Multifunctional Mechanical Sensors for Versatile Physiological Signals

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Surfaces, Interfaces, and Applications

Multifunctional Mechanical Sensors for Versatile Physiological Signals Detection Yu Pang, Zhen Yang, Xiaolin Han, Jinming Jian, Yuxing Li, Xuefeng Wang, Yancong Qiao, Yi Yang, and Tian-Ling Ren ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.8b16237 • Publication Date (Web): 22 Nov 2018 Downloaded from http://pubs.acs.org on November 23, 2018

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ACS Applied Materials & Interfaces

Multifunctional Mechanical Sensors for Versatile Physiological Signals Detection Yu Pang,1,2 Zhen Yang,1,2 Xiaolin Han,1,2 Jinming Jian,1,2 Yuxing Li,1,2 Xuefeng Wang,1,2 Yancong Qiao,1,2 Yi Yang,1,2 Tian-Ling Ren*1,2 1Institute 2Beijing

of Microelectronics, Tsinghua University, 100084, Beijing, China

National Research Center for Information Science and Technology (BNRist), Tsinghua

University, 100084, Beijing, China Corresponding author: [email protected]

Abstract Nowadays, the flexible and wearable mechanical sensors have attracted great attentions due to the potential applications in monitoring various physiological signals. However, conventional mechanical sensors rarely have both pressure and strain sensing abilities, which cannot simultaneously meet the demands of human subtle and large motions detection. Besides, the mechanical sensors with tunable sensitivity or measuring range are also essential for their practical applications. Herein, the graphene ink dip coating method with merits of time saving, low cost and large scale was used to fabricate the foam structured graphene sensors with both pressure and strain sensing performance. Due to the high elasticity of styrene butadiene rubber (SBR) substrate and stacked graphene flakes, the tunable mechanical sensors exhibit high gauge factor (GF) and large measuring range for specific human motions detection. The pressure sensor shows a GF of 2.02 kPa-1 with a pressure range up to 172 kPa, and the strain sensor displays a GF of 250 with a strain range up to 86%. On one hand, various detection of subtle vital signals with small strain change were demonstrated by the pressure sensor due to its flexibility and high sensitivity. On another hand, the strain sensor with large strain change shows

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excellent ability to detect various large human motions including the bending of neck, finger, wrist and knee. Interestingly, both pressure sensor and strain sensor exhibit great recognition capability for the hand-writing 26 letters. The working mechanism based on the contact area variation was also investigated by the morphology evolution and resistance model. We suppose that the foam structured graphene mechanical sensors would be promising wearable electronics for the human healthcare and activity monitoring in the future. Keywords: graphene, foam structure, pressure sensor, strain sensor, physiological signals Introduction Recently, flexible and wearable electronics have attracted great attentions due to their potential applications in various human physiological signals detection.1 In particular, many efforts have been employed to fabricate various pressure and/or strain sensors with superior performance including high sensitivity, large measuring range and long-term durability.2-4 The pressure sensor is capable of detecting the vertical applied force, which is mainly used to detect subtle human signals with small strain change, such as heart beat, breathing, phonation and so on.5-8 While for the strain sensors, they are capable of detecting the horizontal applied force and mainly exhibit the ability to detect the large strain of human motions like finger and knee bending, facial expression and walking states.9-13 Generally, the mechanical sensor with both pressure and strain detection properties can cut down the fabrication cost and extend their multifunctional applications. To date, there are rarely reports about mechanical sensors with both pressure and strain sensing properties,14-16 and their performance of GF and measuring range are still the challenge for their practical applications. Conventional pressure or strain sensors based on micro-electromechanical systems (MEMS) or semiconductors technologies are not suitable for wearable applications because of their hard and


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fragile materials.17-18 With the huge advancement of material science, the novel two-dimensional graphene with exceptional mechanical (Young’s modules ~1.0 TPa),19 electronic (intrinsic mobility 2000 000 cm2 v-1 s-1),20,21 and optical (transmittance ~97.7%)22 properties has been widely employed to fabricate various high-performance devices,23-27 especially the wearable pressure or strain sensor.2835

