Sheath-Core Fiber Strain Sensors Driven by In-Situ Crack and Elastic

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Sheath-Core Fiber Strain Sensors Driven by In-Situ Crack and Elastic Effects in Graphite Nanoplate Composites Jianfeng Wu, Ziyuan Ma, Zheng Hao, John Tao Zhang, Pengfei Sun, Minghao Zhang, Yangkun Liu, Yushu Cheng, Yu Li, Bo Zhong, Tao Zhang, Long Xia, Wang Yao, Xiaoxiao Huang, Huatao Wang, Haiping Liu, Feng Yan, Chien En Hsu, and Guozhong XING ACS Appl. Nano Mater., Just Accepted Manuscript • DOI: 10.1021/acsanm.8b01926 • Publication Date (Web): 09 Jan 2019 Downloaded from http://pubs.acs.org on January 10, 2019

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ACS Applied Nano Materials

Sheath-Core Fiber Strain Sensors Driven by In-Situ Crack and Elastic Effects in Graphite Nanoplate Composites Jianfeng Wua,b,1, Ziyuan Mac,1, Zheng Haoa,b, John Tao Zhanga,b, Pengfei Sund, Minghao Zhanga.b, Yangkun Liua,b, Yushu Chenga,b, Yu Lia,b, Bo Zhonga,b, Tao Zhanga,b, Long Xiaa,b, Wang Yaoa,b, Xiaoxiao Huange, Huatao Wanga,b,*, Haiping Liud,*, Feng Yanf, Chien En Hsug and Guozhong Xingg,* a. School of Materials Science and Engineering, Harbin Institute of Technology at Weihai, 2 West Wenhua Road, Weihai 264209, China b. Weihai Engineering Research Center of Graphite Deep-processing Technology, 2 West Wenhua Road, Weihai 264209, China c. Weihai No.1 High School, 75 West Wenhua Middle Road, Weihai 264209, China d. Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications, Institute of Spacecraft System Engineering, CAST. Beijing 100094, China e. School of Materials Science and Engineering, Harbin Institute of Technology, Harbin 150080, China f. Department of Metallurgical and Materials Engineering, The University of Alabama, Tuscaloosa, Alabama, 35487, USA g. United Microelect Corp. Ltd., 3 Pasir Ris Dr 12, Singapore 519528, Singapore

1 Jianfeng Wu and Ziyuan Ma contribute equally to this work. Correspondence and requests for materials should be addressed to H.WANG (E-mail: [email protected]*), H.LIU (E-mail: [email protected]*), or to G.XING (E-mail: [email protected]* and [email protected]*)

KEYWORDS: Graphite nanoplatelets (GNPs), Sheath–core structure, Crack and elastic effects, Robotic mechatronics, Strain sensors

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Abstract The flexible and stretchable electronics, e.g., graphite nanoplatelets (GNPs) based nanocomposite devices, have attracted great interest due to their potential application in healthcare, robotics, and mechatronics technology. However, the deficient sensors with manipulation of low sensitivity, sluggish responsivity, sophisticated fabrication process and poor repeatability notoriously limit their industrial applications. To enhance the spontaneous sensitivity, flexibility and wearability in GNPs based strain sensors, in this report, the synergistic crack and elastic effects engineering is employed and in turn significantly enhances the sensitivity with the gauge factor of 20 at the strain of 30% and the stability in our developed sheath-core fiber (SCF) strain sensors. Upon reliable devices integration, it is demonstrated that the developed SCF strain sensor could detect the movement of human joint effectively with generating a resistance change rate ΔR/R0 up to 600%. Furthermore, a controlling devices system based on SCF strain sensor has been manufactured in circuit level to realize the real-time control of a robot hand, such as copying gestures and playing piano.

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Introduction As the most promising platform for designing and building multifunctional flexible and scalable electronic devices, mechatronics is well known for its unique multidisciplinary coupling and the combination of mechanical engineering,, electronics, systems engineering and control engineering.1-3 There are various types of multifunctional flexible and stretchable sensors, such as stress, strain, humidity, temperature, capacitance, pH value, etc.,4-15 among which the strain sensor has attracted great interests for their potential application in robotics and mechatronics technology. Recently, stretchable strain sensors, as one of the critical components in wearable electronics, are developed to achieve the real-time mechanical feedback for applications in healthcare, sports monitoring, soft exoskeleton suits, and so forth.16-21 Traditional strain sensors, on the basis of metal foils and semiconductors at the most of time, are not suitable for wearable sensors because of their poor mechanical compliance and low durability of working range (< 5% strain).22,23 While some typical emerging materials with microscale flexibility, for instance, carbon-based, polymer based and metal nanowires have been widely employed in some flexible and stretchable fiber shaped strain sensors.24-28 In 2016, Amjadi developed a flexible strain sensor with microcrack structure using composite thin films, and as-prepared crack leads to a high GF (522.6), and can be applied in human motions and soft robotics.29 In 2018, Wang et al. reported a strain sensor with continuous micro-cracks formed in CNTs / PDMS composites, and the formed micro-crack made the sensor reach a high gauge factor (87), promised typical 3



