Simultaneous Detection of Static and Dynamic

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May 8, 2017 - Rongqing Xu 1,2,*, Di Wang 1, Hongchao Zhang 3, Na Xie 1, Shan Lu 4 and Ke Qu 1,*. 1. College of Electronic Science and Engineering, ...
sensors Article

Simultaneous Detection of Static and Dynamic Signals by a Flexible Sensor Based on 3D Graphene Rongqing Xu 1,2, *, Di Wang 1 , Hongchao Zhang 3 , Na Xie 1 , Shan Lu 4 and Ke Qu 1, * 1 2 3 4

*

College of Electronic Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; [email protected] (D.W.); [email protected] (N.X.) National Laboratory of Solid-State Microstructures and Department of Materials Science and Engineering, Nanjing University, Nanjing 210093, China Department of Information Physics and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; [email protected] Shanghai Key Laboratory of Aerospace Intelligent Control Technology, Shanghai Aerospace Control Technology Institute, Shanghai 201109, China; [email protected] Correspondence: [email protected] (R.X.); [email protected] (K.Q.)

Academic Editors: Hyun-Joong Chung and Tae-il Kim Received: 19 March 2017; Accepted: 21 April 2017; Published: 8 May 2017

Abstract: A flexible acoustic pressure sensor was developed based on the change in electrical resistance of three-dimensional (3D) graphene change under the acoustic waves action. The sensor was constructed by 3D graphene foam (GF) wrapped in flexible polydimethylsiloxane (PDMS). Tuning forks and human physiological tests indicated that the acoustic pressure sensor can sensitively detect the deformation and the acoustic pressure in real time. The results are of significance to the development of graphene-based applications in the field of health monitoring, in vitro diagnostics, advanced therapies, and transient pressure detection. Keywords: acoustic pressure sensor; three-dimensional graphene foam; flexible; wearable

1. Introduction In recent years, flexible pressure sensors have been highly desirable in applications such as electronic skin, structural health monitoring, robot sensor, personal health monitoring, sport performance monitoring, and rehabilitation [1–4]. With the further development of robotics, biomechanics and medical measurement, the demand for flexible pressure sensor has become more urgent. Flexible pressure sensors are divided into two categories: conducting polymer sensors [5] and piezoelectric material sensors [6–8]. It is generally believed that conducting polymers as a stress sensor can only detect static stress, while piezoelectric sensors are only suitable for measuring dynamic behavior, such as vibration and acoustic waves. Currently, scientific breakthroughs are needed to simultaneously measure static and dynamic signals. Various flexible pressure sensors have been fabricated based on different nanomaterials, such as gold nanowire [9], ZnO nanowire [10], carbon nanotube (CNT) [4,11], and graphene [12]. Graphene in particular has sparked intensive interest for pressure sensors in the past few years owing to its superior mechanical and electrical properties [1,13,14]. Dong et al. reported highly sensitive electrochemical sensors for detection of [Fe(CN)6 ]3+ and dopamine with free-standing graphene/ZnO hybrid electrodes. Bae et al. reported a high-performance piezo-resistive pressure sensor device with a linear relationship between applied pressure and output, with a high sensitivity and a wide range of pressure by using two-dimensional (2D) monolayer graphene [15]. Recently, 3D graphene, such as graphene foams (GFs) [16], graphene sponges (GSs) [17], and graphene aerogels (GAs) [18], has attracted much more attention in a wide range of flexible sensors. Compared to monolayer graphene, the graphene sheets