For instance, the strain sensor using chemical vapor deposition (CVD) graphene shows a GF of ~14

within a small strain range of 7%,28 which can only be used to detect the subtle human motions. However, the graphene pressure sensor prepared by CVD usually was fabricated on the hard silicon substrate using a suspended structure,29,30 which cannot detect physiological signals as a wearable device. Besides, the reduced graphene oxide at high temperature or by direct laser patterning was widely used as sensing material for the pressure sensor or strain sensor.31-34 The high-temperature treatment is often harmful to mechanical properties of substrate31 and laser reduction generates the uniform surface,33 which also shows the disadvantages of time consuming, high cost, and small-scale production. Therefore, the graphene inks were developed to satisfy the above demands and used to fabricate the various sensors by flexographic printing, screen, and roll-to-roll gravure,34 especially the strain sensors.35,36 However, the graphene ink mainly shows the ability to form planar film and poor flexibility, which limits the graphene ink as pressure sensor. Therefore, a combination of graphene ink with high elastic substrate should be developed for graphene based mechanical sensors. Herein, we have fabricated the graphene/polymer composite combining the foam structured SBR with graphene flake ink. Because of the high isotropic elasticity of SBR polymer, the composite exhibits both pressure and strain sensing properties with tunable performance for different thicknesses. Interestingly, the thick pressure sensor shows a positive resistance variation in low pressure range of 0-5.5 kPa while a negative resistance variation above 5.5 kPa. It displays a high GF of 2.02 kPa-1, fast



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response time of 100 ms and long-term stability. Meanwhile, the thin strain sensor exhibits a positive resistance variation up to the strain range of 86%, showing a high GF of 250 and frequency independent ability. Due to the excellent flexibility and high performance of graphene foam structured sensors, they show great potentials as wearable device to detect various human physiological signals. The pressure sensor exhibits superior capability of detecting vital signals with subtle strain changes, such as breathing, heart beats and voice recognition. The strain sensor with large strain measuring range shows promising ability to detect the human motions including limb bending, mouth speaking, knees walking and running. Besides, both pressure sensor and strain sensor display the excellent performance to distinguish the hand-writing 26 capital letters, indicating that the hand-writing signals is promising features as biometric information. Furthermore, the resistance variation induced by the pressure or strain has been investigated by the morphology evolution and resistance model. Due to the low-cost, large-scale and high-performance properties of foam structured graphene sensors, the multifunctional pressure and strain sensors show promising applications in human healthcare and activities monitoring. Besides, the desirable SBR substrate would be a promising candidate to fabricate similar mechanical sensors when combining with other active materials. Experimental Section Fabrication of graphene foam structured pressure and strain sensors. The sponge with two thicknesses of 2 mm and 5 mm was purchased from Donguan Juntai Foam Co., Ltd, which is made of high elastic SBR polymer. The graphene ink was purchased from Nanjing XFNANO Materials Tech Co., Ltd. To get a well ink distribution in the foam structure, three concentrations with the weight ratio of 1%, 5% and 10% were used to prepare the sensors at same immersion time of 12 hours. To accelerate the soak of ink solution, the foam samples need to be squeezed to eliminate air in the inner space at


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the initial stage. Then the samples dried naturally at room temperature for 48 hours in the draught cupboard. After cutting into desired shapes, the silver paste was used to lead out the copper foils at two ends of graphene/SBR and both sides of graphene/SBR for strain sensor and pressure sensor, respectively. Noted that the SBR polymer is insulator and has no contribution to the resistance variation. The size of electromechanical testing sample for pressure sensor is 1.5×1.5×0.2(0.5) cm, while 10×2.5×0.2(0.5) cm for strain sensor. It is noted that the sample size needs to be tailed for the specific applications. Characterization. The morphology and structure of fabricated sensors were characterized by the field emission scanning electron microscopy (SEM, Quanta 450 FEG, FEI Inc.) with an acceleration voltage of 15 kV. Raman spectra was carried out with a wavelength of 514.5 nm laser (Lab Ram Infinity Raman). The loading of applied force was recorded with a mechanical testing machine (SHIMADZU AGS-X), while the electrical signals of the pressure sensors were recorded at the same time by a digital electrometer (RIGOL DM 3068). Results and Discussion



