The demand for flexible and wearable electronic devices is increasing due to their facile interaction with the human body. Flexible, stretchable and wearable sensors can be easily mounted on clothing or directly attached to the body. Especially, highly stretchable and sensitive strain sensors are needed for the human motion detection. Here, we report highly flexible, stretchable, sensitive strain sensors based on the nanocomposite of silver nanowire network and PDMS elastomer in the form of the sandwich structure (Ag nanowire thin film embedded between two layers of PDMS). Sandwich structure made the Ag nanowire network electromechanically robust due to the complete penetration of PDMS into the 3D network of the Ag nanowire thin film. The Ag nanowire
network-elastomer nanocomposite based strain sensors show strong piezoresistivity with tunable gauge factors in the ranges of 2 to 14 and a high stretchability up to 70%. Piezoresistivity of the strain sensor was further investigated by a computational model. Ag nanowires were randomly assigned into the PDMS matrix. Moreover, the connectivity of all pair nanowires was investigated by junction identification and total conductance of the network for different strains was calculated. We found an excellent agreement between our experimental and computational results. The mechanisms of piezoresistivity were explained with proposed computational model. We
found that piezoresistivity mechanism for strain sensors with high density Ag nanowires is the reversible disconnection
and connection between Ag nanowires by the applied strain. On the other hand, emergence of bottlenecks is the main mechanism of piezoresistivity in the strain sensors with low density Ag nanowires. Finally, we demonstrate the applicability of our high performance strain sensors by fabricating a glove integrated with five strain sensors for the motion detection of fingers and control of an avatar in the virtual environment.
Table of Contents
Abstract
Table of Contents
List of Figures
Chapter 1. Motivation
1.1 Motivation
1.2 Objective
1.2.1 Nanocomposite of metal nanowires and elastomers
1.3 Dissertation outline
Chapter 2. Research Background
2.1 Strain sensor
2.2 Capacitive type strain sensors
2.2.1 Applications of capacitive type strain sensors
2.3 Resistive type strain sensors
2.3.1 Carbon based strain sensors
2.3.2 Other nanomaterial based strain sensors
2.3.3 Application of resistive type strain sensors
2.4 Human motion detection
2.4.1 Video sensor based human motion detection (VSHMD)
2.4.2 Physical sensor based human motion detection (PSHMD)
2.4.3 Why wearable sensors and systems?
Chapter 3. Sample Preparation
3.1 Introduction
3.2 Why Ag nanowires as conductive nanomaterials?
3.3 Why PDMS as elastomer?
3.4 Synthesis of Ag nanowires
3.5 Sample fabrication
3.5.1 Deposition of Ag nanowire thin film
3.5.2 Preparation of simple structured samples
3.5.3 Preparation of sandwich structured samples
Chapter 4. Electromechanical Characterization
4.1 Introduction
4.2 Experimental setup
4.3 Electromechanical characteristics of the simple structured samples
4.4 Electromechanical characteristics of the sandwich structured samples
4.5 Why are the electromechanical behaviors of simple and sandwich structured samples different?
4.5.1 Wire-PDMS composite model
4.5.2 SEM image analysis
4.6 Sandwich structured strain sensors
4.7 Reliability tests and long-time stability
4.8 Environment effects (i.e. temperature and humidity)
4.8.1 Temperature effect
4.8.2 Humidity effect
4.9 Response time
Chapter 5. Numerical Studies on the Piezoresistivity
5.1 Introduction
5.2 Modeling of piezoresistivity
5.3 Nonlinearity and linearity
5.4 Fracture mechanisms of the Ag nanowire network
5.5 Alignment of nanowires
5.6 Size-dependence of piezoresistivity
Chapter 6. Human Motion Detection
6.1 Introduction
6.2 Human motion detection
6.3 Smart glove system
6.4 Integrated smart glove system
6.4.1 Costume made data acquisition (DAQ) system
6.4.2 Integration of smart glove with interface circuit
6.4.3 Demonstration of human motion detection and avatar control
Chapter 7. Conclusion and Future Remarks
7.1 Conclusion
7.2 Future remarks
List of Figures
Figure 1.1: Illustration of a remote health monitoring system based on wearable sensors[1]
Figure 2.1: Schematics of the capacitance based strain sensors: (top) the initial state, (bottom left) un-der strain, (bottom middle) under pressure[18]
Figure 2.2: a) Pressure sensing mechanism of capacitance based sensors, b) Relative change of the capacitance versus applied pressure[19]
Figure 2.3: Schematic illustration of touch panel, Step 1) Preparation of stretchable electrode arrays, Step 2) Positioning of two electrode arrays face to face and Step 3) Bonding two electrode by a dielectric layer[19]
Figure 2.4: Photograph of fabricated touch panel (left), resulted map of capacitance change by putting a “T” shape PDMS mold on the touch panel (right).