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in GFs derived by template-directed chemical vapor deposition (CVD) are seamlessly interconnected to form a 3D flexible and free-standing graphene scaffolds. The unique interconnected network of GFs provides a great potential for use in composite materials for electrical applications. When the graphene Sensors 2017, 17, 1069 2 of 8 composited with insulating polymer, the composites show good flexibility, and can be bent, stretched, and twisted breaking.graphene The high qualityThe of the graphene sheets and theirofperfect connection in flexiblewithout and free-standing scaffolds. unique interconnected network GFs provides a great potential for use composite materials for electrical applications. When properties the graphene three dimensions provide theinmaterial with outstanding electrical and mechanical [19]. with insulating polymer, show good andsignal can beisbent, Incomposited our previous studies [20], onlythe thecomposites static bending and flexibility, tensile test reported. stretched, and twisted without mostly breaking.detect The high quality the graphene sheets owing and their The piezo-resistive stain sensors signal by of mechanical stimuli toperfect its sensing connection in three dimensions provide the material with outstanding electrical and mechanical mechanism based on the change in resistance due to mechanical deformation. Since the response time properties [19]. of the sensors can reach the second order for the large sample size and large deformation conditions, In our previous studies [20], only the static bending and tensile test signal is reported. The the sensors are suitable forsensors the static stressdetect measurement low frequency signal measurement, such as piezo-resistive stain mostly signal byormechanical stimuli owing to its sensing musclemechanism motions. based Noneonofthe thechange reported studies have claimed that this kind of sensor can measure in resistance due to mechanical deformation. Since the response timesignals of the of sensors reach the second order can for the large sample size andpotential large deformation dynamic high can frequency. This behavior have an impact on the application of conditions, the sensors are suitable for the static stress measurement or low frequency 3D graphene sensors. In this paper, our 3D GF sensors can detect the static signals from the signal mechanical measurement, such as musclesignals motions. None of by the vibrations. reported studies have claimed thisreported kind of that deformation and the dynamic coupled Recently, it hasthat been sensor can measure dynamic signals of high frequency. This behavior can have an impact on the suspended membrane configuration graphene sensors have orders of magnitude of higher sensitivity potential application of 3D graphene sensors. In this paper, our 3D GF sensors can detect the static compared to other sensors [21]. Since 3D graphene has seamless interconnection and a free-standing signals from the mechanical deformation and the dynamic signals coupled by vibrations. Recently, it scaffold, GFreported sensorsthat have high sensitivity. we demonstrate flexible acoustic has the been suspended membraneHere, configuration graphene asensors have orderspressure of sensors based onof3D graphene network. A to dynamic tuning forks signalhas was sensitively magnitude higher sensitivity compared other sensors [21]. Sinceacoustic 3D graphene seamless detected. Moreover, and the physiological human pulse and waves were captured to interconnection a free-standingsignals scaffold,ofthe GF sensors havesound high sensitivity. Here, we demonstrate a flexible acoustic pressure sensors based on 3D graphene network. A dynamic tuning indicate an excellent response under static conditions. Particularly, the muscle motions and vocal cord forksduring acousticspeech signal was sensitively the physiological of human pulse vibrations could both be detected. detected Moreover, by our sensor. We believesignals our work is of significance and sound waves were captured to indicate an excellent response under static conditions. to graphene-based applications. Particularly, the muscle motions and vocal cord vibrations during speech could both be detected by our sensor. believe our work is of significance to graphene-based applications. 2. Materials andWe Methods

2. Materials andsynthesized Methods The 3D GF was by chemical vapor deposition method on nickel foam as a template. Ni foam was used to catalyze the graphene growth [20]. First, the nickel cut a desired size. The 3D GF was synthesized by chemical vapor deposition method onfoam nickelwas foam as to a template. It was Ni sonicated hydrochloric acid (HCl) solution, acetone, and deionized the nickel foam wasinused to catalyze the graphene growth [20]. First, the nickel foam waswater, cut to aSecond, desired size. It wasplaced sonicated in hydrochloric acidand (HCl) solution, acetone, and deionized water, Second, hydrogen the nickel and foam was in the tube furnace pre-treated in mixed gases with 25 SCCM ◦ C forand foamargon was placed in theattube furnace pre-treated in mixed gasessurface with 25oxide SCCMlayer. hydrogen and 50 SCCM flow rates 1000 10 min to eliminate a thin Subsequently, 50 SCCM argon flow rates at 1000 °C for 10 min to eliminate a thin surface oxide layer. Subsequently, the carbon source of ethanol was introduced by a hydrogen and argon flow for 10 min to produce the carbon source of ethanol was introduced by a hydrogen and argon flow for 10 min to produce graphene. Finally, after cooling down the furnace naturally to room temperature under the protection graphene. Finally, after cooling down the furnace naturally to room temperature under the protection of an argon flow, the graphene/nickel foam was corroded in a 10% HCl solution [22]. After that, of an argon flow, the graphene/nickel foam was corroded in a 10% HCl solution [22]. After that, the the sample was threetimes times deionized water and dried at 60 ◦ C. sample wascleaned cleaned three byby deionized water and dried at 60 °C.

(a) Schematic illustration of overall fabrication process; (b) Schematic diagram of the FigureFigure 1. (a)1.Schematic illustration of overall fabrication process; (b) Schematic diagram of the experimental setup. experimental setup.