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Figure 1. The fabrication and characterization of pressure and strain sensors. (a) The fabrication process of pressure and strain sensors based on the high elastic foam structure. The SEM images of foam structure under (b) low magnification and (c) high magnification show three-dimensional interconnected networks with circular shape spaces, (d) SEM image of graphene flakes on the skeleton surface. (e) Raman spectrums of SBR substrate and graphene/SBR foam structure. Figure 1a shows the fabrication process of pressure and strain sensors based on the foam structure. The concentration of graphene ink plays important role in the coating dispersion, as shown in Figure S1. The moderate concentration with proper viscosity would benefit the graphene distribution on the foam network. Firstly, the SBR foam was immersed into the desirable graphene ink solution. The foam samples were squeezed repeatedly to accelerate the soak of ink solution at initial stage. After standing for 12 hours, the redundant ink was extruded slightly and dried at room temperature. Then, they were cut into the square shape and rectangle shape as pressure sensor and strain sensor, respectively. Finally,


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the copper foils were used to lead out at both sides of square samples or at two ends of rectangle samples. Because of the feasible process, it shows excellent ability to fabricate large-scale sample with uniform graphene distribution (Figure S2). Moreover, the flexible graphene/SBR composite shows multiple folding ability, tightly twisted properties, and excellent stretchability, indicating great potential as comfortable wearable electronics. Figure 1b shows the SEM image of as-prepared graphene/SBR composite. As we can see that the SBR foam exhibits three-dimensional interconnected networks with high porosity. The pores display approximate circular shape with a diameter range of 20-200 μm. The magnified SEM image in Figure 1c clearly shows that the pores distribute in the connected skeleton. The stacked graphene layers are well adhered on the SBR skeleton surface, see Figure 1d. The Raman spectrums of SBR substrate and graphene/SBR are shown in Figure 1e. It can be seen that the Raman spectrum of SBR substrate displays a decreasing intensity with the Raman shift increasing. After the graphene ink coating there is no baseline drift in the graphene/SBR composite, indicating that thick graphene layer was coated on the SBR surface. The composite exhibits three clearly peaks around 1349, 1577, and 2719 cm-1 which belongs to the D, G, and 2D characteristic peaks of graphene. Generally, the graphite shows broad subtle peaks of 2D1 and 2D2 among 2D peak while is not observed in our sample,37 indicating good quality of graphene ink. Besides, the calculated intensity ratio of IG/I2D is 1.46 demonstrating the ink mainly consists of multilayer graphene,38 which is also confirmed by the SEM results. The intensity ratio between the D band and G band is 0.61 and a very weak D´ peak around 1625 cm-1 was observed, suggesting reasonably low defect densities due to the sub-micrometer flakes rather than to the large amount of structure defects within flakes.39-40



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Figure 2. Electromechanical properties of the graphene foam structure as pressure sensor. (a) The relative resistance variation versus the pressure of the pressure sensor with a thickness of 5 mm. (b) The relative resistance variation under low pressure loading. (c) The relative resistance response at pressure loading of 10, 15, 30, 40 and 60 kPa. (d) The response and recovery time of pressure sensor to a small loading of 360 Pa. (e) The relative resistance variation under a repeated loading of 35 kPa for 1000 cycles, and (f) a magnified image of cycle testing. After leading out the electrodes at the up and bottom sides, it can be used to detect the vertical applied force as a pressure sensor. The influence of sample thickness on the electromechanical property was investigated as shown in the Figure S3. It can be seen that the thin graphene foam shows much smaller resistance decrease compared to the thick sample in the whole pressure range. The thin pressure sensor displays a resistance variation up to 63% whereas a variation of 81% for thick pressure sensor.


