore size and pore volume were estimated via the Barrett−Joyner−Halenda (BJH) methods. Sonication was performed using a Fisher Scientific model 500 digital sonic dismembrator equipped with a 12.5 mm diameter disruptor horn.

ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (grant no. 21373263), the External Cooperation Program of BIC from Chinese Academy of Sciences (grant no. 121E32KYSB20130009), the Science and Technology of Jiangsu Province (grant no. BE2016086), and the Special Project of Nanotechnology in Suzhou (ZXG201423). REFERENCES (1) Chen, L. Y.; Tee, B. C.; Chortos, A. L.; Schwartz, G.; Tse, V.; Lipomi, D. J.; Wong, H. S.; McConnell, M. V.; Bao, Z. Continuous Wireless Pressure Monitoring and Mapping with Ultra-Small Passive Sensors for Health Monitoring and Critical Care. Nat. Commun. 2014, 5, 5028. (2) Yang, J.; Chen, J.; Su, Y.; Jing, Q.; Li, Z.; Yi, F.; Wen, X.; Wang, Z.; Wang, Z. L. Eardrum-Inspired Active Sensors for Self-Powered Cardiovascular System Characterization and Throat-Attached AntiInterference Voice Recognition. Adv. Mater. 2015, 27, 1316−1326. (3) Pang, C.; Koo, J. H.; Nguyen, A.; Caves, J. M.; Kim, M. G.; Chortos, A.; Kim, K.; Wang, P. J.; Tok, J. B. H.; Bao, Z. Highly SkinConformal Microhairy Sensor for Pulse Signal Amplification. Adv. Mater. 2015, 27, 634−640. (4) Boutry, C. M.; Nguyen, A.; Lawal, Q. O.; Chortos, A.; RondeauGagne, S.; Bao, Z. A Sensitive and Biodegradable Pressure Sensor Array for Cardiovascular Monitoring. Adv. Mater. 2015, 27, 6954− 6961. (5) Li, C.; Cui, Y. L.; Tian, G. L.; Shu, Y.; Wang, X. F.; Tian, H.; Yang, Y.; Wei, F.; Ren, T. L. Flexible CNT-Array Double Helices Strain Sensor with High Stretchability for Motion Capture. Sci. Rep. 2015, 5, 15554. (6) Seyedin, S.; Razal, J. M.; Innis, P. C.; Jeiranikhameneh, A.; Beirne, S.; Wallace, G. G. Knitted Strain Sensor Textiles of Highly Conductive All-Polymeric Fibers. ACS Appl. Mater. Interfaces 2015, 7, 21150− 21158. (7) Ma, Z.; Su, B.; Gong, S.; Wang, Y.; Yap, L. W.; Simon, G. P.; Cheng, W. Liquid-Wetting-Solid Strategy to Fabricate Stretchable Sensors for Human-Motion Detection. ACS Sensors 2016, 1, 303−311. (8) Ge, J.; Sun, L.; Zhang, F. R.; Zhang, Y.; Shi, L. A.; Zhao, H. Y.; Zhu, H. W.; Jiang, H. L.; Yu, S. H. A Stretchable Electronic Fabric Artificial Skin with Pressure-, Lateral Strain-, and Flexion-Sensitive Properties. Adv. Mater. 2016, 28, 722−728. (9) Wang, Y.; Wang, L.; Yang, T. T.; Li, X.; Zang, X. B.; Zhu, M.; Wang, K. L.; Wu, D. H.; Zhu, H. W. Wearable and Highly Sensitive Graphene Strain Sensors for Human Motion Monitoring. Adv. Funct. Mater. 2014, 24, 4666−4670. (10) Choi, S.; Lee, H.; Ghaffari, R.; Hyeon, T.; Kim, D. H. Recent Advances in Flexible and Stretchable Bio-Electronic Devices Integrated with Nanomaterials. Adv. Mater. 2016, 28, 4203−4218. (11) Roh, E.; Hwang, B. U.; Kim, D.; Kim, B. Y.; Lee, N. E. Stretchable, Transparent, Ultrasensitive, and Patchable Strain Sensor for Human-Machine Interfaces Comprising a Nanohybrid of Carbon Nanotubes and Conductive Elastomers. ACS Nano 2015, 9, 6252− 6261. (12) Bandodkar, A. J.; Jeerapan, I.; Wang, J. Wearable Chemical Sensors: Present Challenges and Future Prospects. ACS Sensors 2016, 1, 464−482. (13) Shu, L.; Hua, T.; Wang, Y. Y.; Li, Q. A.; Feng, D. D.; Tao, X. M. In-Shoe Plantar Pressure Measurement and Analysis System Based on Fabric Pressure Sensing Array. IEEE Trans. Inf. Technol. Biomed. 2010, 14, 767−775. (14) Hwang, B.-U.; Lee, J.-H.; Trung, T. Q.; Roh, E.; Kim, D.-I.; Kim, S.-W.; Lee, N.-E. Transparent Stretchable Self-Powered Patchable Sensor Platform with Ultrasensitive Recognition of Human Activities. ACS Nano 2015, 9, 8801−8810. (15) Trung, T. Q.; Lee, N. E. Flexible and Stretchable Physical Sensor Integrated Platforms for Wearable Human-Activity Monitoring and Personal Healthcare. Adv. Mater. 2016, 28, 4338−4372.

ASSOCIATED CONTENT S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsnano.7b02767. The detailed TEM characterizations of H-RGO prepared via gold nanoparticles-catalyzed reaction, mechanical stability and electrical conductivity of the H-RGO/ CNTs/Ag electrode as a function of the number of bending cycles, schematics of the networking electrode showing the mechanical robustness and electrical connection upon bending, analytical model of the ionic senor for the coupling of mechanical deformation and voltage response, potential change of the sensor while maintaining bending state and under different bending frequency, instant response of ionic sensor under transient bending, potential output comparison of the sensors with different ionic liquids, and detailed motion monitoring of fingers, arm, shoulder, knee, and sign language recorded by binary digit array (PDF)

AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected]. ORCID

Wei Chen: 0000-0001-9527-110X Author Contributions ‡

These authors contributed equally.

Notes

The authors declare no competing financial interest. 8597

DOI: 10.1021/acsnano.7b02767 ACS Nano 2017, 11, 8590−8599

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DOI: 10.1021/acsnano.7b02767 ACS Nano 2017, 11, 8590−8599