applications in strain detection, artery pulses, vibration and human motions.30 In 2017, Hua et al. demonstrated a yarn strain sensor using graphene/polyurethane composites through the layer-by-layer method, and the highest gauge factor can reach to 86.86 when the graphene concentration is 1.0 wt. % and the cycles of the coating are up to 9.31 For most of previous research, flexible strain sensors were only used for detecting a specific signal, apparatus collected signal, and researchers generally compiled a trend chart, however, there has been seldom works addressed on the sensors work as a controlling device upon the limitations of low sensitivity, hard fabrication process and poor repeatability. In the present work, we developed a simple and scalable preparation method for graphite nanoplates (GNPs) based composite sheath-core fiber (SCF) strain sensor. A simple system has been made to allow the SCF strain sensor to be used as a controlling device to precisely manipulate the robot hand. The SCF strain sensors have a high gauge factor of 20 at the strain of 30% and excellent repeatability (> 1000 cycles) owing to the established synergistic crack and elastic effects. Moreover, the developed SCF strain sensor is also highly sensitive to joint motion detection, enabling its applications as strain sensors with great stability and reliability as well as the controlling device for a robot hand. Results and Discussion A schematic diagram for the SCF sensor preparation process and corresponding structural difference between stretching and released state is demonstrated in Figure 1. 4


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Briefly there are five steps: (1) GNPs were prepared using Expanded Graphite as raw material by ultrasonic exfoliation method, Figure S1 shows the TEM photos of as-prepared GNPs; (2) With the liquid state PU/Solvent, well-distributed GNP/PU/Solvent compounds were fabricated by filtration, drying, and stirring, etc.; (3) SCF strain sensors were then prepared by the roll to roll process (detailed in Figures 2a-2d), simultaneously the microcrack structures on the conductive sheath were formed; (4) SCF strain sensors were then assembled by bonding electrodes. And the diameter of elastic core-rubber fiber is ~0.5 mm, the thickness of conductive sheath is ~20 m. (5) Lastly, the as-obtained SCF strain sensor was stretched and released for downsized fiber sensor between the observed crack closing (top, releasing state) and crack opening (bottom, stretching state). The “breathable” microcracks worked as the key structural characteristics, enabling the highly sensitivity of the SCF strain sensor. Figures 2a-2c show a schematic of the roll-to-roll fabrication process, including the following steps: (1) the elastic core-rubber fiber was induced by rolling into GNP/PU dispersion; (2) after dip-coating, the wet GNP/PU coated fiber was stretched and dried immediately with the tensile strain ~50% at temperature of ~80 oC.

Because of the tension during the whole drying process, the surface of conductive

sheath will generate randomly distributed microcracks. After drying, the as obtained SCF strain sensor was stretched to achieve crack opening as shown in Figure 2d. Figures 2e-2h illustrate the structural evolution by every step corresponding to the SEM images in Figures 2i-2l. Figure 2i shows the SEM image of elastic core-rubber 5



fiber, Figure 2j depicts the SEM image of the GNP/PU coated fiber which was dried without tension that suggests the surface of conductive sheath without microcrack. The crack formation is attributed to the strain that resulted from the stretching progress, and the orientation of the crack is perpendicular to the stretching direction due to the strain direction. The formation of crack might be caused by the tension of drying and the stiffness mismatch between the top conductive layer and bottom elastic polymer layer (rubber).29,30 As the shape and the gap, namely the length of crack and density can be influenced by the ratio of GNPs and PU, strain and temperature. We had examined the ratio of GNPs and PU, and found the optimal ratio 10:2 with the excellent sensitivity and flexibility of the strain sensors. Aimed at an engineered control upon the stability and repeatability of as-prepared SCF, the strain and curing temperature during preparation process was set as 50% and 80 oC, respectively. Figure 2k presents the SEM image of the SCF, the marked dash box shows the closing microcrack. To observe the opening structure of microcrack, the SCF was stretched at a strain of 30%, it is clear to observe that the microcracks are opening under the strain, as indexed in Figure 2l. Figures 2m-2o are the radial structure SEM images of Figure 2i-k, respectively. Figure 2p demonstrates the SEM image observed at the interface of the core-rubber and conductive sheath, where a uniform thickness distribution of the conductive sheath is achieved.