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Due to the thin foam structure displays about 10% strain range decrease compared to that of thick sample, leading to less contact points among the foam networks and small resistance variation. Figure 2a shows the plot between relative resistance variation and pressure for the pressure sensor, where ΔR is the changed resistance versus the initial resistance and R is the real-time resistance when the pressure was loaded. It can be seen that the pressure sensor exhibits a slight resistance variation in the low pressure range, and then a sharp resistance decrease in the medial pressure range while a gradual resistance decrease in the high pressure. The GF of pressure sensor is defined as ΔR·R-1·P-1, where P is the applied pressure. At the middle pressure range, the calculated gauge factor is as high as 2.02 kPa-1, which almost twofold higher than the pressure sensors with large measuring range41,42 while lower than the foam pressure sensor with small measuring range.43 Interestingly, the resistance variation of pressure sensor shows a typical enhancement with pressure increasing at low range of 05.5 kPa, as shown in Figure 2b. When applying a small mechanical compression to the graphene ink coated SBR foam, the skeleton has a slight bending deformation to cause tension on the graphene layer. Due to the strong adhesion of layer-by-layer graphene coating, the microcracks would be generated and contribute to the resistance increase at the low pressure range, see Figure S4. The phenomenon also was observed in the polyurethane based pressure sensors.44,45 Figure 2c shows a repeatable response peaks at different loading pressures. The pressure sensor exhibits a passive resistance switches at the initial turning points for different pressure loadings, indicating a relaxation process at low pressure range. It is noted that relative resistance variation of the graphene foam pressure sensor is up to 78% at a pressure loading of 60 kPa, which is consistent with the result in Figure 2a. The fast response time is beneficial to detect high-frequency changed signals, contributing to a



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high resolution of measured pressure. The response and recovery time of graphene/SBR pressure sensor was tested in Figure 2d. It exhibits a short response time of 100 ms and a recovery time of 180 ms for a small loading pressure of 360 Pa. Noted that the peak around 7.5 s is ascribed to the small impact when we use the tweezer to grab the object from the pressure sensor. The stability of graphene foam pressure sensor was performed under a pressure loading of 35 kPa, as shown in Figure 2e. The resistance variation of pressure sensor shows a little decrease in the incipient cycles which is caused by the high elastic polymer substrate. A rapid compressive pressure decrease is observed at the initial stages and the mechanical hysteresis presents in each stress-strain curve (Figure S5). During the loading and unloading process the stress and strain could return to the original points, indicating that the graphene/SBR composite completely recover without plastic deformations (Figure S6). It exhibits relative long-term stability after incipient resistance variation decease, as shown in the magnified Figure 2f. As mentioned above, we can see that the thick foam sample have merits of a high sensitivity of 2.02 kPa-1 within a large measuring range of 5.5-40 kPa.


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Figure 3. Electromechanical properties of the strain sensor based on graphene foam structure. (a) The relative resistance variation versus strain of the strain sensor with a thickness of 2 mm. (b) The recorded resistance variation of strain sensor at the low strain range of 1.5%. (c) The resistance response of strain sensor towards the saw-tooth wave strain variation under the strain ranges of 6%, 13%, 19%, 25%, 31%, and 44%. (d) The relative resistance variation of strain sensor at the frequency of 0.05, 0.075, 0.1 and 0.2 Hz. (e) The relative resistance variation under a repeated strain of 30% for 500 cycles, and (f) the magnified image of cycle testing. Due to the high elastic properties of substrate polymer, it shows great potential to fabricate the highperformance strain sensor. To investigate the influence of thickness on the electromechanical property, we have measured the resistance variation versus strain for different thick samples, see the Figure S7. The thin strain sensor exhibits nearly triple of maximum resistance variation compared to the thick



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strain sensor. The mechanical result shows that both thin and thick samples display increased tensile force after graphene ink coating, indicating an increased elastic module. The elastic modulus of the foam structure and its composite are about 127 and 151 kPa, respectively. Due to both resistance increase in the horizontal direction and resistance decrease in the vertical direction contribute to the whole resistance variation, the thick sample has more vertical resistance contribution to the whole resistance, leading to the decline of whole resistance increase. The thin foam structure could contribute to high GF and comfortable attachment on human skin, which would be desirable candidate for practical applications. Figure 3a shows the relative resistance variation of graphene strain sensor with the strain increasing. Herein, we defined the GF of strain sensor as ΔR·R-1·ε-1, where ε is the strain. As we can see that the resistance variation of strain sensor shows three different ratios in the three strain sections. It shows the lowest GF of 37 at low strain range less than 45%, the highest GF of 250 at medial strain range of 45-75%, and a GF of 117 above 75% strain. Noted that the strain sensor shows a large strain change up to 86%, which is much larger than some of previous reported strain sensors based on CVD graphene.46,47 The graphene/SBR foam sensor exhibits preferable advantages compared with those of previous reported pressure sensors, see the Table S1. The porous SBR structure provides more free space for large deformation or high stretchability, and surface adhesion of graphene flakes offer more remarkable resistance variation than that immersed sample. Those reasons contribute to the high sensitivity in a wide range. Moreover, the influence of cutting areas among the strain sensor on electromechanical properties were investigated, see the Figure S8. The results indicate that the reduction of effective area can decrease resistance variation, especially in the high strain range. The largest cutting area leads to the lowest resistance variation at the strain of 86%. Due to the excellent stretchable and fast response abilities, the strain sensor shows distinguish sensing performance in a