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Figure 1a illustrates the fabrication process of our 3D graphene foam based flexible acoustic pressure sensor. First, a suitable mold was prepared for design 3D GF foam sensor. Then,flexible the 3Dacoustic GF was Figure 1a illustrates the fabrication process of our 3D graphene based symmetrically bonded the silver plastic electrodes with two lead wires after being transferred inwas the pressure sensor. First, a suitable mold was prepared for design 3D GF sensor. Then, the 3D GF mold. Subsequently, a the small amount of electrodes PDMS solution (10:1lead volume and curing symmetrically bonded silver plastic with two wiresratio afterpre-polymer being transferred in the agent mixed) was poured into the mold. Finally, the sample was tailored to a desired size andcuring shape mold. Subsequently, a small amount of PDMS solution (10:1 volume ratio pre-polymer and to obtain a final 3D GF-based acoustic pressure sensor after the to PDMS wassize dried room agent mixed) was poured into the mold. Finally, the sample was tailored a desired and at shape to temperature. obtain a final 3D GF-based acoustic pressure sensor after the PDMS was dried at room temperature. 3. 3. Results Results and and Discussion Discussion Figure 2a shows the photograph of the acoustic acoustic pressure pressure sensor. sensor. Such fabricated devices are wearable and and bendable bendabledue duetotothe theflexible flexiblenature nature of both PDMS and graphene. Figure 2b presents of both PDMS and graphene. Figure 2b presents the the scanning electron microscopy 3D graphene foam, shows which the shows theexhibits 3D GF scanning electron microscopy (SEM)(SEM) imageimage of theof 3Dthe graphene foam, which 3D GF exhibits a well-defined micronetwork porous network with a pore diameter of about µm. 200~400 μm. a well-defined micro porous structurestructure with a pore diameter of about 200~400

Figure 2. 2. (a) (a)Photograph Photographofofthe theacoustic acousticpressure pressure sensor; SEM image of 3D graphene foam; Figure sensor; (b)(b) SEM image of 3D graphene foam; (c) (c) Raman spectrum of the 3D GF; (d) XRD patterns of the 3D GF. Raman spectrum of the 3D GF; (d) XRD patterns of the 3D GF.

The Raman spectrum spectrum of of the the 3D 3D GF GF is is shown shownin inFigure Figure2c. 2c.The Theshape shapeof ofthe the2D 2Dband band(~2700 (~2700cm cm−−11)) − 1 −1 between the the 2D and bands ) prove the 3D is composed and the theintensity intensityratio ratio between 2D the andGthe G (~1560 bands cm (~1560 cm )that prove thatGF the 3D GF is of few-layeroforfew-layer multilayer [23]. In addition, Raman spectrum measured on composed or graphene multilayersheets graphene sheets [23]. the In addition, the Raman spectrum − 1 −1 free-standing shows a strongly suppressed defect-related D band (~1350 cmD ), indicating measured on GF free-standing GF shows a strongly suppressed defect-related band (~1350overall cm ), high qualityoverall of the graphene in GF. 2d is an illustration of the (XRD) patterns indicating high quality of Figure the graphene in GF. Figure 2dX-ray is andiffraction illustration of the X-ray of 3D GF. The intensity of the carbon peak in the pattern without nickel peak indicates a pure 3D pore diffraction (XRD) patterns of 3D GF. The intensity of the carbon peak in the pattern without nickel structure of as-fabricated GF. structure of as-fabricated GF. peak indicates a pure 3D pore 3, 3, the size of of the the PDMS PDMSmatrix matrixfor forthe the3D 3DGF GFsensor sensorused usedininthe theexperiment experiment × 10 5 mm The size is is 3030 × 10 × 5×mm the internal dimensions GF composited inare PDMS 6 ×3.2In×order 1 mm . In order to study internal dimensions of theof 3Dthe GF3D composited in PDMS 6 × 2 are × 1 mm to3study the frequency the frequency responseof characteristics the sensor, weforks choose tuning forkssource. as an excitation response characteristics the sensor, weofchoose tuning as an excitation Figure 1b source. shows Figure 1b shows the tuning fork The vibration The sensor attached one arm of The the tuning the tuning fork vibration setup. sensorsetup. is attached at oneisarm of theattuning fork. fork. begin The tuning forkstobegin to vibrate generateacoustic a high frequency wave the other forks to vibrate generate a high to frequency wave whenacoustic the other armwhen is stroked with is stroked with a hammer. Thiswith vibration signal with a high-frequency stress on results in a aarm hammer. This vibration signal a high-frequency stress on GF, results inGF, a mechanical mechanical deformation (compression and decompression) and recovery GF. When the deformation deformation (compression and decompression) and recovery in GF. in When the deformation and and recovery occurs, band graphene dynamically changes,leading leadingtotothe thechange change of of its recovery occurs, the the band gapgap of of graphene dynamically changes, resistance due to the piezo-resistive effect [24–26]. In our experiment, the acoustic wave propagation m/s. A velocity in PDMS is over 1000 m/s. Avoltage voltage divider divider circuit circuit is is setup setup to to evaluate evaluate the the frequency frequency signal detection capability. The fixed resistor (with resistance RS) and 3D GF sensor (with resistance R) are

