Self-Powered Pressure- and Vibration-Sensitive Tactile Sensors for

3 hours ago - School of Chemical Engineering, Sungkyunkwan University , Suwon , Gyeonggi-do 16419 , Republic of Korea. § Department of Smart Textile ...
0 downloads 0 Views 7MB Size
Download PDF
Letter Cite This: Nano Lett. XXXX, XXX, XXX−XXX

pubs.acs.org/NanoLett

Self-Powered Pressure- and Vibration-Sensitive Tactile Sensors for Learning Technique-Based Neural Finger Skin Sungwoo Chun,†,‡ Wonkyeong Son,§ Haeyeon Kim,∥ Sang Kyoo Lim,§ Changhyun Pang,†,‡ and Changsoon Choi*,§ †

Nano Lett. Downloaded from pubs.acs.org by KEAN UNIV on 04/25/19. For personal use only.

Department of SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea ‡ School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea § Department of Smart Textile Convergence Research, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Republic of Korea ∥ Advanced Process and Materials R&D Group, Korea Institute of Industrial Technology, Incheon 21999, Republic of Korea S Supporting Information *

ABSTRACT: Finger skin electronics are essential for realizing humanoid soft robots and/or medical applications that are very similar to human appendages. A selective sensitivity to pressure and vibration that are indispensable for tactile sensing is highly desirable for mimicking sensory mechanoreceptors in skin. Additionally, for a human−machine interaction, output signals of a skin sensor should be highly correlated to human neural spike signals. As a demonstration of fully mimicking the skin of a human finger, we propose a self-powered flexible neural tactile sensor (NTS) that mimics all the functions of human finger skin and that is selectively and sensitively activated by either pressure or vibration stimuli with laminated independent sensor elements. A sensor array of ultrahigh-density pressure (20 × 20 pixels on 4 cm2) of interlocked percolative graphene films is fabricated to detect pressure and its distribution by mimicking slow adaptive (SA) mechanoreceptors in human skin. A triboelectric nanogenerator (TENG) was laminated on the sensor array to detect high-frequency vibrations like fast adaptive (FA) mechanoreceptors, as well as produce electric power by itself. Importantly, each output signal for the SA- and FAmimicking sensors was very similar to real neural spike signals produced by SA and FA mechanoreceptors in human skin, thus making it easy to convert the sensor signals into neural signals that can be perceived by humans. By introducing microline patterns on the top surface of the NTS to mimic structural and functional properties of a human fingerprint, the integrated NTS device was capable of classifying 12 fabrics possessing complex patterns with 99.1% classification accuracy. KEYWORDS: Self-power, mechanoreceptors, skin electronics, sensors, triboelectric nanogenerator, finger skin uman finger skin has the most effective sensory system and senses tactile modalities (e.g., touch, pressure, vibration, warm and cold, pain) with spatiotemporal perception of externally applied stimuli through cutaneous sensory receptors. Then the spatiotemporal tactile signals transmitted to the somatosensory cortex through neural afferents are encoded as voltage spikes of action potential to be transmitted to the brain.1 The brain then comprehensively recognizes the types and intensity of the tactile stimuli.2 Mimicry of the finger skin functions and its sensory system

H

© XXXX American Chemical Society

inspires the development of efficient and adaptive tactile sensing systems. Some attempts have been made to achieve sensory functions of human skin by developing either a single device sensitive to pressure, strain, vibration, and tactile sensors or their multistacked architectures.3−13 Received: March 4, 2019 Revised: April 16, 2019