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small strain range. Figure 3b shows the resistance variation versus strain at the strain range of 0-1.5 %. It exhibits a GF of 15 and high linearity between the resistance variation and strain, which would benefit for the detection of subtle strain change. Besides, the strain sensor shows superior dynamic response to different strain changes. As shown in Figure 3c, it can be seen that the foam structured strain sensor shows fast response to the saw-tooth wave strain under the strain range of 6%, 13%, 19%, 25%, 31%, and 44%. The strain sensor shows repeatable signals for different strain changes and enhanced intensity with the strain range increasing, indicating fast response and excellent recovery ability. Figure 3d shows the resistance response of strain sensor under the strain of 23% at the frequency of 0.05, 0.075, 0.1 and 0.2 Hz. It is noted that the increased frequency has no obvious influence on the resistance variation, indicating frequency independent ability which would be helpful to detect slow and fast human motions. Figure 3e shows the resistance variation at a repeatable strain change of 30%. At the initial state, the strain sensor shows a little electrical overshoot response which is caused by the elastic SBR substrate, which is usually observed among the high elastic strain sensors.48 It maintains stable state over long-term cycles (Figure 3f), indicating excellent durability for its applications. It can be seen that high elastic SBR foam not only can be used to fabricate out-standing pressure sensor but also high-performance strain sensor, which shows the advantages to detect various human physiological signals compared to the single-functional mechanical sensor.



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Figure 4. Detection of human vital signals with subtle strain change using the pressure sensor. The human breathing detection using pressure sensor fixed on the (a) mask, (b) heart area, and (c) lung cavity. The human pulse detection using pressure sensor fixed on the (d) neck artery, (e) wrist artery and (f) fingertip, showing promising potentials in physiological information acquisition. As we all known, the human vital signals play significant point in assessing our healthcare or rehabilitation states, which is beneficial to disease diagnose and treatment. Due to the high sensitivity and flexible ability, the pressure sensor shows excellent ability to detect vital signals with subtle strain change, as shown in Figure 4. It can be seen in Figure 4a that the human breathing signal can be recorded by pressure sensor fixed on the mask. The relative resistance shows about 0.2% variation to each breathing cycle and a rate of 26/min, indicating excellent sensitivity to the airflow pressure variation and non-contact respiration monitoring property. Interestingly, both the breathing wave and heart pulse can be detected after we fixed the pressure sensor on the heart area, as shown in Figure 4b. The pressure sensor exhibits an increased response of 0.37% and a breathing rate of 23/min. Apart from the thoracic cavity fluctuation caused by inhale and exhale, the heart beats overlapped with breathing wave can be clearly observed, indicating superior resolution ability for different subtle


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signals. When fixing the pressure sensor on the lung cavity, no heart beat signal is observed on the breathing wave, as shown in Figure 4c. An increased relative resistance response of 0.4% is achieved due to the effect of large chest movement. Generally, the heart beat is another important vital signal for human life which needs to be monitored accurately. Figure 4d shows the relative resistance variation of pressure sensor fixed close to the neck artery. It can be seen that neck pulse displays weak response of about 0.6% and a heart beats of 84/min. When fixing the pressure sensor on the human wrist, Figure 4e shows increased response intensity of about 0.75% and long-term monitoring stability. The magnified inset in Figure 4e shows two distinct characteristic peaks corresponding to the P-wave and D-wave characteristic peaks,32 which could be helpful to determine the pulse condition. Finally, the pressure sensor is also capable of detecting the fingertip tiny pulse, as shown in Figure 4f. The pulse shows the lowest resistance variation of ~0.4% among three detecting regions due to weak blood pressure in the fingertip. In all, due to the high sensitivity and flexibility the foam structured graphene/SBR demonstrates promising potentials to detect various subtle physiological signals.