In Figure 3a, the XRD results show that the sharp peaks at 26.3° and 54.5° are corresponding to the (002) and (004) planes, respectively, which are originated from 6


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their 2D layer structure, and the (002) peak reveals a lattice distance of ~3.34 Å in the developed GNPs. Figure 3b shows the Raman analysis of GNPs, wherein the peaks at ~1352 cm−1, ~1580 cm−1 and ~2714 cm−1 are corresponding to the D band, G band and 2D band, respectively. And the weak intensity of D band reveals that there is a very small amount of defects in as-prepared GNPs.32 Figure 3c shows the FTIR spectrum of as-prepared GNP/PU nanocomposite. The peaks at ~1725 cm-1 mean the existence of carbonyl groups in the composite film, and the peaks at ~2953 cm-1 and ~ 3328 cm-1 are the characteristic peaks of C-H and N-H groups. It is obvious that the carbonyl group reveals the existence of urethane.33 In addition, the peaks at ~1527 cm-1 and ~1134 cm-1 meaning the presence of C-N-H and -C-O-C- are consistent with the previous research.34

To measure the SCF strain sensors’ performance, different stains (i.e., 10%, 20%, 30%) are applied, and the curves of relative resistance change rate (ΔR/R0 %) with time (s) are indicated in Figure 3d, the inset images are their schematic diagram of SCF strain sensors with various strain, in which the higher the stain is, the larger the crack becomes. The performance under the cyclic tensile strain of 20% is shown in Figure 3e (1000 times), the right-top inset shows representative zoom-in cycles as marked in the shadow area, suggesting the repeatability with reliable endurance. Figure 3f is the corresponding simulating plot of the relative resistance change with various strain, and the fitting equation is R/R0 =17.11S, where S represents the strain, suggesting the linear relationship between strain and (ΔR/R0). It is clear that the 7



fitting curve shows the different linearity in two regimes (0-20% and 20-30%). When the strain is 0-20%, the openness of the crack is increased with the strain linearly mainly because of the crack effect. However, when the strain is between 20% and 30%, the conductive sheaf is not only under crack effect, but also under much more elastic effect. Although it still shows linearity in 20-30%, it does not have a linear behaviour up to 30% in the whole region.

To further explore the sensing mechanism of SCF strain sensor, especially when we find the close-open-close structure changing is like the breathing of the crack to some extent. Here, we propose a model of crack and elastic effects to explain such a special strain-dependent sensing mechanism, as shown in Figure 4. The SCF strain sensor will be stretched when a strain is applied (Figure 4a, 4b, 4c, and 4f) firstly, the microcrack will be opened from the initial closing state, which will significantly promote the resistance change, i.e., crack effect, as shown in Figures 4b and 4c. Secondly, some area of the conductive sheath didn’t generate microcracks, when the SCF strain sensor is under tension. This area without microcracks will be stretched too, so the resistance of this area will rise due to the increasing distance between GNPs. When the tension is relaxed, owing to the elasticity of the GNP/PU coating, the SCF strain sensor will return to the releasing state, which called elastic effect (Figures 4d and 4e). Furthermore, we have investigated the crack effect and elastic effect in the contribution of relative resistance response under different strain intensities. Figure 4g shows the corresponding schematic diagram of two different 8


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structure sensors: Figure 4g (i) without microcracks to investigate the elastic effect in the contribution of relative resistance response, while Figure 4g (ii) SCF strain sensor to investigate both elastic effect and crack effect in the contribution of relative resistance response. As shown in Figure 4h, obviously, the crack effect dominates the relative resistance changing with a ratio of elastic effect lower than 5%. Thus, we believe that the special roll-to-roll process creates the microcracks, and the breathing of microcracks does improve the sensitivity of SCF strain sensor, particularly, the fabrication process is simple and scalable.

To ascertain the potential applications in human action, the stain sensors were applied to detect the joint motion, involving finger, wrist and elbow. They were attached to fingers and utilized to detect corresponding movement. The SCF strain sensor was attached on index finger under a releasing state with adhesive tape, the finger is straight as revealed in Figure 5a, and both sides of the sensor were fixed on the first and third joints of the index finger. The value of R/R0 was measured with the movement of fingers to a certain degree. The photograph in Figure 5a indicates the various bending angles (e.g., 30, 60 and 90) of the index finger with SCF strain sensor. Figure 5b illustrates the resistance change with time. Note that the SCF strain sensor is bent with index finger 10 cycles at each bending angle. Obviously, the SCF strain sensors show a sensitive and quickly response to the bending process. During the bending of fingers, the resistance is quickly increased, subsequently, when the finger was bent to the straight state, the resistance was decreased and then was 9



