Carbon Nanotube Embedded Nanostructure for Biometrics - ACS

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CNT Embedded Nanostructure for Biometrics Juhyuk Park, Jae Ryoun Youn, and Young Seok Song ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.7b15567 • Publication Date (Web): 30 Nov 2017 Downloaded from http://pubs.acs.org on December 5, 2017

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ACS Applied Materials & Interfaces

CNT Embedded Nanostructure for Biometrics Juhyuk Parka, Jae Ryoun Youna*, and Young Seok Songb* a

Research Institute of Advanced Materials (RIAM), Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea.

b

Department of Fiber System Engineering, Dankook University, Gyeonggi Do 16890, Republic of Korea.

*Corresponding author: Prof. Jae Ryoun Youn Research Institute of Advanced Materials (RIAM) Department of Materials Science and Engineering Seoul National University E-mail: [email protected] *Corresponding author: Prof. Young Seok Song Department of Fiber System Engineering Dankook University E-mail: [email protected]

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ABSTRACT Low electric energy loss is a very important problem to minimize the decay of transfered energy intensity due to impedance mismatch. This issue has been dealt with by adding an impedance matching layer at the interface between two media. A strategy was proposed to improve the charge transfer from human body to a biometric device by using an impedance matching nanostructure. Nanocomposite pattern arrays were fabricated with shape memory polymer (SMP) and carbon nanotubes (CNTs). The shape recovery ability of the nanopatterns could enhance durability and sustainability of the structure. It was found that the composite nanopatterns improved the current transfer by two times, compared with the non-patterned composite sample. The underlying mechanism of the enhanced charge transport was understood by carrying out numerical simulation. We anticipate that this study can provide a new pathway for developing advanced biometric devices with high sensibility of biological information.

Keywords: Nanocomposite; Nanopattern; Biometric sensing; Smart material; Sustainable material; Energy transfer; Numerical analysis;


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INTRODUCTION Biometric systems,1,2 which are capable of uniquely identifying or evaluating a person, have emerged with the rise of smart electronic devices. As one of the biometric systems, a personal identification system has been developed for encrypting personal information using finger prints,3 hand geometry,4 or other biological templates. Furthermore, the biometric system is also utilized for gathering anthropometric information for in-vitro diagnosis, such as electrocardiogram,5,6 ultrasonography,7,8 electric impedance tomography (EIT),9 and body mass index (BMI).10,11 A prerequisite for improving the accuracy of biometric devices is to improve signal quality by reducing energy loss at the interface between the two media, i.e., human body and a touch pad. In general, an impedance matching gel is used under wet condition as an intermediate medium at the interface.12 Developing highly conductive materials is essential to advance touch pads in biometric systems.13–15 Polymeric nanocomposites have been investigated to accomplish not only electrically conductive but also mechanically robust materials.16–18 Among them, carbon nanotube (CNT) composites are one of the most widely studied materials for substituting metals, due to the high strength and exceptional electron mobility of CNTs.19–26 Indeed, CNTs possess unique physico-chemical properties such as super-hydrophobicity27 and indirect heating for a remote control of shape memory polymer (SMP) composites.28–30 Nanopattern array31–35 is an effective structure to increase energy transfer due to gradual change in impedance at the interface between discrete media. In nanosurface of Moth’ eye, optical impedance matching, i.e., refractive index matching is a key mechanism to enhance light transmittance by directing ray toward patterns.36–38 The nanopattern/air coexisting region at the interface between air and the substrate acts as a gradually changing refractive index layer (GRIN layer).39 ‘Impedance matching’ means a way to reduce loss induced by the impedance difference of two different connection stages.40–42 The nanostructure can also serve as an electrical impedance matching layer between two different media, by directing and focusing the electrical current flow toward the patterns. This impedance matching strategy has advantages over the gel-based method since it is a dry-state method. However, surface nanopatterns have poor structural stability after direct contact. On the other hand, shape memory polymers (SMP)43–45 is a smart material that can memorize and recover the original shape. So far, SMPs have been applied for various purposes, e.g., to impart a switchable function to microstructures46,47 or to be used for adhesion in a dry state.48–50 Recently, our study has attempted to apply SMPs to enhance 3 ACS Paragon Plus Environment