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Figure 5. The relative resistance variation of pressure sensor when writing on its surface using a pencil, showing superior distinction ability for 26 capitals. In addition, we have investigated the phonation recognition property of pressure sensor, showing excellent ability to detect throat vibration (Figure S9). When the tester spoke words including “graphene”, “pressure” and “sensor”, the repeatable feature peaks was detected for each word. Different words exhibit some subtle characteristic peaks, indicating syllable distinction ability. The pressure sensor was further put on the loudspeaker to detect the sound wave fluctuation. Compared to the human phonation, the pressure sensor shows much smaller resistance variation of less than 1%. Similarly, the characteristic peaks are recorded for different words while very clear subtle peaks are


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observed. The pressure sensor shows excellent capability to detect the high-frequency voice, like music rhythm. The resistance shows highly synchronous variation following the audio signals. Furthermore, we firstly have investigated the pressure sensor to recognize the hand-writing capitals on the surface (Figure 5). The varying force can be recorded when the pencil write on sensor surface, and resistance variation would be detected due to various stroke numbers or writing weight for different words. Thus, it exhibits characteristic and repeatable peaks for each capital. For instance, the capital A shows three subtle peaks while two subtle peaks for capital N. Because of the distinct recognition properties for hand writing, it shows promising potential as the effective authentication technique. To investigate the performance of pressure sensor at high loading, it was fixed on the heel to detect walking and running signals, see Figure S10. The pressure sensor shows a repeatable resistance variation of about 80% for walking state, and landing and lifting processes can be clearly observed by the flat region of resistance variation. For running it shows sharp impact peaks for each step and almost triple frequency enhancement, demonstrating excellent property for large human motions.

Figure 6. Detection of human activities with large strain change using the strain sensor. (a) The strain sensor fixed on the human face close to mouth for speaking words including “graphene”, “porous”, “strain” and “sensor”. Bending degree detection of strain sensor fixed on the human (b) neck, (c) finesse, (d) wrist, (e) finger joint and (f) knee joint, demonstrating excellent detection ability for large



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human motions. Due to the large strain range and high sensitivity of as-prepared strain sensor, it was widely used to detect various human motions with large strain change, as shown in Figure 6. Figure 6a shows the resistance variation of strain sensor fixed close to the mouth for speaking detection. Due to the mouth opening and closing procedure during speaking, the strain sensor exhibits repeatable peaks and subtle feature peaks for different words, showing similar capability of word identification with our pressure sensor. The strain sensor shows a low resistance variation of ~10% because of the small strain change of mouth speaking. When the strain sensor was used to detect neck bending with much large strain change, it shows an over 100% resistance variation for all the bending degrees (Figure 6b). With the bending degree increasing the strain sensor exhibits an enhanced signal intensity and repeatable signals for a certain bending angle (Figure S11), which can set a threshold value to remind incorrect setting posture. The finesse motion is one of common activities in daily life, which also can be detected by our strain sensor. Figure 6c shows that different bending degrees could be measured by the strain sensor fixed on the finesse. It shows sharp resistance enhancement for finesse bending angle increasing due to the large strain change. Noted that the strain sensor displays the highest resistance variation among all the limb motions. Apart from finesse motion the wrist bending degrees can also be distinguished, as shown in Figure 6d. Interestingly, the strain sensor on the wrist not only shows the excellent properties to detect different bending angles but also records the signals for the hand-writing letters (Figure S12). The strain deformation is mainly induced by the stretching, bending and twisting of wrist during the capital writing, which is different from the pressure sensor to record the force track directly. Similarly, the strain sensor exhibits repeatable characteristic peaks for the 26 capitals while different peak shapes for each letter, indicating promising potential in hand-writing word recognition.


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Moreover, the strain sensor can be used for detecting finger bending with small strain changes, as shown in Figure 6e. For all the finger bending angles, the strain sensor shows a small resistance variation range of 45-70%. Finally, the strain sensor was fixed on human knee to detect different bending and walking states. Figure 6f shows that the relative resistance variation of strain sensor displays sharp enhancement with knee bending degree increasing, showing a resistance variation of 600% for the largest bending angle. The walking and running states can also be detected by the strain sensor, and a signal with high-frequency variation and large resistance intensity is observed for running state.