A

DOI: 10.1021/acs.nanolett.9b00922 Nano Lett. XXXX, XXX, XXX−XXX

Letter

Nano Letters

Figure 1. Flexible electronic mechanoreceptors mimicking human finger skin. (a) Human finger skin and fingerprint structure of epidermis and mechanoreceptors of dermis. (b) Schematic illustration of an NTS device. The NTS device is composed of a bottom panel with a SA-mimicking sensor, a top panel with an FA-mimicking sensor, and an artificial fingerprint structure with microlines. (c) Correlation between neural spikes of human skin and electrical output signals of an NTS device for slow and fast adaptation in response to physical stimuli, respectively. (d) Interlocked percolative graphene sensor array. (d(i)) Schematic of individual interlocked top and bottom GNP films. (d(ii)) SEM image showing graphene morphology of the interlocked region. With natural force, top and bottom graphene films are slightly interlocking with distance. (d(iii)) Optical image of the bottom graphene film array and top-view GNP film. (e) Photograph of completed SA-mimicking sensor array as bottom panel.

To realize the sense of touch by natural skin, it is important to mimic the sensing properties of sensory receptors in human skin, wherein the receptors encode tactile information as a time interval between voltage spikes of action potentials. Specifically, there are four types of mechanoreceptors that perceive innocuous mechanical stimuli (pressure and vibration) by the rate of adaptation, respectively: two types of slow adaptive receptors (SA-I and SA-II) and two of fast adaptive receptors (FA-I and FA-II).2 The SA receptors sensitively respond to static pressure to detect responses to sustained physical stimuli, perceiving high-resolution pressure or pressure distribution information and skin stretching. In contrast, the FA receptors preferentially respond to dynamic pressures or vibrations; thus, it is crucial to recognize their texture discrimination. Efforts were made to achieve artificial mechanoreceptor-mimicking

tactile sensors by emulating the material, structural, and functional properties of human skin.1,8−10,13−18 However, the reported sensors are mostly focused on sensitive pressure sensing. Meanwhile, the capability to detect high-frequency vibrations has been limited because of the inherently slow responses of the sensors as mostly polymeric materials5,8 have been adopted to achieve high sensitivity to pressure. Pressure sensing only is not sufficient for achieving the function of human tactile perception because texture recognition in human perception is a complex process including the sensitive detection of both pressure and vibration. Human beings mainly perceive surface texture by detecting an interacting vibration induced by the roughness of the object being touched on the skin through FA mechanoreceptors (Pacinian and Meissner) in the skin that are sensitively B