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detection capability. The fixed resistor (with resistance RS ) and 3D GF sensor (with resistance R) are connected in 1069 series in the circuit. The voltage source is adjustable at 1~12 V. The voltage Sensors 2017, 17, 4 of of 8 the graphene sensor V G is measured by an oscilloscope, where RS is selected to satisfy RS >> R. We usually connected in series in the circuit. The voltage source is adjustable at 1~12 V. The voltage of the take R S ≈ 10 R0 , and the output of the oscilloscope V G is then related by graphene sensor VG is measured by an oscilloscope, where RS is selected to satisfy RS >> R. We usually take RS ≈ 10 R0, and the output VG is then related by R of the oscilloscope R

VG =

RS R+ R

VG 

RS  R

VS ≈

VS . RRS RS S . RS are the

VS 

V

(1)

(1)

Here, V S is the power supply voltage, while R and resistances of the GF/PDMS sensor and the resistor, to the initial resistance GF/PDMS deformation. Here, Vrespectively, S is the power R supply while R and RS areofthe resistancessensor of the without GF/PDMS sensor 0 refervoltage, and the resistor,that respectively, R0 refer thesensor initial isresistance of GF/PDMS sensorvoltage without Equation (1) shows the resistance of thetoGF proportional to the output VG on deformation. This Equation (1) shows that the resistance of the GF sensor is proportional to the output by the oscilloscope. indicates the mechanical deformation and external stimuli can be detected voltagethe VG output on the oscilloscope. measuring voltage VG .This indicates the mechanical deformation and external stimuli can be detected by measuring the output voltage VG. To carry out this test, five kinds of frequency tuning fork were chosen in the experiments, and their To carry out this test, five kinds of frequency tuning fork were chosen in the experiments, and natural frequencies were calibrated with microphone. The natural frequency is, respectively, 128.0 Hz, their natural frequencies were calibrated with microphone. The natural frequency is, respectively, 247.6128.0 Hz, 516.8 Hz, 930.1 Hz, and 1873.0 Hz. Hz, 247.6 Hz, 516.8 Hz, 930.1 Hz, and 1873.0 Hz. Figure 3a is photograph ofof the totest testthe the acoustic waves of tuning the tuning Figure 3aa is a photograph theexperiment experiment setup setup to acoustic waves of the forks.forks. The sensor is adhered to one arm ofofthe Thetuning tuningfork fork begins to vibrate when the other The sensor is adhered to one arm thetuning tuningfork. fork. The begins to vibrate when the other arm is stroked withwith a rubber hammer. shaking,resulting resulting dynamic variation arm is stroked a rubber hammer.The Thesensor sensor is shaking, in in thethe dynamic variation on on its resistance. Figure 3b–f show the variation of its resistance caused by the tuning fork vibration. its resistance. Figure 3b–f show the variation its resistance caused by the tuning fork vibration. The signals of Figure were obtainedby bythe thetuning tuning forks frequency, respectively. The signals of Figure 3b–f3b–f were obtained forkswith withnatural natural frequency, respectively.

Figure 3. (a)3.A(a)photograph of of thethe experiment (b–f)Signals Signalsobtained obtained tuning Figure A photograph experimentsetup; setup; (b–f) by by thethe tuning forksforks with with natural frequency, respectively: 128128 Hz,Hz, 247.6 Hz,Hz, 516.8 Hz, 930.1 dotted line natural frequency, respectively: 247.6 516.8 Hz, 930.1Hz, Hz,and and1873 1873 Hz. Hz. The The blue blue dotted line means recovery timebe should longer thaninx-axis (b–f). Theisinsert is the high-frequency means recovery time should longerbethan x-axis (b–f).inThe insert the high-frequency oscillation signal of the of the curve inside signaloscillation of the portion theportion curve inside the red box.the red box.

As shown in Figure 3b–f, the signals have instantaneous arising edge and then back up slowly.