recovered. Figure 5c depicts the fitting plot of R/R0 with various bending angle, and the fitting equation of R/R0 = 3.72, where  represents bending angle of the index finger, indicating the linear relationship between bending angle and (ΔR/R0). Moreover, the SCF strain sensor is also sensitive to the wrist motion. It is known that the joint of wrist can be bent back and forth (for different people the angle of bending back and forth are also different). Here, we mount the SCF strain sensor outside of the wrist to detect the action of wrist varus, as indicated in Figure 5d. One half of the SCF strain sensor was adhered to the handback under a releasing state with adhesive tape while another half was attached on the arm, and the hand is straight as shown in the first picture of Figure 5d. Figure 5e is the curve of resistance change with time. Figure 5f demonstrates the R/R0-time curve of a cycle of wrist varus. It is noted that the wrist was bent to the maximum angle of the volunteer during the three cycles. Noticeably, the SCF strain sensor has stable feedback to the wrist varus. Furthermore, the wrist inside was covered by SCF strain sensors to measure the movement of wrist valgus, as indicated in Figure 5g. One half of the SCF strain sensor was adhered to the palm side under a releasing state with adhesive tape while another half was on the arm, the hand is straight as shown in the first picture of Figure 5g. The plot of resistance change with time and the R/R0-time curve of a cycle of wrist valgus are shown in Figure 5h and 5i, respectively. The wrist was also bent to the maximum angle of the volunteer during the three cycles and the SCF strain sensor has stable feedback to the wrist valgus too. Because of the different angle in the two kinds of 10


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wrist movement, the SCF strain sensor is under more strain when the wrist was bent back (wrist valgus), that is represented in the relative resistance change (R/R0: 120% in wrist varus compared with 600% in wrist valgus), demonstrating that the SCF strain sensor can recognize the difference between two kinds of wrist movement. Except of the excellent sensitivity at ~1 cm level movement of finger bending, and ~2-3 cm level movement of wrist bending, the SCF strain sensor also shows outstanding performance in the sensing of ~10 cm level movement of elbow bending. The SCF strain sensor was bonded on the outside of the elbow and bent with the arm. One half of the SCF strain sensor was adhered to the outside of the elbow close to the fore-arm under a releasing state with adhesive tape while another half was on the rear-arm, the arm is straight as shown in the first picture of Figure 5j. The various bending angles are 45, 90 and 135 corresponding to the photograph in Figure 5j, and the plot of resistance change with time is shown in Figure 5k. Notably, the SCF strain sensor is bent with arm 10 cycles at each bending angle. Obviously, the SCF strain sensor showed fast and sensitive feedback to different bending angle, and R/R0 was increasing with the adding of bending angle. Figure 5l is the fitting plot of R/R0 with bending angle, and the fitting equation of R/R0= 9.29, where  represents bending angle of the elbow, indicating the linear relationship between bending angle and (ΔR/R0).

As aforementioned, the majority of flexible strain sensors in previous works focused on the sensitivity and the detection of physiological activities, few of them 11



can realize the real-time signal collection, transformation into CPU system and then driving robots.27-31 Herein, a designed complete system has been made to allow the SCF strain sensor to be used as a signal collecting device then to further control Robot hand. As shown in Figure 6a and Figures S2-S5, a smart sensing glove with five SCF strain sensors on each finger and CPU board has been fabricated, in order to collect the signal of human hands and drive Robot hand. Through a central processing unit (CPU), the signal collected by five SCF strain sensors can be stored and transformed into the control system of Robot hand in real-time, and then driving Robot hand. Therefore, Robot hand can “copy” the action of our hand, including various gestures and even playing piano.

In order to adapt to various hand grasping, a self-learning model has been proposed to help the CPU record the resistance threshold and assign robot hand instructions due to various bending state, while everyone try to use this sensing glove. When the SCF strain sensor is at a releasing state, namely the bending angle of the finger at a minimum angle (min), the resistance is held in a certain value and in turn recorded by CPU as Rmin. In contrast, when the SCF strain sensor is at a stretching state with a bending angle of the finger at a maximum angle (max), the resistance is up to a higher value, consequently recorded by CPU as Rmax, as shown in Figure 6b. With the recording Rmin and Rmax, the built-in program of CPU will divide the bending angle difference (max - min) within the interval (Rmax- Rmin) of Rmax and Rmin linearly as

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designed in Figure 6c. The pre-set linear calculation formula of the real-time bending angle is as the following,

Bending angle  R 

 max   min R max  R min

where R represents the real-time resistance monitored by CPU. Aiming at a clear circuit level integrated devices system demonstration, the five SCF strain sensors are separately connected with corresponding precision resistance, and consequently are inter-connected with corresponding pins of an analog switch chip HCF4051, as shown in Figure 6d. With the control of a microcontroller, only a set of analog signal can be allowed by chip HCF4051, output to the operational amplifier of AD620 at a time. In order to gain higher accuracy and quicker response, 100 voltage signals from one circuit of five sensors are collected and then averaged by the microcontroller in several milli-seconds. After the signals of one sensor are collected in short time, the signals of another sensor are subsequently collected, till to all sensors.