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the sustainability of nanostructures for overcoming the mechanical drawback of nanopatterns.29,38 When a nanopattern is composed of a general polymer, it is easily broken by an external impact. However, the pattern can be compressed rather than being destroyed if it is made of SMP, and the deformed pattern can be recovered by heating. SMPs have also been studied in a myriad of biological applications due to their fascinating functionality and biocompatibility.44,51–53 In this study, we explored a nanostructure fabricated using CNTs/SMP nanocomposites for a biometric sensing system. The amount of delivered energy was increased using the synergistic nanoeffects; CNT-induced effect and nanopattern-induced effect. Furthermore, SMP was employed as a matrix material for retaining the nanoeffects. A shape memory copolyacrylate (SMCPAc), which can be triggered at body temperature was synthesized. After blending CNTs with the SMCPAc resin, a replica moulding method was employed to fabricate the nanopattern arrays. CNTs in the patterns were identified using an X-ray photoelectron spectroscope (XPS) and a focused ion beam (FIB). Shape memory behavior at nanoscale was verified experimentally and numerically. The flow of electric current strength from the human skin to a biometric sensor was measured using a touch sensor circuit and simulated numerically.


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RESULTS AND DISCUSSION Preparation of materials A replica moulding method was employed to inversely copy the geometry of the prepared silicon master for constructing SMCPAc/CNT nanopattern arrays. Figure 1(a) illustrates a scheme of the fabrication process. The SMCPAc material38,54 was designed for the shape recovery capability at body temperature and synthesized using two monomers (methyl methacrylate (MMA) and butyl methacrylate (BMA)) and a cross-linker (polyethyleneglycol dimethacrylate (PEGDMA, Mw=525). A SMCPAc/CNT composite resin was prepared by incorporating 1 wt% CNTs into the SMCPAc resin. 1 wt% of CNTs is sufficient to form an interconnected network of CNTs in the matrix for fast charge transfer.55 The composite resin was injected into nanoholes arrayed on the silicon master before polymerization (Figure 1(ai)). The viscosity of the composite resin was measured using a rheometer and compared with a pure SMCPAc resin (see Supporting Information Figure S1). Despite the high viscosity of the composite resin, the high production rate of the nanopatterns was achieved due to the shear thinning behavior and surface tension of the resin.56,57 After filling the nanoholes, the resin was polymerized by thermal irradiation using two reactions, chain elongation reaction and crosslinking reaction, simultaneously (Figure 1(a-ii)). The scheme for synthesis is explained in SI Figure S2(a-d). Free radicals were generated on AIBN (2,2-Azobisisobutyronitrile, a thermal initiator) by thermal decomposition of the N-C bonds of AIBN. The generated radicals reacted with alpha-carbons of adjacent monomers and crosslinkers. As a result, polymer chains were lengthened, and 3-D shape memory networks were formed. The fabricated samples were coded as IB (Intrinsic bare SMCPAc sample), IP (Intrinsic patterned SMCPAc sample), CB (CNT-embedded bare SMCPAc/CNT sample), and CP (CNT-embedded patterned SMCPAc/CNT sample), depending on the presence and absence of CNTs and patterns. It is of great importance to verify the existence of CNTs in the nanopatterns which contribute to impedance matching. For this, XPS analysis of the nanocomposite in the nanopattern was performed for indirect confirmation of the existence of CNTs in the pattern. The results showed a discrepancy in the chemical binding energy between the IP and the CP (Figure 1(b) and SI Table S1). X-ray of XPS penetrates only a shallow depth beneath the surface,20 and a subwavelength structure (SWS) of nanopatterns scatters the X-ray. Since the ray cannot reach the bottom area, it is possible to examine the carbon binding energy of the pattern alone. The graph of the CP sample shows a 10% higher content of C-C and C-H 5 ACS Paragon Plus Environment