As shown in Figure 3b–f, the signals have instantaneous arising edge and then back up slowly. We believe that this phenomenon is caused by the suddenly mechanical deformation of the 3D GF We believe thatrubber this phenomenon is caused by fork. the suddenly mechanical of strikes the 3D GF due to the hammer striking the tuning When a rubber hammerdeformation instantaneously due to the rubber hammer striking the tuning fork. When a rubber hammer instantaneously strikes

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thethe tuning fork, itit causes a large amplitudemechanical mechanical deformation the 3D GF momentarily, tuning fork, causes deformation ofthe theof 3D GFmomentarily, momentarily, and the tuning fork, it causesa alarge largeamplitude amplitudemechanical deformation of 3D GF and and induces a rapid change in the electrical resistance. As shown in Figure 3b–f, the steep induces a rapid change shown in in Figure Figure3b–f, 3b–f,the thesteep steeprising rising edge is induces a rapid changeininthe theelectrical electricalresistance. resistance. As shown edge isrising edge isajust a few milliseconds. It indicates responses to mechanical deformation very justjust few milliseconds. ItItindicates that the 3D GF GF responses to mechanical mechanical deformation very a few milliseconds. indicates thatthat the the 3D 3D responses to deformation very quickly. However, the 3D GF relatively longer recovery time tomechanical the mechanical deformation. quickly. However, the 3D GFhas hasaaarelatively relativelylonger longer recovery time to deformation. In In quickly. However, the 3D GF recovery time tothe the mechanical deformation. Figure 3b–f, recoverytime timeeven even reached several seconds. 3b–f, its recovery reached several seconds. In Figure Figure 3b–f, itsits recovery time even reached several seconds. Figure 4 is thespectra spectra insets Figure 3b–f transform (FFT) algorithm. Figure is the ofofinsets ofofFigure fast Fourier Fourier transform (FFT) algorithm. It Figure 4 is4 the spectra of insets of Figure 3b–f3b–f withwith fast fast Fourier transform (FFT) algorithm. It It shows shows that the five main frequencies of the acoustics are, respectively, 126.5 Hz, 241.3 Hz, 508.7 Hz, shows thatmain the five main frequencies of the acoustics are, respectively, 126.5 Hz, 241.3 Hz,Hz, 508.7 Hz,Hz, that the five frequencies of the acoustics are, respectively, 126.5 Hz, 241.3 Hz, 508.7 929.3 929.3 Hz, and 1870 Hz.They Theyare arenearly nearlyequal equal to to the the natural natural frequency ofofthe It It indicates 929.3 Hz, and 1870 Hz. frequency thetuning tuningfork. indicates and 1870 Hz. They are nearly equal to the natural frequency of the tuning fork. Itfork. indicates that the that the sensor can detect the natural frequency of the tuning fork. There is a small deviation that the sensor can detect the natural frequency of the tuning fork. There is a small deviation sensor can detect the natural frequency of the tuning fork. There is a small deviation between these between these twosets setsofofvalues, values,because because the the frequency frequency of the isisdetermined byby thethe between these two of determined the tuning tuningfork forkthe determined two sets of values, because the frequency of the is tuning fork structure, tuning fork structure, which changes when thetuning sensorfork contacts the tuning by fork, resulting in a small tuning fork structure, changes when the sensor contacts thein tuning fork, resulting in frequency. a small which changes when thewhich sensor contacts the tuning fork, resulting a small change in the change in the frequency. change in the frequency.

Figure 4. The spectra of insets of Figure 3b–f. Figure 4. The spectra of insets of Figure 3b–f. Figure 4. The spectra of insets of Figure 3b–f. The capability of monitoring human physiological signals is essential for 3D GF sensor to be The capability of monitoring physiological signals 3D in GFFigure sensor applied in the fields of e-skin. A human wrist pulse signal test with a 3D is GFessential sensor is for shown 5a.to be The capability of monitoring human physiological signals is essential for 3D GF sensor to be applied thepulse fieldssignal of e-skin. A wrist pulse signal testin with a 3D sensor is shown inartery Figure 5a. Thein wrist was read out accurately as shown Figure 5b,GF in which a typical radial applied in the fields of e-skin. A wrist pulse signal test with a 3D GF sensor is shown in Figure 5a. pulsepulse waveform was obtained with two clearlyasdistinguishable peaks P3) aand a lateradial systolic The wrist signal was read out accurately shown in Figure 5b,(P1 in and which typical artery The wrist pulse signal was read out accurately as shown in Figure 5b, in which a typical radial artery augmentation shoulder (P2).with Figure 5c clearly shows magnified peaks of P2 and P3and in Figure 5b.aThe line pulse waveform was obtained two distinguishable peaks (P1 P3) and late systolic pulse waveform was obtained with two clearly distinguishable peaks (P1 and P3) and a late systolic shape is known to be caused by the constitution of the bloodpeaks pressure from theP3 leftinventricle contracts augmentation shoulder (P2). Figure 5c magnified ofP2 P2 and Figure The line augmentation shoulder (P2). 5c shows shows magnified peaks and in Figure 5b.5b. The line and a reflective wave from theFigure lower body, and similar results were of observed byP3 Gong et al. [9].

shape is is known totobebecaused the blood bloodpressure pressurefrom fromthe theleft leftventricle ventricle contracts shape known causedby bythe theconstitution constitution of of the contracts and a reflective wave from the lower body, and similar results were observed by Gong et al. and a reflective wave from the lower body, and similar results were observed by Gong et al. [9]. [9].