Since the transistor-transistor logic (TTL) scheme operates without the transmission stability over a long distance, chip MAX232 is used to convert the signals of TTL into the ones of RS232, and then the receiver converts the signals of RS232 to the ones of TTL in our developed platform. At end, the signals are resolved and then converted to the pulse width modulation (PWM) of target machine by the

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microcontroller in the receiver, and subsequently the controlling on Robot hand is realized.

Figure 7a shows the control on the index finger of Robot hand by a SCF strain sensor, and the releasing and stretching states are corresponding to the straightening and bending of index finger of Robot hand, respectively (Movie S1). Figure 7b presents that the fingers of Robot hand can be completely controlled by human hand, except the little finger, because the volunteer could not bend it solely (Movie S2). Figure 7c reveals the relevant sign language of Robot hand controlled by human hand with the sensing glove, in which Robot hand perfectly “imitates” the human hand to sign Rock, Paper and Scissors (Movie S3). Furthermore, the continuous movement of Robot hand controlled by human hand with the sensing glove can be also realized. For example, Robot hand can successfully play piano with the tune of “Do Re Mi” (The Sound of Music) as indicated in Figure 7d, which are continuously controlled in real-time by human hand with as-prepared sensing glove. And the whole playing is recorded in Movie S4. Flexible and wearable sensing devices have attracted worldwide great interest due to its amazing visual impact reflected from the latest applications in robotics, human−machine interaction, sports monitoring, and artificial insemination. Some representative works have been reported in the flexible sensing field, such as piezoresistive, capacitive, piezoelectric etc., in particular, the piezoresistive sensors

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have drawn considerable attention because of the facile fabrication and simple mechanism. As shown in Table 1, tabulating our developed SCF strain sensors performance indices in comparison with most recent fiber-based strain sensors works reported in typical literature pertaining to the key factors (i.e., materials, structure, substrate, composites matrix, fabrication procedure, demonstrative gauge factor along with working mechanism.28,31,35-43 From the fabrication process perspective, the procedure developed in our work is more simple and effective than other works summarized in Table 1. Importantly, the key parameters in strain sensors, i.e., gauge factor, derived from our as-prepared SCF strain sensor which is up to 20 with a strain at 30%, hits the first tier performance record of reported strain sensors to date. Moreover, this work explores the deep applications of SCF in the controlling of robot systematically, which is a significant advance in this field. Conclusion In summary, we have developed a simple and scalable preparation strategy for graphite nanoplatelets (GNPs) based sheath−core fiber (SCF) strain sensors by a roll-to-roll process. As-prepared SCF strain sensors with great sensitivity and stability perform well due to the crack and elastic effects and their unique microstructure, with the gauge factor up to 20 at the strain of 30%. More than 1000 cycles show the excellent repeatability and outstanding performance of monitoring joint movement. Moreover, a microcontroller unit (MCU) system has been integrated to allow the SCF strain sensor to control the robot hand, including finger bending and piano playing. It 15



is demonstrated that the as-prepared SCF strain sensor has great potential applications in wearable electronics, to monitor the human being or robot movement such as bending and athletic sports. Moreover, the developed preparation process of the SCF strain sensor is simple and scalable with the potential of large-scale industrial production. We believe that our work creates a novel route to multifunctional strain sensors, providing more perspective deep applications in flexible electronics and smart controlling devices.

Experimental Section Preparation and assembly of graphite nanoplatelet and polyurethane solution, sheath−core fiber (SCF) strain sensor and sensing glove. Expanded graphite (EG) was produced by using the raw material of natural flake graphite with the size of 180 μm. And graphite intercalation compounds (GICs) were prepared by using Acetic acid, H2SO4 and K2MnO4 as intercalator and oxidizer. Graphite nanoplatelets were then synthesized by an exfoliation method with the assistant of surfactant. After drying, GNPs and polyurethane were mixed together with a weight ratio of 10:2. The diameter of commercial rubber latex thread is about 0.5 mm, and then the SCF strain sensor was obtained by dip coating method and the roll-to-roll process. A hand-like Polyethylene terephthalate (PET) was shaped to support five SCF strain sensors, named sensing glove. At last, the designed chip circuit and board were fabricated and integrated sensing glove and Robot hand with connected cables.

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Spectroscopic and Microscopic Characterizations. Samples were characterized by SEM (Zeiss, Merlin Compact), Raman (inVia ， Renishaw), XRD (DX-2700, Dandong Haoyuan Instrument CO., LTD) and FT-IR(Thermo Electron Nicolet 380), respectively. Digital multimeter (ROGOL, DM3068) was used to measure the resistance of samples.