Figure 2.5: a) Schematic of capacitance based strain sensors, b) Relative change of capaci- tance against applied strain, c) Response of the sensor to dynamical stretch/release cycles[13]
Figure 2.6: Human motion detection of the capacitive type strain sensors, (a) One capacitance strain sensor mounted on a thumb joint. (b) Relative capacitance change and strain associated with thumb from starting the bending to backing to relaxed state. (c) Schematic of the patellar reflex experi- ment. (d) Relative capacitance change and strain caused by knee motion in patellar reflex. (e) Relative capacitance change and strain versus time for various human motions (e.g. walking, running and jump- ing from squatting)[18].
Figure 2.7: Schematic diagram showing the interconnection and spacing change of carbon nanotubes when PDMS-CNTs nanocomposite is exposed to tensile strain[12]
Figure 2.8: Fracturing mechanism of CNT thin film deposited on PDMS. a-e) Images of the SWCNT film on initial loading; crack propagation on the surface of the thin film by the elongation. Scale bar, 100 µm. f) SEM image of the fractural structure of the SWCNT film at 100% strain. Scale bar, 5 µm. Inset: three-dimensional image at 100% strain. g) Low-resolution SEM image of homogeneous fracturing of the SWCNT film. Scale bar, 50 µm[9].
Figure 2.9: Modeling of percolation through graphene flake network under strain. (a) Repre- sentation of voltage drop at fixed current in a graphene film at different levels of strain. (b) Re- sistance−strain curve for different graphene flake number density. (inset) Gauge factor GF as a function of unstrained resistance R0[15]
Figure 2.10: Stress and relative resistance change as a function of strain for samples with var- ious filler contents
Figure 2.11: Resistance change of a nanocomposite sample under a cyclic loading. It shows the direction of resistance change in the stretching/relaxing phases of testing with a maximal strain of 42.2%[23]
Figure 2.12: A long-term goal of the implantable strain sensor with wireless sensing capabil- ity[30]
Figure 2.13: Test setup of the SWNT based strain sensor to measure dynamic strain[33]
Figure 2.14: a,d,f) Photographs of a bandage strain sensor (a), a strain sensor fixed to a stock- ing (d) and adata glove (f). Inset to a: Photograph of the sensor adhered to the throat. Inset to d: close-up of the device. b,c,e,g) Relative changes in resistance versus time for breathing, phonation (speech), knee motion and data glove configurations, respectively[9].
Figure 2.15: The schematic diagram of the CNT array based strain sensor. b) Curves of weight vs. current of the CNT array sensor upon loading and unloading cycles[16].
Figure 2.16: General activity recognition system architecture[38]
Figure 2.17: Global representations approach, a person is localized first using background tracking[7]
Figure 2.18: Local representations, some interest points are detected first and then proceed to whole of body[7]
Figure 2.19: The bottom row ((f)-(j)) shows sample silhouette frames. The raw image corre- sponding to each silhouette is shown on the top row ((a)-(e))[39].