Figure 5. (a) Wrist pulse signal test with GF/PDMS sensor; (b) Typical radial artery pulse waveform obtained from GF/PDMS sensor; (c) Magnified peaks of P2 and P3 in (b).

Figure Wrist pulsesignal signaltest testwith withGF/PDMS GF/PDMS sensor; pulse waveform Figure 5. 5. (a)(a) Wrist pulse sensor;(b) (b)Typical Typicalradial radialartery artery pulse waveform obtained from GF/PDMSsensor; sensor; (c) (c) Magnified Magnified peaks obtained from GF/PDMS peaksof ofP2 P2and andP3 P3inin(b). (b).

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The flexible and highly sensitive 3D GF sensor was also attached to the skin of the neck for the phonetic signal test, as shown in Figure 6a. Figure 6b shows the measured 3D GF sensor signal of a tester speaking “Nanjing,” in which two distinguished waveforms could be ascribed to the change in resistance arising arisingfrom from muscle motions to pronounce individual Furthermore, high resistance muscle motions to pronounce individual syllables.syllables. Furthermore, high frequency frequency signals superimposed on the waveforms are also (the observed (the labeled part 6b). in Figure signals superimposed on the waveforms are also observed labeled part in Figure Figure6b). 6c Figure the 6c shows the magnified viewfrequency of high frequency signals, regular oscillation is shows magnified view of high signals, in whichina which regulara oscillation signal issignal clearly clearly observed. The characteristic frequency determined by fast Fourier transform 6d) is observed. The characteristic frequency determined by fast Fourier transform (Figure (Figure 6d) is around around Hz,the within the frequency of phonation. human phonation. 200 Hz, 200 within frequency range ofrange human

Figure 6. 6. (a) (a) Phonation Phonation signal signal test test with withGF/PDMS GF/PDMS sensor; sensor; (b) (b) The The measured measured GF/PDMS GF/PDMS sensor signal Figure sensor signal when a tester is speaking Chinese characters “Nanjing”; (c) The amplified view of high frequency when a tester is speaking Chinese characters “Nanjing”; (c) The amplified view of high frequency (d) Fast Fast Fourier Fourier transform transform of of high high frequency frequency oscillation oscillation signal. signal. oscillation signal in Figure 6b; (d)

4. Conclusions 4. Conclusions We have havedeveloped developed efficient, low-cost approach to fabricating a flexible and wearable We anan efficient, low-cost approach to fabricating a flexible and wearable acoustic acoustic pressure sensor based on 3D graphene foam. The sensor is made of flexible macro block pressure sensor based on 3D graphene foam. The sensor is made of flexible macro block material, material, easily made into various sizes and shapes, and it is economical, simple to prepare, easy to easily made into various sizes and shapes, and it is economical, simple to prepare, easy to operate, operate, easy toand install etc.importantly, More importantly, test results showthe that the sensor easy to install test,and etc.test, More the testthe results show that sensor has a has gooda good response to the natural frequency of the tuning fork, human pulse, and sound in real-time. response to the natural frequency of the tuning fork, human pulse, and sound in real-time. Because of Because of the distinctive of high sensitivity and the flexible, thehas sensor has potential a wide potential for the distinctive features offeatures high sensitivity and flexible, sensor a wide for health health monitoring, in vitro diagnostics, advanced therapies, and transient pressure detection. monitoring, in vitro diagnostics, advanced therapies, and transient pressure detection. Acknowledgments: The Theproject project supported byNational the National Nature Foundation Science Foundation of China Acknowledgments: waswas supported by the Nature Science of China (51402155), the Jiangsu Natural Science Foundation China (BK20151508) and sponsoredand by NUPTSF (Grant NO. NY215037 (51402155), the Jiangsu Natural ScienceofFoundation of China (BK20151508) sponsored by NUPTSF (Grant and NO. NY215163). NO. Grant NY215037 and Grant NO. NY215163). Author Contributions: All authors performed the research. K.Q. wrote the manuscript. R.X. was the research Author Contributions: All authors performed the research. K.Q. wrote the manuscript. R.X. was the research supervisor. R.X. led the research, and initiated manuscript writing. supervisor. R.X. led the research, and initiated manuscript writing. Conflicts of Interest: The authors declare no conflicts of interest. Conflicts of Interest: The authors declare no conflicts of interest.