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bonding peaks than the peaks of the IP sample. The difference in chemical binding energy verified the presence of CNTs in the nanopatterns. In addition, a dual beam focused ion beam (DB-FIB) system was used to directly observe the presence of CNTs in the nanopattern (Figure 1(c)). Several CNTs were found in the cross section of the CP sample, whereas the IP sample did not show any particles on the cross-section. Mechanical properties of the samples were examined by using a universal testing machine (UTM) and a nanoindenter was employed for nanoscale analyses (Figure 1(d-e)). It was found from the tensile test that the elastic modulus of the CP sample was increased by 168% compared with that of the IP sample, and the tensile strength was also doubled. Difference in the mechanical properties between the patterned and the non-patterned specimens are not readily measured in UTM experiments since they have the same material composition. Therefore, the nanoindentation was harnessed for the nanoscale mechanical analysis. It was verified that the inclusion of CNTs enhanced the stiffness of the nanopatterns by 9 times (i.e., from 359.8 to 3254.4 N/m) as well as the stiffness of the planar region by 7 times (i.e., from 515.4 to 3782.1 N/m). The nanoindentation results indicated that the composite patterns could withstand greater damages than the intrinsic patterns. Topological characterizations Nanotopology of the CP sample was observed by using a scanning electron microscope (SEM) and an atomic force microscope (AFM). Dimension of the nanopatterns was 200 nm in height, 200 nm in diameter at the bottom surface, and distance between the peaks was 200 nm (Figure 2(a) and (d)). Transcription ratio of the nanopattern was about 90% due to the trapped air in the holes. The gradually changing geometry of the nanopatterns made it possible to construct an impedance matching system.36,38 The shape memory behavior of nanopatterns was analyzed by conducting a cyclic shape memory test at nanoscale. A pressure of about 30 MPa was first applied to the nanopatterns at 40℃ for compressive deformation, followed by cooling. A height of the deformed patterns was about 55 nm (Figure 2(b)). It has been proved in previous reports that the nanoscale impedance matching effect must be weaken when the nanopatterns was compressed and deformed.29,38 Deformed nanopatterns cannot provide sufficient nanofunctionality due to their low height and flat top surface. Detail explanation about the underlying mechanism is discussed in ‘Enhanced current transfer’ section and ‘Nanoelectronic modeling’ section. For this reason, shape memory polymer (SMP) was employed as a substrate for biometric systems. Also, the synthesized SMCPAc has an ability 6 ACS Paragon Plus Environment

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to recover at body temperature. As a result, the deformed patterns could recover their permanent geometry by heating. Figure 2(c) shows the fully recovered shape of the patterns. The nanopattern array was illustrated in Figure 2(d). The embedded CNTs have a diameter of 15 to 20 nm, and a length of about 20 m with a high aspect ratio of about 2000:1 (Figure 2(e)). The CNTs in the matrix were dispersed well as shown in Figure 2(f). Thermomechanics analysis Thermomechanics of the samples was investigated carrying out a dynamic mechanical thermal analysis (DMTA). Figure 3(a) shows the thermomechanical properties of the IP sample and the CP sample at temperature ranging from -20 to 100℃. Embedding CNTs increased the storage modulus ( ) by 2 times. When compared with the modulus drop of the IP sample (i.e., around 2000 MPa), the relatively large reduction in modulus of the CP sample (i.e., around 4250 MPa) could reinforce the capability for shape memory and recovery.28 The glass transition temperature ( ) of the CP sample was lowered from 46℃ (IP) to 40℃ (CP). Addition of CNTs increases the thermal conductivity of materials, leading to a rapid heat transfer across the specimens.58 SMCPAc/CNT composites are suitable materials for developing bio-based instruments due to their mechanical and thermodynamic properties, such as very high modulus drop and shape recovery capability at body temperature. Cyclic shape memory experiments were conducted to obtain stress-strain-temperature (SST) curves by using DMTA (see the solid line in Figure 3(b)). The IP and CP specimens were used for the measurement. Details of the test are presented in Supplementary Information (SI). The specimen was deformed to 3% compressive strain at 40℃ (iii) and cooled to 0℃ (iiiii). Stored strain energy maintained the deformed shape even after the applied stress was removed (iiiiv). Gentle heating triggered the shape restoration with over 90% shape recovery ratio. Remote shape recovery could also be done by irradiating microwave in just 1 second, due to joule heating by CNTs (SI Figure S3).59 Theoretical shape memory behavior was demonstrated by numerical simulation with the hyperelastic neo-Hookean model, which uses entropic elastic energy.29,38,60 More details about the simulation method are explained in Supplementary Information. By applying thermomechanical material parameters, the theoretical SST curve was obtained as the dot line in Figure 3(b). The inconsistency between two curves in the cooling and heating step originated from the assumption that phase transition is linearly proportional to the glassy state fraction (α) according to temperature change.54 Also, some errors could be caused by the fact that the neo-Hookean model cannot take into account the viscoelastic properties of the 7 ACS Paragon Plus Environment