Figure 2.20: Results for 20 hours long road surveillance video. Usual events consist of cars moving along the road. Correctly detected unusual events include: (A) cars pulling off the road, (B) cars stopping and backing up, (C) car making U-turns, and people walking on the road[40]
Figure 2.21: A typical kitchen setup. The user is manipulating a waterjug (red rectangle) while wearing an RFID bracelet (blue rectangle). Some objects have RFID tags attached (green rectan- gle)[42]
Figure 2.22: Human activity recognition using binary sensors which were installed in home[43]
Figure 2.23: Left: User wearing sensors on wrist, hip and thigh. Right: The sensor platform, consisting of the power supply (bottom), the BSN node for logging (middle) and the sensor board (top) [46]
Figure 2.24: Complex systems under development will soon provide enhanced monitoring ca- pability, the ability to facilitate clinical interventions, and features that are suitable for detecting emer- gency situations, assess needs and alert a remote clinical center when necessary[46]
Figure 2.25: The interface for the EEG-based BCI archery game[53]
Figure 3.1: a) Ag nanowire solution with concentration of 12 mg/ml. b) Ag nanowire samples under light heating
Figure 3.2: a) Fabrication process of the simple structured sample. b) SEM image of the sur- face of simple structured sample; all Ag nanowires are embedded onto the surface of PDMS. c) Cross- sectional SEM image of simple structured sample; liquid PDMS penetrated into the porous network of bare Ag nanowires and made Ag nanowires-PDMS nanocomposite after annealing. d) A photograph of the fabricated simple structured sample.
Figure 3.3: The fabrication processes and result of the sandwich structured PDMS/Ag nan- owire/PDMS nanocomposite sample: a) Fabrication process of the sandwich structured sample. b) Pho- tographs of the sandwich structured sample before and after 100% stretching. c) Photographs of the sandwich structured sample under bending and twisting. d) Optical microscope images on top and cross- section of the sandwich structured sample.
Figure 3.4: a) Cross-sectional SEM of the sandwiched structured sample. b) SEM image with higher resolution.
Figure 4.1: Experimental setup for the electromechanical tests; inset, attached sandwiched structured sample on the moving stage by epoxy glue
Figure 4.2: a) Response of the simple structured sample under 10% of stretch/release cycles. b) Hysteresis curves for the simple structured sample
Figure 4.3: a) Response of the sandwich structured strain sensor under 70% stretch/release cycles. b) Hysteresis curve for the sandwich structured strain sensor
Figure 4.4: a) Highly cross-linked Ag NW-PDMS nanocomposite on the PDMS layer. b) Permanent deformation and surface instability of the nanocomposite layer during stretching/releasing cycle. c) Buckling and fracture of Ag NWs due to compressive strain in the transverse direction of stretch and friction force in the longitudinal direction of stretch.
Figure 4.5: Schematics for the behavior of the simple (a) and sandwich (b) structured samples under cyclic stretch/release cycles, respectively; inset, SEM image on the surface of the simple struc- tured sample before applying the strain and after releasing it from stretching; wrinkle patterns emerge on the surface
Figure 4.6: Wire-PDMS Composite Model: a) Fabricated simple and sandwich structured samples by using PDMS as medium and Cu wires as fillers. b) Random orientation of wires in PDMS matrix. c and d) The morphology of wires’ orientation in the case of simple structured sample before and under stretch at the same spot. e) Image of wire tip which partially slide back to its position after releas- ing. f) Ripped PDMS due to out-of-plane buckling of wire. g, h and j) The morphology of wires’ orien- tation in the case of sandwich structured sample before, under and after stretch at the same spot. k) Ori- entation of wires after releasing in the case of sandwich structured sample; all wires slide back to their original locations.