Supporting Information Supplementary movies include that the index finger of Robot hand controlled by the stretching and releasing of SCF strain sensor, Robot hand sign language controlled by human hand in real-time, Robot hand “imitates” human hand to sign Rock, Paper and Scissors, and the continuous piano playing by Robot hand. Movie S1-Single finger controlled by one fiber strain sensor; Movie S2-Robot hand controlled by human hand; Movie S3-Rock-paper-scissors of Robot hand; Movie S4-Piano playing by Robot hand.

Author information Corresponding Authors *E-mail: [email protected]* *E-mail: [email protected]* *E-mail: [email protected]* and [email protected]* All authors conceived the idea and contributed the design of the project. J. Wu, H. Wang and G. Z. Xing prepared and characterized the samples. H. Wang, H. Liu and 17



G. Z. Xing provided constructive instructions during data analysis. All authors discussed the results and contributed to the manuscript writing. Notes The authors declare no competing financial interest. Acknowledgements The authors gave thanks to NSFC (Grants 51302051, 51502060, 51302050, 51621091, 51372052, 51772060), the Defense Industrial Technology Development Program (No.A0320110016), the Key Research and Development Program of Shandong Province (No.2018JMRH0107), Natural Science Foundation of Shandong (ZR2012EMQ 007) and Weihai supporting project. References (1)Kim, D.-H.; Ahn, J.-H.; Choi, W. M.; Kim, H.-S.; Kim, T.-H.; Song, J.; Huang, Y. Y.; Liu, Z.; Lu, C.; Rogers, J. A., Stretchable and Foldable Silicon Integrated Circuits. Science 2008, 320 (5875), 507-511. (2)Rogers, J. A.; Someya, T.; Huang, Y., Materials and Mechanics for Stretchable Electronics. Science 2010, 327 (5973), 1603-1607. (3)Kim, Y.; Zhu, J.; Yeom, B.; Di Prima, M.; Su, X.; Kim, J.-G.; Yoo, S. J.; Uher, C.; Kotov, N. A., Stretchable Nanoparticle Conductors with Self-Organized Conductive Pathways. Nature 2013, 500 (7460), 59-U77. (4)Garriga, R.; Jurewicz, I.; Seyedin, S.; Bardi, N.; Totti, S.; Matta-Domjan, B.; Velliou, E. G.; Alkhorayef, M. A.; Cebolla, V. L.; Razal, J. M.; Dalton, A. B.; Munoz, E., Multifunctional, Biocompatible and Ph-Responsive Carbon Nanotube- and Graphene Oxide/Tectomer Hybrid Composites and Coatings. Nanoscale 2017, 9 (23), 7791-7804.

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(14)Li, L.; Lin, H.; Qiao, S.; Huang, Y.-Z.; Li, J.-Y.; Michon, J.; Gu, T.; Alosno-Ramos, C.; Vivien, L.; Yadav, A.; Richardson, K.; Lu, N.; Hu, J., Monolithically Integrated Stretchable Photonics. Light-Sci Appl 2018, 7. (15)Yin, D.; Jiang, N.-R.; Liu, Y.-F.; Zhang, X.-L.; Li, A.-W.; Feng, J.; Sun, H.-B., Mechanically Robust Stretchable Organic Optoelectronic Devices Built Using a Simple and Universal Stencil-Pattern Transferring Technology. Light-Sci Appl 2018, 7. (16)Lee, S.; Inoue, Y.; Kim, D.; Reuveny, A.; Kuribara, K.; Yokota, T.; Reeder, J.; Sekino, M.; Sekitani, T.; Abe, Y.; Someya, T., A Strain-Absorbing Design for Tissue-Machine Interfaces Using a Tunable Adhesive Gel. Nat Commun 2014, 5. (17)Kim, K. K.; Hong, S.; Cho, H. M.; Lee, J.; Suh, Y. D.; Ham, J.; Ko, S. H., Highly Sensitive and Stretchable Multidimensional Strain Sensor with Prestrained Anisotropic Metal Nanowire Percolation Networks. Nano Lett 2015, 15 (8), 5240-5247. (18)Park, J.; You, I.; Shin, S.; Jeong, U., Material Approaches to Stretchable Strain Sensors. Chemphyschem 2015, 16 (6), 1155-1163. (19)Amjadi, M.; Kyung, K.-U.; Park, I.; Sitti, M., Stretchable, Skin-Mountable, and Wearable Strain Sensors and Their Potential Applications: A Review. Adv Funct Mater 2016, 26 (11), 1678-1698. (20)Chen, X., Making Electrodes Stretchable. Small Methods 2017, 1 (4). (21)Yu, J.; Lu, W.; Smith, J. P.; Booksh, K. S.; Meng, L.; Huang, Y.; Li, Q.; Byun, J.-H.; Oh, Y.; Yan, Y.; Chou, T.-W., A High Performance Stretchable Asymmetric Fiber-Shaped Supercapacitor with a Core-Sheath Helical Structure. Adv Energy Mater 2017, 7 (3). (22)Ajovalasit, A.; Zuccarello, B., Local Reinforcement Effect of a Strain Gauge Installation on Low Modulus Materials. J Strain Anal Eng 2005, 40 (7), 643-653.