DOI: 10.1021/acs.nanolett.9b00922 Nano Lett. XXXX, XXX, XXX−XXX

Letter

Nano Letters

Figure 2. Pressure- and vibration-sensing performances. (a) Pressure sensitivity with statistically applied pressures in the range of 100 to 100 000 Pa. The pressure sensitivity (Sp = (ΔI/I0)/ΔP) was estimated as Sp1 = 1.63 kPa−1 from 0.1 to 6 kPa and as Sp2 = 0.04 kPa−1 from 6 to 100 kPa. (b) The operating mechanism of the SA-mimicking sensor in the pressure range of 0 to 100 kPa. (c) Minimum pressure detection limit with application of a weight of 4.8 mN on an area of 1 cm2. (d) Response time (140°) (inset images of Figure 1d(iii)). Figure 1e shows the completed flexible SA-mimicking sensor array fabricated by a simple solution process of GNP suspension. The TENG sensor was fabricated as the top panel by using PEN and polytetrafluoroethylene (PTFE) surfaces for friction with an acryl bumper (∼1 mm height) to isolate the surfaces without applied force and was laminated on the top surface of the graphene sensor array. The piezoresistive response of the SA-mimicking graphene force sensor array was first investigated for vertical pressures. Figure 2a shows the response of the sensor under a statically applied pressure in the range of 100−100 000 Pa, which corresponds to the general pressure detection range of human tactile perception.24,25 The applied pressure induces an enhancement in the interlocking contact between the upper and lower GNP sheet films, resulting in an increase in the current (I) from the initial current (I0). The pressure sensitivity (Sp) is defined as Sp = (ΔI/I0)/ΔP, where ΔI is the change in current (I − I0) in response to the change in the applied vertical pressure (ΔP). The sensitivity curve does not change linearly with applied pressure (Figure 2a). In the pressure range below 6 kPa, the sensitivity (Sp1) was estimated to be 1.63 kPa−1 through a linear fit. When the vertical pressure increased from 6 to 100 kPa, the sensitivity (Sp2) rapidly decreased to 0.04 kPa−1, a much smaller difference than that observed in the lower pressure range. The change in sensitivity with applied pressures can be attributed to different operating mechanisms of the interlocked percolative GNP film sensor (Figure 2b). In the low-pressure range, the upper and the lower GNP sheet films undergo a mechanical and electrical contact stage with initial interlocking contact, leading to a rapid decrease in resistance. Once the electrical contact is stabilized at a certain pressure close to 6 kPa (thus forming a single GNP film with the combination of the upper and lower films), the value of Sp2 increases slowly because of the increased electrical conductivity of the GNP percolation networks. The sensor exhibits a pressure detection limit (∼48 Pa), which was applied by a weight (4.8 mN) on an area of 1 cm2 (Figure 2c). This detection limit is attributed to the force needed to achieve the initial interlocking contact between the upper and lower films. The response time was confirmed through the change in the current response of the interlocked sensor accompanying a releasing operation where the measurement interval was 1 ms (Figure 2d). The response time was estimated to be less than 3 ms for restoration, indicating that the sensor can detect vibrating pressures up to ∼330 Hz with negligible signal loss. This is comparable to the frequency range (3 dB) in the power spectrum (Figure 3d). Specifically, the SAmimicking sensor showed a reduction in the SNR of signals with decreasing roughness wavelength under a fine texture level (1 mm), whereas the FA-mimicking sensor can detect fine textures without insignificant degradation in the SNR for signals over 8 dB. The frequency response of SNR versus scan speed was investigated (Figure 3e). The frequency response of the SA-mimicking sensor strongly depended on scan speed because at a faster speed, the sensor experiences incomplete restoration of the electrical response. In contrast, the FA-mimicking sensor maintained high frequency responses in the range of 1 to 100 mm/s, which corresponds to the general range of rubbing speed for human tactile perception. Such sensing capability contributed to the vibration-sensitive characteristic of the FA-mimicking sensor (TENG), wherein the sensor outputs electrical voltage/current only at the initial and final contacts of a mechanical stimulus. The SA and FA sensors with a higher aspect ratio (AR) of microlines exhibited higher output signals in SNR, indicating that a higher AR sensor is more effective for texture distinction (SI Figure S8). Unlike periodic roughness patterns, a new tool is required for quantifying the surface texture of intricately textured materials, because these mostly contain irregular patterns including roughness that is aperiodic in amplitude and spatial intervals. To analyze and classify the waveform of the interacting vibrations induced by the physical interaction between our NTS and complex texture patterns (Figure 3f), we used a deep learning technique (MatLab 2017b version, Neural Network Pattern Recognition Toolbox) that has been widely adopted for the recognition of various patterns in fields including speech and visual image recognition.32,33 Twelve different fabrics were tested to classify the texture patterns. The SEM images of the testing fabrics with different surface textures show they are delicate enough to be distinguishable (Figure 3g). With consecutive back-and-forth scanning with gentle contact of the testing fabrics on the sensor (∼10 kPa), electrical output signals were collected during 1 h with a