Figure 4.7: a and b) SEM images on the surface of simple structured sample before stretch and after releasing from strain. c) Buckled fractured and detached NWs in the case of simple structure sample after releasing it from 50%of stretch. d) Wrinkle patterns on the surface of nanocomposite layer after releasing from stretch
Figure 4.8: Electromechanical response of the sandwich structured AgNWs-PDMS nanocom- posite strain sensors: a) Current-voltage curves of the strain sensor for different levels of strains. b) Relative change of resistance vs. strain for the sensors with different levels of initial resistance··· 44
Figure 4.9: a) Cyclic test for low strain level (Ɛ=10%). b) Performance of a strain sensor to high level strains (from 10 to 40%)
Figure 4.10: a) Assembly of the strain sensor inside a convection oven, b) Temperature sensi- tivity of the strain
Figure 4.11: Relative change of the resistance for the both simple and sandwich structured samples against the immersion time; upset, Photograph of the sandwich structured strain sensor im- mersed in water.
Figure 4.12: a) Ramp strain applied to the strain sensor. b) Experimental data from a strain sensor, best fitted curve and ideal response.
Figure 5.1: Computational model of the Ag nanowire network in the PDMS matrix for numer- ical simulation of piezoresistivity of the Ag nanowires-PDMS nanocomposite: Randomly orientated Ag nanowires in the PDMS matrix
Figure 5.2: Coordinates of single nanowire in the 3D space
Figure 5.3: Different electrical interconnections between two adjacent nanowires: (i) complete ohmic connection with zero contact resistance, (ii) tunneling current between neighboring nanowires and (iii) complete disconnection of nanowires
Figure 5.4: 3D fiber reorientation model: a) Orientation and position of neighboring nan- owires before mechanical strain. b) Re-orientation and re-position of neighboring nanowires after me- chanical strain
Figure 5.5: a) Response of the Ag nanowires-PDMS nanocomposite to the applied strain by experimental measurement and numerical simulation. b) The number of non-current flowing nanowires and tunneling junctions against the applied strain
Figure 5.6: The morphology of the Ag nanowire network: a) Current flowing (yellow) and non-current flowing nanowires (blue) before stretching. b) Current flowing (yellow) and non-current flowing nanowires (blue) after 100% of stretching
Figure 5.7: a) Piezoresistive response for high and low resistance strain sensors-both simula- tion and experiment. b) Decrease of the current flowing nanowires for high and low resistance strain sensors. c) Top projected view of the Ag nanowire network at different level of strain for a high re- sistance strain sensor with bottleneck location. d) Top projected view of the Ag nanowire network at different level of strain for a low resistance strain sensor
Figure 5.8: Relative change of the resistance for the nanocomposite thin film till electrical fracture-both experiment and simulation
Figure 5.9: Topology of the nanocomposite thin film under different levels of strain: a) Bot- tlenecks emerges under high strain causing higher electrical resistance of the thin film. b) Histogram of orientations of Ag nanowires with respect to the y-axis under various strains
Figure 5.10: a and b) SEM images on the surface of the simple structured sample for 0% and 50% of stretch. c and d) The morphology of wires’ orientation before and after 40% of stretch at the same spot. e and f) Topological changes of the simulated thin film under 0% and 50% of stretch
Figure 5.11: Relative change of the resistance versus strain for the different aspect ratios.
Figure 6.1: Bendability test of the strain sensor by attaching it on a bended PET carrier
Figure 6.2: Wrist bending measurement by mounting the strain sensor on the wrist joint
Figure 6.3: Bending angel measurement using an artificial finger: a) Response of Ag nan- owires-PDMS nanocomposite strain sensor to the bending angles from 0° to 120°; inset, photograph of the artificial finger. b) Response of the strain sensor under repeated bending/relaxation cycles (10°-90°)
Figure 6.4: Gaming using hand gesture recognition[89].
Figure 6.5: User interface with real robot[84]
Figure 6.6: Overall architecture of interface circuit
Figure 6.7: Programmable amplifier based on the binary weighted resistor model.
Figure 6.8: Electrical circuit for the subtractor.
Figure 6.9: Offset calibration of a strain sensor.
Figure 6.10: Photographs of (a) DAQ system and (b) receiver.
Figure 6.11: Overall specifications of designed interface circuit.