Figure 7. The working mechanism of pressure sensor and strain sensor. The morphology evolution of



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pressure sensor under horizontal force compressing at its (a) initial state, (b) small pressure loading and (c) large pressure loading. The morphology evolution of strain sensor under horizontal force stretching at its (d) initial state, (e) small strain and (f) large strain. The resistance model of (g) graphene/SBR foam structure, (h) corresponding models for pressure sensor under small and large compression, and (i) corresponding models for strain sensor under small and large strain. Generally, there are three kinds of working mechanisms contribute to the graphene based piezoresistive mechanical sensor. The first one is the energy band open occurred in the monolayer or few layer graphene,49 leading to the resistance enhancement with strain increasing. Besides, the tunneling effect among the graphene sheets or graphene quantum dots can induce the electrically percolatingnetwork and contributes to the resistance variation.50,51 Finally, the continuous and discontinuous conductive path caused by contact area variation is a widely used model for various pressure and strain sensors.52,53 In this work, due to the micro graphene flakes adhered on the foam skeleton, the change of contact area would result in the resistance variation for pressure sensor and strain sensor. For the pressure sensor, it can be seen from Figure 7a that all the pores show circle shape without external pressure loading. Noted that the graphene flakes were coated on the skeleton surface rather than immersed into the skeleton. Thus the cross section of SBR in the white area can be observed. The electric current mainly flows the whole pressure sensor through the interconnected networks, which contributes to the initial resistance. As we discussed before, the initial positive resistance variation attributes to the micro cracks on the skeleton surface. Herein, we mainly investigate the morphology evolution at middle and high pressure ranges. During the middle pressure loading the large circle would undertake the force and shows deformed oval shape, as shown in Figure 7b. The increased contact area among the 3D networks mainly contributes to increased current paths and resistance decrease. Besides,


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the pre-existing cracks on the foam surface would be closed to each other. With loading pressure increasing, Figure 7c shows that both small and large pores are remarkably deformed, showing almost closed seal line. Therefore, more and more contacted area contributes to the further resistance decline. Due to the high elastic SBR substrate large pressure was needed to make further deformation, namely the change rate of contact area per pressure unit dramatically is decreased. Thus the resistance variation exhibits huge decrease to a saturation condition for the high pressure loading. The morphology evolution of strain sensor under different strains has been investigated to explain the resistance variation along the stretch extending. Figure 7d shows the circle shape of foam structure at the initial state. Under the small strain, it can be seen from Figure 7e that all the pores show oval shape due to the stretching of horizontal force. For x-axis direction, on one hand, the overlapped graphene coating layer on the stretching skeleton surface would be separated (see the inset in Figure 7e), leading to the resistance increase. On another hand, as we discussed for the pressure sensor the compression in vertical direction would contribute to the resistance decrease. At the small strain of 45%, the obvious thickness decrease can be observed (Figure S13), demonstrating the contact area increase in y-axis direction. Therefore, the interacting effect of adverse resistance variation contributes to the whole resistance variation, which results in a small GF within small strain range. Noted that the strain sensor is a high-aspect ratio shape and whole resistance increase still plays a dominant role. However, the thickness displays almost same thickness at the strain of 80%, indicating that the resistance increase along the horizontal direction mainly contributes to whole resistance variation. With the strain further increasing, the fresh area without graphene flakes coating widely appears, as marked with arrows in Figure 7f. Thus the resistance shows a sharp increase and large GF obtained at high strain range. Generally, the strain sensor shows increased GF with the strain increasing,48,54 while



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our strain sensor shows decreased GF above strain of 75%. Because large fresh area without graphene coating has largely occurred, the current need to flow more long distance through the network to build the conductive path. With the strain increasing, the compression along the vertical direction would display a saturation and high density of networks could be obtained, see the Figure S14. Thus, the saturated connected networks contribute to an increase of conductivity and small negative effect to the relative resistance variation. It can be concluded that the synergy of y-axis resistance decrease and xaxis resistance increase contribute to low GF at small strain range while the generated fresh area in xaxis direction leads to large GF at high strain range. To better understand the resistance variation in the pressure and strain sensor, the resistance models have been established as shown in Figure 7g-i. Figure 7g is the initial resistance model for the mechanical sensor without force loading. For each circle, we can see that four resistance contribute to total resistance, which can be expressed as following equation: 𝑅