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material. However, we can see that the numerical modeling works quite well with little error in our cases. The similar simulation was carried out at nanoscale using the unit cell constructed based on the topology of the nanopattern arrays on the surface of the CP sample. Figure 3(c) and Movie S1 show the von Mises stress contours during the shape memory cycle. The nanopatterns were compressed up to 55 nm at 40℃ in the loading step (iii), where the deformation energy was saved as an entropy, the Helmholtz potential. The cooling step (iiiii) generated glassy regions in the pattern. The following unloading step did not trigger the shape recovery since the stored elastic entropy at high temperature could overcome the high stiffness. The deformed patterns restored their original shape by changing the material from glassy to rubbery states. The stress distribution in the pattern could be explained numerically, and the shape memory behavior at nanoscale was visualized theoretically. Enhanced current transfer A touch sensor circuit was employed to examine the current transfer performance in a biometric system. Schematic illustration of the circuit is shown in Figure 4(a), and the relevant configuration is provided in the Materials and Methods section. While supplying a voltage to the circuit in contact with a finger, returning current from body was measured. Before measuring the return current, the electrical conductivity of the sample was measured by using a 2-point-probe method. The electrical conductivity is the inverse value of the electrical resistance, i.e., electrical impedance. SI Figure S4 shows that addition of 1 wt% CNTs into the SMCPAc matrix results in an increase of 7 to 8 orders of magnitude in the conductivity, i.e., 1.06 x 10-10 S/m for IB, 2.16 x 10-11 S/m for IP, 2.5 x 10-3 S/m for CB, and 1.0 x 10-3 S/m for CP. This trend accords with results of polymer/CNT composites reported in the literature when a percolated network of CNTs is formed.20,61 The patterned samples (IP and CP) exhibited lower conductivity than the bare ones (IB and CB). When measuring electrical conductivity of the patterned samples, the measuring tip of the multimeter contacted only the top of the patterns. Therefore, the contact area of the measuring tip on the nanopatterned surfaces was much smaller than the contact surface of the bare sample. This resulted in a decrease in the current flow measured between the device and the sample as discussed in the reference.20 On the other hand, different phenomenon was found in the biometric experiments since the lipid layer of skin filled the gaps between the patterns. The lipid layers before and after touching a pad were identified by using an alpha-step profiler as shown in right-hand 8 ACS Paragon Plus Environment

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side of Figure 4(a). This lipid layer over the nanopattern can serve as an electrical impedance matching layer for electrical current transmission. The current flow can be directed to the normal direction of the patterns, avoiding loss at the interface between the two media. Figure 4(b) shows the experimental results for biometric sensing. Since the IB and IP samples were not conductive, they could not be used as a sensing pad. For the CB sample filled with CNTs, the measured current value was 169.3 (± 39.3) A. Furthermore, the CP sample with nanopattern arrays showed the current of 336.4 (± 30.3) A which is about twice as high as that of the CB sample. Several mechanisms can be considered to figure out these experimental results of nanopattern effects. The first mechanism is the enlarged specific surface area of the nanoscale patterns. This can significantly reduce the interfacial contact resistance between the two media. The second mechanism is the orientation of CNTs in the patterns. Although the orientation of CNTs cannot be observed clearly, the FIB test confirmed the presence of CNTs within the nanopatterns. The shear stress developed by the resin flow into nanoholes during the filling step could align the CNTs unidirectionally in the patterns.20 The third mechanism is the impedance matching phenomenon that causes the current to drift toward the patterns. This induces the efficient transmission of current to the device. All potential mechanisms could not be proved experimentally in this study. Furthermore, it was not clear which one is the major mechanism among them. The nanoscale phenomena, e.g., Moth’s eye effect, have been reported mainly in the theoretical perspective. It has failed to provide an experimental basis about the nanoeffect. Therefore, we performed numerical analysis and discussed the results in ‘Nanoelectronic modeling’ section, to present the plausible rationale and to support the basis of the results obtained by the experiments. The current measurement experiments support these mechanisms and show the capability of shape memory function. (Figure 4(c)). After deformation of the pattern, the current value was reduced to 67% of the original value of CP, i.e., 227.1 (± 36.9) A. The interfacial area between finger and the sample was greatly decreased. In addition, the directionality of pattern was reduced as the height decreased to about 55 nm, resulting in a decrease in the focusing ability of the pattern. The degree of CNT alignment in the nanopatterns was also reduced due to the lowered pattern height. The degraded current sensing capability could be recovered to the original level, i.e., 323.6 (± 28.7) A after gentle heating up to body temperature. Nanoelectronic modeling 9 ACS Paragon Plus Environment