Figure 6.12: Integrated smart glove system.
Figure 6.13: a) Motions detection of index and middle fingers. b) Control of avatar fingers in the virtual environment using smart glove system
ABSTRACT
The demand for flexible and wearable electronic devices is increasing due to their facile interaction with the hu- man body. Flexible, stretchable and wearable sensors can be easily mounted on clothing or directly attached to the body. Especially, highly stretchable and sensitive strain sensors are needed for the human motion detection. Here, we report highly flexible, stretchable, sensitive strain sensors based on the nanocomposite of silver nan- owire network and PDMS elastomer in the form of the sandwich structure (Ag nanowire thin film embedded between two layers of PDMS). Sandwich structure made the Ag nanowire network electromechanically robust due to the complete penetration of PDMS into the 3D network of the Ag nanowire thin film. The Ag nanowire network-elastomer nanocomposite based strain sensors show strong piezoresistivity with tunable gauge factors in the ranges of 2 to 14 and a high stretchability up to 70%. Piezoresistivity of the strain sensor was further investi- gated by a computational model. Ag nanowires were randomly assigned into the PDMS matrix. Moreover, the connectivity of all pair nanowires was investigated by junction identification and total conductance of the net- work for different strains was calculated. We found an excellent agreement between our experimental and com- putational results. The mechanisms of piezoresistivity were explained with proposed computational model. We found that piezoresistivity mechanism for strain sensors with high density Ag nanowires is the reversible discon- nection and connection between Ag nanowires by the applied strain. On the other hand, emergence of bottle- necks is the main mechanism of piezoresistivity in the strain sensors with low density Ag nanowires. Finally, we demonstrate the applicability of our high performance strain sensors by fabricating a glove integrated with five strain sensors for the motion detection of fingers and control of an avatar in the virtual environment.
Keywords: Stretchable strain sensor, silver nanowire, nanocomposite, piezoresistivity and human motion detec- tion.
Chapter 1. Motivation
1.1 Motivation
The demand for flexible and wearable electronic devices is increasing due to their facile interaction with the human body. Flexible, stretchable and wearable sensors can be easily mounted on clothing or directly attached to the body. Especially, wearable sensors have drawn lots of attentions for the human motion detec- tion. Detection of the human body can be utilized in different applications such as in biomedicine[1], bio- robotics and surgery machines[2], robotics[3], avatar control [2, 4] and entertainments [5, 6]. As an example, a conceptual representation of a system for personal health monitoring using wearable sensors is shown in Figure 1.1[1]. Wearable sensors are used to collect human motion data for patient’s status monitoring. Hu- man motion data would be used in applications such as monitoring the effectiveness of home-based rehabilita- tion interventions in stroke survivors or the use of mobility assistive devices in older adults. Patient’s data can be transmitted to a mobile phone or an access point through wireless communication and then the information could be sent to a remote center via the Internet. Emergency situations (e.g. falls) could be detected by data processing and then an alarm message could be sent to an emergency service center to provide immediate assistance to patients. Clinical personnel can remotely monitor patient’s status and be alerted in case of medi- cal decision has to be made.
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Figure 1.1: Illustration of a remote health monitoring system based on the wearable sensors[1].
1.2 Objectives
As mentioned above, there are many potential applications for the human motion detection. Different approaches were pursued for the human body detection. For example, vision based techniques (i.e. using set of cameras and image processing) were introduced to detect the human movements[7]. However, cost, space requirement, very complex image processing and low resolution to small movements are among serious drawbacks of these methods preventing their applications in the real-life. Another approach is using wearable sensors as sensing elements. Sensors can be mounted on the human body and the response from sensors can be analyzed based on the human body motion. For instance, acceleration sensors (or accelerometers) are used to sense acceleration caused by the change in body position. Operational principle of accelerometer is based on an element named proof mass that attached to a suspension system with respect to a reference point and when force is applied on proof mass, deflection is produced in it. Deflections can be measured electrically to sense the changes in body location. Accelerometers are most commonly used sensors to monitor the human motion of persons who recently recovered from brain disease[8]. However, accelerometry based motion de- tection is expensive and it might not be accurate way of detection.