some pressure dependence but it was not large when compared with the SA-mimicking graphene sensor. Note that the TENG was selectively sensitive not to pressure but to vibration inputs, mimicking the FA mechanoreceptors. The frequency responses of the TENG sensor present a specific response curve which is caused by the resonance frequency (∼20 Hz) of the TENG (SI Figure S6).29 The SI Figure S7 presents the voltage versus time curve obtained by connecting the TENG to a power charging capacitor with a rectifying bride circuit. A pressure of 51 kPa was applied on the TENG with an operating frequency of 4 Hz to charge the capacitor during 50 s. The result indicates excellent charging performance with a low charging leakage under repeated loading−unloading forces. In addition, the electric power delivered from the TENG during the press-andrelease operations was sufficient to directly turn on 100 lightemitting diodes (LEDs) brightly without charging capacitors (Figure 2h). Using the integrated NTS device, we finally demonstrated surface texture recognition. Human beings perceive surface texture using the SA1 receptor (Merkel disk) and FA receptors (Meissner and Pacinian corpuscles). Specifically, touch by fingertip generates the most sensitive tactile feeling because mechanoreceptors are more densely distributed in this area (∼240 per cm2) than in the palm area (∼60 per cm2 for adults).30 It has been proven that SA1 afferents, which densely innervate the fingertip skin, respond strongly to coarse textures by spatial patterns of activation across the population, but respond only weakly or not at all to finer patterns, whereas FA corpuscles discern fine textures through features mediated by transduction and processing of vibrations produced on the skin during scanning.19 This fact indicates that a complex combination of output signals from SA and FA sensors provides texture-dependent characteristic clues: coarse textural features by spatial encoding arising from geometrical properties with induced pressure and fine textural features by vibrotactile encoding with interacting vibrations. The NTS device with periodic microlines (300 μm width and intervals, 300−500 μm height) mimics fingerprint structure (FPS) scans on single-ridge texture (100 μm width and height) to detect interacting pressure or vibration, as shown in the schematic in Figure 3a. The pressure and vibration are directly transferred to the SA and FA sensors underneath the individual FPS by a frictional shear force induced by rubbing contact in the in-plane direction between the single ridge and the microlines on the sensor with a slipping motion.31 As demonstrated previously (Figure 2 and Figure 3), our NTS device is designed so that the SA and FA sensors respond selectively and sensitively to pressure and vibration, respectively. Moreover, the FA sensors can provide the electrical power in the sensor system. The fingerprint at the fingertips effectively generates and amplifies the pressure and vibration induced by the interacting surface during physical interaction with the touched object. The operation of the sensors combined with FPS for recognition of a surface texture with rubbing motion is analogous to human tactile perception by touching and rubbing the surface of an object (Figure 3b,c). The rubbing of a textured object having a roughness pattern (L = width + pitch) with a rubbing velocity (v) on a periodic FPS pattern on the sensors generates time-dependent electrical output signals by the interacting pressure and vibration between the FPS and the surface roughness. There is no electrical crosstalk between the SA and FA sensors because they are connected by dielectric film. The SA-mimicking F

DOI: 10.1021/acs.nanolett.9b00922 Nano Lett. XXXX, XXX, XXX−XXX

Letter

Nano Letters Author Contributions

sampling frequency of 1 kHz by rubbing the fabrics on the NTS device with a scanning velocity of 24 mm/s. The textures of fabrics were classified in three different cases: (1) using only SA sensor signals, (2) using only FA sensor signals, and (3) using combined SA and FA signals (Figure 3h). The results indicated that using only the electrical texture information obtained by our device, our NTS sensor generally classified the fabrics with an accuracy of over 92%. This was enabled by rich amplitude information on roughness with pressure responsivity from the SA sensor and a wealth of spatial interval information on roughness with vibration responsivity from the FA sensor. This implies that our sensor elements are sensitive enough to recognize textures including fine and complex patterns, because of the ability to sensitively detect fine pressure or vibration differences. With a combination of SA and FA sensor signals, our NTS device successfully classified the fabrics with 99.1% classification accuracy, quite comparable to human tactile perception ability. In conclusion, we report a self-powered flexible neural tactile sensor (NTS) mimicking human finger skin obtained by complementing a high-density (100 pixels per cm2) pressure sensor array of interlocked percolative graphene films and a self-powered triboelectric nanogenerator with a thin-film structure. Our NTS device selectively enables sensitive detection of pressure and vibration like real human skin does. All signal outputs respectively produced by the SA and FA mechanoreceptors are very similar to human neural spike signals. Using this characteristic, we successfully demonstrated an excellent texture recognition capability of 12 fine and complex fabrics. Our approach has enabled new possibilities for skin electronics mimicking human finger skin for humanoid soft robots, artificial prosthetics, human−machine interactions, and medical applications.