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(23)Barlian, A. A.; Park, W.-T.; Mallon, J. R., Jr.; Rastegar, A. J.; Pruitt, B. L., Review: Semiconductor Piezoresistance for Microsystems. P Ieee 2009, 97 (3), 513-552. (24)Zhu, S.; So, J.-H.; Mays, R.; Desai, S.; Barnes, W. R.; Pourdeyhimi, B.; Dickey, M. D., Ultrastretchable Fibers with Metallic Conductivity Using a Liquid Metal Alloy Core. Adv Funct Mater 2013, 23 (18), 2308-2314. (25)Frutiger, A.; Muth, J. T.; Vogt, D. M.; Menguec, Y.; Campo, A.; Valentine, A. D.; Walsh, C. J.; Lewis, J. A., Capacitive Soft Strain Sensors Via Multicore-Shell Fiber Printing. Adv Mater 2015, 27 (15), 2440-2446. (26)Liu, Z. F.; Fang, S.; Moura, F. A.; Ding, J. N.; Jiang, N.; Di, J.; Zhang, M.; Lepro, X.; Galvao, D. S.; Haines, C. S.; Yuan, N. Y.; Yin, S. G.; Lee, D. W.; Wang, R.; Wang, H. Y.; Lv, W.; Dong, C.; Zhang, R. C.; Chen, M. J.; Yin, Q.; Chong, Y. T.; Zhang, R.; Wang, X.; Lima, M. D.; Ovalle-Robles, R.; Qian, D.; Lu, H.; Baughman, R. H., Hierarchically Buckled Sheath-Core Fibers for Superelastic Electronics, Sensors, and Muscles. Science 2015, 349 (6246), 400-404. (27)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 (4), 722-728. (28)Wang, H.; Liu, Z.; Ding, J.; Lepro, X.; Fang, S.; Jiang, N.; Yuan, N.; Wang, R.; Yin, Q.; Lv, W.; Liu, Z.; Zhang, M.; Ovalle-Robles, R.; Inoue, K.; Yin, S.; Baughman, R. H., Downsized Sheath-Core Conducting Fibers for Weavable Superelastic Wires, Biosensors, Supercapacitors, and Strain Sensors. Adv Mater 2016, 28 (25), 4998-5007. (29)Amjadi, M.; Turan, M.; Clementson, C. P.; Sitti, M., Parallel Microcracks-Based Ultrasensitive and Highly Stretchable Strain Sensors. Acs Appl Mater Inter 2016, 8 (8), 5618-5626. (30)Wang, S.; Xiao, P.; Liang, Y.; Zhang, J.; Huang, Y.; Wu, S.; Kuo, S.-W.; Chen, T., Network Cracks-Based Wearable Strain Sensors for Subtle and Large Strain Detection of Human Motions. J Mater Chem C 2018, 6 (19), 5140-5147.

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(31)Li,

X.;

Hua,

T.;