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Numerical analysis was performed to verify that the improved current transfer was induced by the nanopatterned structure (Figure 5). In the simulation the impedance matching arising only from the nanopattern array was considered. To model the interface between finger and the touch sensor pad, three-dimensional unit cells were created (SI Figure S5). Detailed assumption and explanation about the simulation were provided in Supporting Information (SI) material. The current conservation equation was solved taking into account boundary conditions. Figure 5(a) shows the normalized current density ( , A/m2) and the current density field ( ), which are represented with color contour and white arrows, respectively. In the CB unit cell (Figure 5(a) (i)), both and are not distorted at the interface between the two domains. However, the current density field vectors focus on the nanopattern (Figures 5(a) (ii)). Figure 5(b) shows the electric charge density ( , C/m3) in the unit cell. It was found that an enormous amount of electric charge is accumulated nearby the top of the nanopattern in the CP unit cell. This nanoelectronic phenomenon allows electric current to pass relatively easily through the interface between the two media. This current concentration is an important mechanism that allows more current to be delivered with low loss. The volume fraction of the nanopattern is sufficiently small compared with that of the entire CP domain. Thus, the improved current transfer can arise from the nanopattern effect of the surface. When looking at the deformed CP unit cell (Figure 5(a) (iii)), the current field vector was directed toward the pattern. Quantitative comparison was made by calculating the current flows through unit cells ( ). The calculated current through the unit cell was obtained integrating the normalized current density ( , A/m2) over the bottom surface of each unit cell (SI Figure S6). The of the CP unit cell was 306.3 A. This theoretical value was in good agreement with the measured value (323.6 A). Also, the other values show good agreement with measured results; 169.6 A for the CB unit cell, and 233.6 A for the deformed CP unit cell. Although the orientation of CNTs and the interfacial resistance were not considered, the simulation results showed a fairly good agreement. The theoretical exploration demonstrated that nanoeffects of both CNTs and the nanopattern induced synergetic contribution to the enhancement of the current transfer for high-quality biometric information sensing.


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CONCLUSIONS In this study, we produced CNT embedded nanopattern arrays to generate the advanced biometric sensing system by using the synergetic nanoeffect. The presence of CNTs in the nanopattern was confirmed experimentally. The CNTs contributed to the enhancement of the mechanical strength of the patterns. By using SMCPAc as a smart material, the CNT embedded nanopattern was implemented to recover the original shape at body temperature. This performance could improve the sustainability of nanopatterns because the original shape could be recovered from the deformed state even at body temperature. The magnitude of the transferred current was increased twice due to nanocomposite patterns on the surface. The electrical impedance matching was understood through theoretical modeling based on nanoelectronics. The materials developed in this study can be applied to various advanced biometric devices transferring electric energy.

Supporting Information Characterization details (XPS, DMTA, etc.); Simulation methods and details; Synthetic scheme; XPS peak information

Acknowledgements This work was supported by the Industrial Strategic Technology Development Program (10052641) funded by the Ministry of Trade, Industry & Energy (MI, Korea). In addition, this research was supported by the Commercializations Promotion Agency for R&D Outcomes (COMPA) funded by the Ministry of Science, ICT and Future Planning (MISP). The authors are grateful for the supports.