Stretchable strain sensors could be a promising candidate for accurate and fast detection of the human body movements. Even though very large strains (ε>50%) should be accommodated by the strain sensors for the human motion detection, commercially available metal-foils or semiconductor based strain gauges exhibit a poor stretchability (maximum~5%). Thus they are not suitable for the human motion detection [9-10]. Moreover, highly flexible, stretchable and sensitive strain sensors are required for the human motion detection. Several alternatives are proposed to fabricate flexible strain sensors using MEMS technology and nanomaterials. For example, stretchable strain sensors were achieved by the thin film deposition of carbon nanotubes (CNTs) or graphene sheets on the flexible substrates [9-10]. However, the performances of those strain sensors are still limited for the real-time motion detection of the human body.
1.2.1 Nanocomposite of metal nanowires and elastomers
Metal nanowires such as silver (Ag) nanowires show excellent electrical, mechanical, thermal and op- tical properties[11]. But these nanomaterials cannot be used in the flexible applications because of their rigid structure. On the other hand, elastomer materials such as polydimethylsiloxane (PDMS) possess very low conductivity with high stretchability (ε>100%)[12]. Therefore, the superior electrical properties of metal nan- owires and excellent stretchability of elastomers can be combined together in the form of nanocomposites made of elastomers as matrix and nanowires as fillers. Nanowires-elastomer nanocomposites are soft and flex- ible and they can withstand to higher strains with acceptable mechanical and electrical properties. For exam- ple, Ag nanowires-PDMS nanocomposite was utilized as a highly stretchable conductor[13]. The nanocomposite thin film was conductive under high strain (ε>50%) and it was used in the flexible circuit applications. Flexible and stretchable strain sensors based on the metal nanowires-elastomer nanocomposite is not investigated in depth largely due to irreversible change of the resistance to the cyclic loading and well as very difficult dispersion of metal nanowires inside the elastomer medium.
In summary, there are many applications for the human motion detection, in particular, personal health monitoring, biomechanics, physiology and kinesiology applications[14]. However, complexity, cost, space requirement and low resolution are among the main limitations of previously developed techniques. Highly flexible, stretchable and sensitive strain sensors can be a promising candidate for the human motion detection. High accuracy, human-friendly and low cost of fabrication could be the advantages of the flexible strain sen- sors. We believe that such a high performance strain sensor can be achieved by using Ag nanowire-PDMS nanocomposite due to superior electrical and mechanical properties of Ag nanowires and high stretchability of PDMS.
1.3 Dissertation outline
This dissertation contains seven chapters. In chapter 1, we present the motivation and objectives of this research, and introduce the significances of the human motion detection and its applications. Metal nanowires- elastomer nanocomposites are introduced as stretchable conductors and are proposed as highly flexible, stretchable and sensitive strain sensors. In chapter 2, previous and current research related to the flexible strain sensors have been explored and their application to human motion detection, different techniques for human motion detection and limitations of current sensors for practical usage are exploited. In chapter 3, we describe material selection and experimental procedures for preparation of the Ag nanowires-polymer nanocomposite. Various dispersion agents and/or approaches are used to homogenously suspend Ag nanowires in solvents. Different conditions are examined to achieve very uniform Ag nanowire networks on glass slide. Conductive Ag nanowire-PDMS nanocomposites are successfully fabricated by transferring the Ag nanowire network from glass slide to polymer medium by filtration of the liquid PDMS into the Ag nanowire network. Finally, electrical characteristics of the nanocomposite under repeated cyclic loading are investigated. In chapter 4, we characterize the performances of the Ag nanowire-PDMS nanocomposite based strain sensors by various elec- tromechanical tests such as static and dynamic loadings, stretchability test, reliability test, environmental ef- fect test, response time test and etc. In chapter 5, we demonstrate a computational model developed to study piezoresistivity of the Ag nanowire-PDMS nanocomposite strain sensor. Prosperities of the strain sensors are investigated in different conditions and the mechanisms of piezoresistivity are explored. In chapter 6, we in- troduce the design and fabrication of multifunctional interface circuit and integrated glove system for the hu- man motion detection. Data acquisition (DAQ), calibration of sensors and data transmission though a wireless module are combined in a chip integrated on a glove system. In chapter 7, we summarize our results and pro- pose future works.