Xu,

B.,

Electromechanical

Properties

Page 22 of 33

of

a

Yarn

Strain

Sensor

with

Graphene-Sheath/Polyurethane-Core. Carbon 2017, 118, 686-698. (32)Patole, A. S.; Patole, S. P.; Jung, S.-Y.; Yoo, J.-B.; An, J.-H.; Kim, T.-H., Self Assembled Graphene/Carbon Nanotube/Polystyrene Hybrid Nanocomposite by in Situ Microemulsion Polymerization. Eur Polym J 2012, 48 (2), 252-259. (33)Wang, X.; Hu, Y.; Song, L.; Yang, H.; Xing, W.; Lu, H., In Situ Polymerization of Graphene Nanosheets and Polyurethane with Enhanced Mechanical and Thermal Properties. Journal of Materials Chemistry 2011, 21 (12), 4222-4227. (34)Ye, L.; Meng, X.-Y.; Ji, X.; Li, Z.-M.; Tang, J.-H., Synthesis and Characterization of Expandable Graphite-Poly(Methyl Methacrylate) Composite Particles and Their Application to Flame Retardation of Rigid Polyurethane Foams. Polym Degrad Stabil 2009, 94 (6), 971-979. (35)Pang, C.; Lee, G.-Y.; Kim, T.-i.; Kim, S. M.; Kim, H. N.; Ahn, S.-H.; Suh, K.-Y., A Flexible and Highly Sensitive Strain-Gauge Sensor Using Reversible Interlocking of Nanofibres. Nat Mater 2012, 11 (9), 795-801. (36)Cheng, Y.; Wang, R.; Sun, J.; Gao, L., A Stretchable and Highly Sensitive Graphene-Based Fiber for Sensing Tensile Strain, Bending, and Torsion. Adv Mater 2015, 27 (45), 7365-+. (37)Zhang, M.; Wang, C.; Wang, Q.; Jian, M.; Zhang, Y., Sheath-Core Graphite/Silk Fiber Made by Dry-Meyer-Rod-Coating for Wearable Strain Sensors. Acs Appl Mater Inter 2016, 8 (32), 20894-20899. (38)Liu, Z.; Qi, D.; Hu, G.; Wang, H.; Jiang, Y.; Chen, G.; Luo, Y.; Loh, X. J.; Liedberg, B.; Chen, X., Surface Strain Redistribution on Structured Microfibers to Enhance Sensitivity of Fiber-Shaped Stretchable Strain Sensors. Advanced materials (Deerfield Beach, Fla.) 2017.

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(39)Wu, Y.-h.; Zhen, R.-m.; Liu, H.-z.; Liu, S.-q.; Deng, Z.-f.; Wang, P.-p.; Chen, S.; Liu, L., Liquid Metal Fiber Composed of a Tubular Channel as a High-Performance Strain Sensor. J Mater Chem C 2017, 5 (47), 12483-12491. (40)Zang, S.; Wang, Q.; Mi, Q.; Zhang, J.; Ren, X., A Facile, Precise Radial Artery Pulse Sensor Based on Stretchable Graphene-Coated Fiber. Sensor Actuat A-Phys 2017, 267, 532-537. (41)Chen, S.; Liu, H.; Liu, S.; Wang, P.; Zeng, S.; Sun, L.; Liu, L., Transparent and Waterproof Ionic Liquid-Based Fibers for Highly Durable Multifunctional Sensors and Strain-Insensitive Stretchable Conductors. Acs Appl Mater Inter 2018. (42)Li, L.; Shi, P.; Hua, L.; An, J.; Gong, Y.; Chen, R.; Yu, C.; Hua, W.; Xiu, F.; Zhou, J.; Gao, G.; Jin, Z.; Sun, G.; Huang, W., Design of a Wearable and Shape-Memory Fibriform Sensor for the Detection of Multimodal Deformation. Nanoscale 2018, 10 (1), 118-123. (43)Wang, Y.; Hao, J.; Huang, Z.; Zheng, G.; Dai, K.; Liu, C.; Shen, C., Flexible Electrically Resistive-Type Strain Sensors Based on Reduced Graphene Oxide-Decorated Electrospun Polymer Fibrous Mats for Human Motion Monitoring. Carbon 2018, 126, 360-371. \

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ACS Applied Nano Materials 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

Page 24 of 33

Table 1. Summary of key performance indexes from latest representative fiber-like strain sensors reports including our work.

STRUCTURE

FUNCTION MATERIALS

SUBSTRATE/ MATRIX

SENSING MECHANISM

GAUGE FACTOR

FABRICATION PROCESS

DEEP APPLICATION

YR

REF

Sheath−core

GNP

Polyurethane/ rubber

Piezoresistive

20

Dip-coating

Yes

2018

Present Work

Sheath–core

CNT

Rubber

Capacitive

14-65

Melt-draw/ wrap CNT/stretch release,

None

2016

28

Coaxial configuration

CNT

Thermoplastic polyurethane/polypyr role

Piezoresistive

12

CVD/electrochemically deposit

None

2018

42

Graphene/vinyl alcohol

Polyurethane yarn

Piezoresistive

86.86

Dip-coating

None

2017

31

Ultrafine graphite flakes

Silk

Piezoresistive

14.5

Dry-Meyer-rod-coating

None

2016

37

rGO

Thermoplastic polyurethane

Piezoresistive

11-79

Electrospinning and ultrasonication

None

2017

43

Graphene

Polyurethane and polyester

Piezoresistive

3.7-10

Dip-coating

None

2015

36

Pt-coated polyurethane acrylate

Polydimethylsiloxane

Piezoresistive

0.75-11.45

Ultraviolet-curable/ oxygen-plasma-treating

None

2012

35

Sheath−core

rGO

Double covered yarn/ polyurethane/ polyester

Piezoresistive

Sheath-Core Fiber Strain Sensors Driven by In-Situ Crack and Elastic

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