■

S.C. and C.C. conceived this work and developed the design of the NTS device. S.C. and W.S. fabricated the NTS devices and S.C. and H.K. measured their properties. S.K.L. and C.P. analyzed the experimental results. S.C. and C.C. wrote the paper. All authors reviewed the manuscript and provided feedback. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS This work was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (NRF2017R1A6A3A04004987 and 2018R1A6A3A01011866). This work was also supported by the DGIST R&D Program (18NT-02) of the Ministry of Science, ICT and Future Planning.

■

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.nanolett.9b00922. Fabrication of NTS devices, deep learning for tactile classification, preparation of GNP suspension, electrical response measurements of an interlocked percolative graphene sensor array and a TENG device, measurements of interacting pressure or vibration by rubbing motion, contact angle measurements, thickness of the GNP film, sensitive and reliable piezoresistive responses to dynamic pressures, consistent piezoresistive responses, electrical responses as a function of bending curvature, output voltages with different pressure inputs, voltage versus time curve, SNR in frequency responses depending on the aspect ratio of microlines for SA- and FA-mimicking sensors (PDF)

■

REFERENCES

(1) Chortos, A.; Liu, J.; Bao, Z. Nat. Mater. 2016, 15, 937−950. (2) Abraira, V. E.; Ginty, D. D. Neuron 2013, 79, 618−639. (3) Someya, T.; Sekitani, T.; Iba, S.; Kato, Y.; Kawaguchi, H.; Sakurai, T. Proc. Natl. Acad. Sci. U. S. A. 2004, 101, 9966−9970. (4) Yokota, T.; Zalar, P.; Kaltenbrunner, M.; Jinno, H.; Matsuhisa, N.; Kitanosako, H.; Tachibana, Y.; Yukita, W.; Koizumi, M.; Someya, T. Sci. Adv. 2016, 2, e1501856. (5) Gong, S.; Schwalb, W.; Wang, Y.; Chen, Y.; Tang, Y.; Si, J.; Shirinzadeh, B.; Cheng, W. Nat. Commun. 2014, 5, 3132. (6) Jason, N. N.; Wang, S. J.; Bhanushali, S.; Cheng, W. Nanoscale 2016, 8, 16596−16605. (7) Bao, Z.; Chen, X. Adv. Mater. 2016, 28, 4177−4179. (8) Zhu, B.; Niu, Z.; Wang, H.; Leow, W. R.; Wang, H.; Li, Y.; Zheng, L.; Wei, J.; Huo, F.; Chen, X. Small 2014, 10, 3625−3631. (9) Jin, M. L.; Park, S.; Lee, Y.; Lee, J. H.; Chung, J.; Kim, J. S.; Kim, J.-S.; Kim, S. Y.; Jee, E.; Kim, D. W.; Chung, J. W.; Lee, S. G.; Choi, D.; Jung, H.-T.; Kim, D. H. Adv. Mater. 2017, 29, 1605973. (10) Chun, K.-Y.; Son, Y. J.; Jeon, E.-S.; Lee, S.; Han, C.-S. Adv. Mater. 2018, 30, 1706299. (11) Hua, Q.; Sun, J.; Liu, H.; Bao, R.; Yu, R.; Zhai, J.; Pan, C.; Wang, Z. L. Nat. Commun. 2018, 9, 244. (12) Lee, Y.; Park, J.; Cho, S.; Shin, Y.-E.; Lee, H.; Kim, J.; Myoung, J.; Cho, S.; Kang, S.; Baig, C.; Ko, H. ACS Nano 2018, 12, 4045− 4054. (13) Park, J.; Kim, M.; Lee, Y.; Lee, H. S.; Ko, H. Sci. Adv. 2015, 1, e1500661. (14) Tee, B. C.-K.; Chortos, A.; Berndt, A.; Nguyen, A. K.; Tom, A.; McGuire, A.; Lin, Z. C.; Tien, K.; Bae, W.-G.; Wang, H.; Mei, P.; Chou, H.-H.; Cui, B.; Deisseroth, K.; Ng, T. N.; Bao, Z. Science 2015, 350, 313−316. (15) Darabi, M. A.; Khosrozadeh, A.; Mbeleck, R.; Liu, Y.; Chang, Q.; Jiang, J.; Cai, J.; Wang, Q.; Luo, G.; Xing, M. Adv. Mater. 2017, 29, 1700533. (16) Zhao, S.; Zhu, R. Adv. Mater. 2017, 29, 1606151. (17) Ho, D. H.; Sun, Q.; Kim, S. Y.; Han, J. T.; Kim, D. H.; Cho, J. H. Adv. Mater. 2016, 28, 2601−2608. (18) Lei, Z.; Wang, Q.; Wu, P. Mater. Horiz. 2017, 4, 694−700. (19) Weber, A. I.; Saal, H. P.; Lieber, J. D.; Cheng, J.-W.; Manfredi, L. R.; Dammann, J. F., III; Bensmaia, S. J. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, 17107−17112. (20) Liao, Z.; Liu, W.; Wu, Y.; Zhang, C.; Zhang, Y.; Wang, X.; Li, X. Nanoscale 2015, 7, 10801−10806. (21) Zhuang, Y.; Xu, Z.; Fu, X.; Li, F.; Li, J.; Liao, Z.; Liu, W. Semicond. Sci. Technol. 2017, 32, 074002. (22) Hu, H.; Han, Y.; Song, A.; Chen, S.; Wang, C.; Wang, Z. Sensors 2014, 14, 4899−4913. (23) Scheibert, J.; Leurent, S.; Prevost, A.; Debregeas, G. Science 2009, 323, 1503−1506.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Sang Kyoo Lim: 0000-0002-8971-8232 Changhyun Pang: 0000-0001-8339-7880 Changsoon Choi: 0000-0003-4456-4548 G