Chapter 2. Research background
2.1 Strain sensor
Strain sensor responds to the mechanical deformations by the change of electrical characteristics such as resistance or capacitance. Many requirements are needed to make high performance strain sensors includ- ing sensitivity or gauge factor (GF), stretchability, response speed, stability, fabrication cost and simplicity. Even though conventional strain sensors require low cost of fabrication, they typically have poor stretchability and sensitivity (maximum strain of 5% and GF~2) [9, 10, 15-17]. Recently, there have been numerous efforts to develop flexible, stretchable and sensitive strain sensors by using MEMS technology, nanostructures and nanomaterials. Strain sensors based on their operation principals can be categorized into capacitive type strain sensors and resistive type strain sensors.
2.2 Capacitive type strain sensors
Capacitive type strain sensors transduce the mechanical deformations to the change of capacitance. The strain sensors contain two stretchable electrodes laminated with an inner dielectric layer, as shown in Figure 2.1[18].
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Figure 2.1: Schematics of the capacitive type strain sensors: (top) the initial state, (bottom left) under strain, (bottom middle) under pressure[18].
When two ends of the capacitors stretched by the applied strain, change of the capacitance is caused by the change of the dielectric layer and contact area. In this case, the capacitors can be used as a strain sensor. Moreover, capacitors can be used for other application such as pressure sensing and touch sensing as well (different configurations are illustrated in Figure 2.1).
Assume that two electrodes are overlapped with the length of l0 width of w0 and distance of d0 (i.e. thickness of the dielectric layer). The initial capacitance is given by:
illustration not visible in this excerpt
where ε0 and εt are the electric and dielectric constants for the dielectric layer, respectively. If the sensor is uniaxially stretch with ε strain, increase of the length (in direction of the stretching) is[illustration not visible in this excerpt], decrease of the width is (1-Electrodeε)w0 and decrease of the thickness of the dielectric layer is [illustration not visible in this excerpt]. As a results, the capacitance of the capacitor under strain is estimated to be:
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For most of the polymers, the value for the is 0.5. So the capacitance can be simplified to:
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The sensitivity of the capacitive type strain sensors are defined as relative change of the capacitance divided by the applied strain and given by:
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Based on the calculations, the sensitivity (GF) for the capacitive type strain sensors should be 1. However, experimental values are always below 1. That could be due to the network structure of electrodes (i.e. all surface of the electrodes are not covered with conductive materials). For example, Lipomi et al. re- ported capacitive type strain sensors made of the sprayed CNT thin film on PDMS as electrodes. The GFs of 0.4 with stretchability of 30% was achieved[19]. Capacitance based strain sensors made of patterned Ag nanowire network on the PDMS substrate as stretchable electrode layers exhibit the GFs of 0.7 with stretchability of 50%[18]. Moreover, strain sensors with GF of 0.2 and stretchability of 20% were demonstrated by Feng et al. using PDMS embedded Ag nanowire network as electrode layers[13].