References References 1. 1.

2.

Samad, Y.A.; Li, Y.; Schiffer, A.; Liao, K.; Alhassan, S.M. Graphene Foam Developed with a Novel Two-Step Samad, Y.A.; Li, Y.; Schiffer, A.; Liao, K.; Alhassan, S.M. Graphene Foam Developed with a Novel Two-Step Technique for Low and High Strains and Pressure-Sensing Applications. Small 2015, 11, 2380–2385. Technique for Low and High Strains and Pressure-Sensing Applications. Small 2015, 11, 2380–2385. [CrossRef] [PubMed] 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. Highly sensitive flexible pressure sensors with microstructured rubber dielectric layers. Nat. Mater. 2010, 9, 859–864.

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2.

3.

4.

5.

6.

7. 8.

9.

10. 11. 12. 13.

14. 15.

16.

17. 18. 19.

20.

21.

7 of 8

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. Highly sensitive flexible pressure sensors with microstructured rubber dielectric layers. Nat. Mater. 2010, 9, 859–864. [CrossRef] [PubMed] Sokolov, A.N.; Tee, B.C.K.; Bettinger, C.J.; Tok, J.B.H.; Bao, Z. Chemical and engineering approaches to enable organic field-effect transistors for electronic skin applications. Acc. Chem. Res. 2012, 45, 361–371. [CrossRef] [PubMed] Lipomi, D.J.; Vosgueritchian, M.; Tee, B.C.-K.; Hellstrom, S.L.; Lee, J.A.C.; Fox, H.; Bao, Z. Skin-like pressure and strain sensors based on transparent elastic films of carbon nanotubes. Nat. Nanotechnol. 2011, 6, 788–792. [CrossRef] [PubMed] Liang, Y.; Yu, L.; Yang, R.; Li, X.; Qu, L.; Li, J. High sensitive and selective graphene oxide/molecularly imprinted polymer electrochemical sensor for 2,4-dichlorophenol in water. Sens. Actuators B Chem. 2017, 240, 1330–1335. [CrossRef] Schwartz, G.; Tee, B.C.-K.; Mei, J.; Appleton, A.L.; Kim, D.H.; Wang, H.; Bao, Z. Flexible polymer transistors with high pressure sensitivity for application in electronic skin and health monitoring. Nat. Commun. 2013, 4, 1859. [CrossRef] [PubMed] Jeong, Y.R.; Park, H.; Hong, S.Y.; Ha, J.S.; Jin, S.W.; Lee, S.S. Highly Stretchable and Sensitive Strain Sensors Using Fragmentized Graphene Foam. Adv. Funct. Mater. 2015, 25, 4228–4236. [CrossRef] Qin, Y.; Peng, Q.; Ding, Y.; Lin, Z.; Wang, C.; Li, Y.; Li, J.; Yuan, Y.; He, X.; Li, Y. Lightweight, Superelastic, and Mechanically Flexible Graphene/Polyimide Nanocomposite Foam for Strain Sensor Application. ACS Nano. 2015, 9, 8933–8941. [CrossRef] [PubMed] Gong, S.; Schwalb, W.; Wang, Y.; Chen, Y.; Tang, Y.; Si, J.; Shirinzadeh, B.; Cheng, W. A wearable and highly sensitive pressure sensor with ultrathin gold nanowires. Nat. Commun. 2013, 5, 163–180. [CrossRef] [PubMed] Marcu, A.; Nicolae, I.; Viespe, C. Active surface geometrical control of noise in nanowire-SAW sensors. Sens. Actuators B Chem. 2016, 231, 469–473. [CrossRef] Zhang, Y.; Sheehan, C.J.; Zhao, J.; Zou, G.; Luo, H.; Xiong, J.; Zhu, Y.T.; Jia, Q.X. Polymer-embedded carbon nanotube ribbons for stretchable conductors. Adv. Mater. 2010, 22, 3027–3031. [CrossRef] [PubMed] Rinaldi, A.; Tamburrano, A.; Fortunato, M.; Sarto, M.S. A Flexible and Highly Sensitive Pressure Sensor Based on a PDMS Foam Coated with Graphene Nanoplatelets. Sensors 2016, 16, 2148. [CrossRef] [PubMed] Dong, X.; Cao, Y.; Wang, J.; Chan-Park, M.B.; Wang, L.; Huang, W.; Chen, P. Hybrid structure of zinc oxide nanorods and three dimensional graphene foam for supercapacitor and electrochemical sensor applications. RSC Adv. 2012, 2, 4364–4369. [CrossRef] Bolotin, K.I.; Sikes, K.J.; Jiang, Z.; Klima, M.; Fudenberg, G.; Hone, J.; Kim, P.; Stormer, H.L. Ultrahigh electron mobility in suspended graphene. Solid State Commun. 2008, 146, 351–355. [CrossRef] Bae, G.Y.; Pak, S.W.; Kim, D.; Lee, G.; Kim, D.H.; Chung, Y.; Cho, K. Linearly and highly pressure-sensitive electronic skin based on a bioinspired hierarchical structural array. Adv. Mater. 2016, 28, 5300–5306. [CrossRef] [PubMed] Li, J.; Zhao, S.; Zeng, X.; Huang, W.; Gong, Z.; Zhang, G.; Sun, R.; Wong, C. Highly Stretchable and Sensitive Strain Sensor Based on Facilely Prepared Three-Dimensional Graphene Foam Composite. ACS Appl. Mater. Interfaces 2016, 8, 18954–18961. [CrossRef] [PubMed] Xie, X.; Zhou, Y.; Bi, H.; Yin, K.; Wan, S.; Sun, L. Large-range control of the microstructures and properties of three-dimensional porous graphene. Sci. Rep. 2013, 3, 2117. [CrossRef] [PubMed] Worsley, M.A.; Pauzauskie, P.J.; Olson, T.Y.; Biener, J.; SatcherJr, J.H.; Baumann, T.F. Synthesis of graphene aerogel with high electrical conductivity. J. Am. Chem. Soc. 2010, 132, 14067–14069. [CrossRef] [PubMed] Chen, Z.; Ren, W.; Gao, L.; Liu, B.; Pei, S.; Cheng, H.-M. Three-dimensional flexible and conductive interconnected graphene networks grown by chemical vapour deposition. Nat. Mater. 2011, 10, 424–428. [CrossRef] [PubMed] Xu, R.Q.; Lu, Y.Q.; Jiang, C.H.; Chen, J.; Mao, P.; Gao, G.H.; Zhang, L.B.; Wu, S. Facile fabrication of three-dimensional graphene foam/poly(dimethylsiloxane) composites and their potential application as strain sensor. ACS Appl. Mater. Interfaces 2014, 6, 13455–13460. [CrossRef] [PubMed] Smith, A.D.; Niklaus, F.; Paussa, A.; Vaziri, S.; Fischer, A.C.; Sterner, M.; Forsberg, F.; Delin, A.; Esseni, D.; Palestri, P.; et al. Electromechanical Piezoresistive Sensing in Suspended Graphene Membranes. Nano Lett. 2013, 13, 3237–3242. [CrossRef] [PubMed]