DOI: 10.1021/acs.nanolett.9b00922 Nano Lett. XXXX, XXX, XXX−XXX

Letter

Nano Letters (24) Mannsfeld, S. C. B.; Tee, B. C-K.; Stoltenberg, R. M.; Chen, C. V. H-H.; Barman, S.; Muir, B. V. O.; Sokolov, A. N.; Reese, C.; Bao, Z. Nat. Mater. 2010, 9, 859−864. (25) Chun, S.; Kim, Y.; Jung, H.; Park, W. Appl. Phys. Lett. 2014, 105, 041907. (26) Dahiya, R. S.; Metta, G.; Valle, M.; Sandini, G. IEEE Trans. Robot. 2010, 26, 1−20. (27) Zhu, G.; Lin, Z.-H.; Jing, Q.; Bai, P.; Pan, C.; Yang, Y.; Zhou, Y.; Wang, Z. L. Nano Lett. 2013, 13, 847−853. (28) Wang, Z. L.; Jiang, T.; Xu, L. Nano Energy 2017, 39, 9−23. (29) Chen, J.; Zhu, G.; Yang, W.; Jing, Q.; Bai, P.; Yang, Y.; Hou, T.H.; Wang, Z. L. Adv. Mater. 2013, 25, 6094−6099. (30) Johansson, R. S.; Vallbo, A. B. J. Physiol. 1979, 286, 283−300. (31) Chun, S.; Choi, Y.; Suh, D. I.; Bae, G. Y.; Hyun, S.; Park, W. Nanoscale 2017, 9, 10248−10255. (32) LeCun, Y.; Bengio, Y.; Hinton, G. Nature 2015, 521, 436−444. (33) Jordan, M. I.; Mitchell, T. M. Science 2015, 349, 255−260.

H

DOI: 10.1021/acs.nanolett.9b00922 Nano Lett. XXXX, XXX, XXX−XXX