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MATERIALS AND METHODS Materials Shape memory copolyacrylate (SMCPAc) was synthesized using a free radical polymerization method. The precursor consisted of two monomers (methyl methacrylate (MMA, DAEJUNG chem., Republic of Korea) and butyl methacrylate (BMA, DAEJUNG chem., Republic of Korea)), a crosslinker (polyethyleneglycol dimethacrylate (PEGDMA, Mw=525, Sigma Aldrich, USA)), and a thermal initiator (2,2-Azobisisobutyronitrile (AIBN, DAEJUNG chem., Republic of Korea)). The volume ratio of the mixed co-monomer (MMA:BMA=1:1) to the crosslinker was 1:2. A 0.2 wt% AIBN was added in the precursor. A 1 wt% multi-wall carbon nanotube (MWCNT, CM-95, Hanwha chem., Republic of Korea) with a 10 to 15 nm in diameter and a 2 m in length was incorporated with the precursor. The mixture was rigorously vortex-mixed for 0.5 hour, and then ultrasonic wave was applied for the dispersion of CNTs in the precursor. Fabrication of nanopattern arrays A silicon master with nanohole patterns of 230 nm in depth, 200 nm in diameter, and 200 nm distance between the nanoholes was fabricated using the e-beam lithography. A replica moulding

method

was

employed

to

prepare

nanopattern

array

samples.

Trichloro(1H,1H,2H,2H-perfluoro-octyl)silane (Sigma Aldrich, USA) was deposited on the surface of the master for easy separation. A mould for manufacturing the samples was built using 1 mm spacers and a slide glass. After filling the mould with the precursor resin, the mould was placed in a hot oven for polymerization. Residual monomers and crosslinkers were cleaned by rinsing the samples with isopropyl alcohol (IPA, DAEJUNG chem., Republic of Korea), and then the samples were dried. Characterizations Nanopattern array of the samples was observed using a field emission scanning electron microscope (FE-SEM, JSM-6390LV, JEOL, Japan) and an atomic force microscope (AFM, Park NX10, USA). A cross-section of the nanopatterns was prepared and observed by using a dual beam focused ion beam (DB-FIB, Helios NanoLab™, FEI, Netherlands) system. To avoid collapse of patterns, a Pt protection layer was deposited on the surface before splitting the patterns. The electrical conductivities of specimens were evaluated using a 2-point-probe method with a digital multi-meter (HiTESTER 3454-11, HIOKI E.E.Corporation, Japan). The current passing through the samples was measured by using a touch sensor circuit. The touch 12 ACS Paragon Plus Environment

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pad circuit consisted of a 12-channel self-calibration capacitive touch sensor (TSM12M, TOUCH-ON corp., Republic of Korea) and a digital multi-meter (Fluke 27/FM Military Digital Multimeter, FLUKE corp., USA).



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Figure 1. Fabrication scheme and characteristics of the CP sample. (a) Schematics of nanopatterning processes: (i) injection of resin mixture onto the master, (ii) polymerization by thermal heating, and (iii) demolding of nanocomposite pattern array. Confirmation of CNTs in the nanopattern with use of (b) XPS and (c) DB-FIB. (d) Macroscale and (e) nanoscale mechanical properties of the samples.


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Figure 2. Topological observation of the CP sample using SEM and AFM. The surface of the CP sample in (a) the original state, (b) the deformed state, and (c) the recovered state. (d) Side view of the composite pattern array. Morphology of (e) employed CNTs and (f) embedded CNTs in the matrix.



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Figure 3. Shape memory behavior of the samples. (a) Thermomechanics of the IP and CP samples. (b) Characterized SST curves of the CP sample. (c) Numerical simulation results of shape memory nanostructure; (i) the initial state, (ii) the deformed state after heating up to 40℃, (iii) the cooled state by 0℃, (iv) the unloaded state, and (ivi) the recovery of the shape of nanopatterns.


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Figure 4. Experimental results of biometric sensing. (a) Schematics of the circuit employed to measure current passing through the touch pad. The right-hand side figure shows alpha-step profiles before and after touching. (b) Measured and simulated signal currents for the IB, IP, CB, and CP samples. (c) Shape memory-recovery cycle of the patterns.



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Figure 5. Numerical simulation results of the impedance matching nanostructure. (a) The normalized current density fields and (b) the charge density fields of (i) the CB, (ii) the original CP, and (iii) the deformed CP samples.


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TOC/Abstract Graphics


Carbon Nanotube Embedded Nanostructure for Biometrics - ACS

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