Sensors 2017, 17, 1069

22.

23.

24.

25. 26.

8 of 8

Maturos, T.; Phokaratkul, D.; Jaruwongrungsee, K.; Wisitsoraat, A.; Maturos, K.; Lomas, T.; Tuantranont, A. Fabrication of Stretchable 3D Graphene Foam/PolyDimethylsiloxane Composites for Strain Sensing. In Proceedings of the 15th IEEE International Conference on Nanotechnology, Rome, Italy, 27–30 July 2015. Ferrari, A.C.; Meyer, J.C.; Scardaci, V.; Casiraghi, C.; Lazzeri, M.; Mauri, F.; Piscanec, S.; Jiang, D.; Novoselov, K.S.; Roth, S.; et al. Raman spectrum of graphene and graphene Layers. Phys. Rev. Lett. 2006, 97, 13831–13840. [CrossRef] [PubMed] Hammock, M.L.; Chortos, A.; Tee, B.C.-K.; Tok, J.B.-H.; Bao, Z. 25th Anniversary Article: The Evolution of Electronic Skin (E-Skin): A Brief History, Design Considerations, and Recent Progress. Adv. Mater. 2013, 25, 5997–6038. [CrossRef] [PubMed] Bae, S.-H.; Lee, Y.; Sharma, B.K.; Lee, H.-J.; Kim, J.-H.; Ahn, J.-H. Graphene-based transparent strain sensor. Carbon 2013, 51, 236–242. [CrossRef] Zhao, J.; Zhang, G.; Shi, D. Review of graphene-based strain sensors. Chin. Phys. B 2013, 22, 057701. [CrossRef] © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).