𝑅𝑡𝑜𝑡𝑎𝑙 = 2𝑅 + 2

(1)

where R is the resistance for each segment. For pressure sensor at small pressure loading, the circle pore exhibits oval deformation and increased contact area at interconnection between two circles (see Figure 7h). Thus the resistance exhibits a decrease to r1, and the total resistance can be expressed as: 𝑅𝑡𝑜𝑡𝑎𝑙 = 2𝑟1 +

𝑟1 2

(2)

With the pressure increasing, the circle displays severe deformation to form a seal line and more connections are established. Then the total resistance can be expressed as: 𝑅𝑡𝑜𝑡𝑎𝑙 = 2𝑟2 +

𝑟2 3

(3)

where r2 is the further increased resistance for each segment. The resistance models of strain sensor at small and large strain are shown in Figure 7i. Under the small strain, the separation of overlapped


Page 23 of 32 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


graphene flakes mainly contributes to the resistance increase. The total resistance for each unite can be expressed as: 𝑅𝑡𝑜𝑡𝑎𝑙 = 2𝑅1 +

𝑅1 2

(4)

where the R1 is increased resistance for each segment. With the strain increasing, the large amount appearance of fresh area contributes to the sharp enhancement of resistance. Then the total resistance can be expressed as: 𝑅𝑡𝑜𝑡𝑎𝑙 = 2(𝑅1 + 𝑅2) +

𝑅1 + 𝑅2 2

(5)

where the R1 and R2 represent the resistance of disconnected graphene flakes and the resistance of fresh area, respectively. Conclusion In summary, based on the high elastic foam structure we have fabricated graphene mechanical sensors with both pressure and strain sensing properties. The graphene ink used in the method shows time-saving, low-cost and large-scale advantages compared with CVD growing or high temperature reduced graphene sensors. Due to the flexible, stretchable and porous SBR substrate, the pressure sensor and strain sensor show tunable properties of high sensitivity and large measuring range for different thicknesses. The maximum GF of pressure sensor and strain sensor are as high as 2.02 kPa-1 and 250, respectively. Because of the wearable and high-performance properties, they show great potentials to detect various human vital signals and activities. The pressure sensor exhibits excellent ability to monitor human subtle vital signals with small strain change, such as heart beat, breathing, and phonation. The strain sensor with large strain range displays superior performance to detect human motions including speaking, bending of neck, wrist, finger and knee. Besides, both pressure sensor and strain sensor can record the hand-writing force track and show characteristic and repeatable signals for



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26 capitals, showing a promising information for personal identification. Meanwhile, the working mechanism of resistance variation under different pressures or strains have been investigated based on the contact area change and resistance model. The multifunctional mechanical sensor with foam structure could be promising wearable electronics for human healthcare monitoring in the future. Acknowledgements This work was supported by National Key R&D Program (2016YFA0200400), National Natural Science Foundation (61574083, 61434001), National Basic Research Program (2015CB352101), Special Fund for Agroscientific Research in the Public Interest of China (201303107), and Research Fund from Beijing Innovation Centre for Future Chip. The authors are also thankful for the support of the Independent Research Program of Tsinghua University (2014Z01006), and Shenzhen Science and Technology Program (JCYJ20150831192224146). Y. Pang and Z. Yang contribute equally to this work. We confirm that all the photos were taken by the authors of this manuscript. All the photos were taken by the authors of this work. Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Photos of as-prepared sensors, electromechanical and stress-strain curves, SEM images, sensor applications, performance comparison table (PDF). All the photos were taken by the authors of this work. Reference (1) Trung, T. Q.; Lee, N. E. Flexible and Stretchable Physical Sensor Integrated Platforms for Wearable Human-Activity Monitoringand Personal Healthcare. Adv. Mater. 2016, 28, 4338-4372. (2) Lee, W. S.; Lee, S. W.; Joh, H.; Seong, M.; Kim, H.; Kang, M. S.; Cho, K.; Sung, Y.; Oh, S. J.


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TOC


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Multifunctional Mechanical Sensors for Versatile Physiological Signals

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