2.2.1 Applications of capacitive type strain sensors
Capacitive type sensors are mainly applied for the pressure or touch detection. Recently capacitance based strain sensors are used for the human motion detection. When a force or finger is pressed on the sensor, the dielectric thickness decreases, thereby causing the value of capacitance to increase, see Figure 2.2a. So change of the capacitance is a function of force or pressure. These interesting properties are utilized in artifi- cial skins and touch panel arrays. Figure 2.2 shows the pressure sensing characteristics of a capacitive type pressure sensor[19]. The capacitance increased linearly with the applied pressure, as depicted in Figure 2.2b. Pressure measurement range depends on properties of the dielectric material, thickness of the dielectric layer and initial capacitance between top and bottom electrodes. For example, CNTs/PDMS based capacitance pressure sensors can measure pressure up to 0.9 MPa[19]or Ag nanowires/PDMS based capacitance sensors are able to detect pressure for up to 1.2 MPa[18].
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Figure 2.2: a) Pressure sensing mechanism of capacitance based pressure sensors, b) Relative change of the capacitance versus applied pressure[19].
Figure 2.3 schematically illustrates the fabrication process for touch panel arrays. Arrays of stretchable electrodes are first patterned on a flexible substrate and then two arrays are positioned face to face and finally two electrodes are bonded with a dielectric layer. Capacitive type pressure sensors are achieved where two electrode arrays are overlapped in a point. Position and amount of applied pressure are determined by arrays’ identification and performance of the capacitance sensors, respectively.
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Figure 2.3: Schematic illustration of touch panel, step (1) Prepration of stretchable electrode arrays, step (2) Positioning of two electrode arrays face to face and step (3) Bonding two electrode by a dielectric layer[19].
Figure 2.4 shows a touch panel assembled by the patterned Ag nanowire arrays as electrodes and Ecoflex layer as dielectric layer[18]. As the figure illustrates, touch panel exhibits a good sensitivity to a “T” shape PDMS mold put on the surface of the touch panel.
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Figure 2.4: Photograph of fabricated touch panel (left), resutled map of capacitance change by putting a “T” shape PDMS mold on the touch panel (right)[18].
As mentioned above, capacitive type sensors are commonly used in the pressure and touch sensing ap- plications. As another application, capacitive type sensors were used as stretchable strain sensors for the hu- man motion detection. Moreover, stretching changes the overlap area and thickness of the dielectric layer causing an increase in the capacitance of the sensor, see Figure 2.5a. Capacitive type strain sensors can be mounted on the human body for the body motion detection. However, they may not provide an accurate measurement because of unpredicted response from sensors as well as capacitance interaction with the human body. As shown in Figure 2.5b, the capacitance of the sensor linearly increases by applied strain. Linearity and very low hysteresis are among advantages of the capacitive type strain sensors[18]. The response of the sensor to the dynamical loading/unloading cycles is illustrated in Figure 2.5c. The capacitance of the sensor increases by the applied strain and decreases upon unloading.
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Figure 2.5: a) Schematic of the capacitance type strain sensors, b) Relative change of capacitance against applied strain, c) Response of the sensor to dynamical stretch/release cycles[13].
Figure 2.6a shows the strain sensor mounted onto a thumb. The skin of the phalange was under a slight tension strain, leading to a slight increase of capacitance. When the thumb was straightened, it actually relaxed and the strain decreased, accompanied by a decrease in capacitance. Then the person changed the gesture to downward, and the thumb joints experienced increase of stretching. As a result, the sensor showed large increase in capacitance (e.g., 25% that corresponds to 36% tensile strain). From the capacitance measurement, the skin strain in thumb flexure can be obtained. The measured strain signals could be used as a feedback for robotics or prosthetic devices to realize better human control[18].
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Figure 2.6: Human motion detection of the capacitive type strain sensors, (a) One capacitance strain sensor mounted on a thumb joint. (b) Relative capacitance change and strain associated with thumb from starting the bending to backing to relaxed state. (c) Schematic of the patellar reflex experiment. (d) Relative capacitance change and strain caused by knee motion in patellar reflex. (e) Relative capacitance change and strain versus time for various human motions (e.g. walking, running and jumping from squatting)[18].
Patellar reflex or knee-jerk is very useful in early diagnoses of nervous system diseases. The lack of knee-jerk, known as Westphal's sign, could be a symptom of diseases such as tabes dorsalis and receptor dam- age. As shown in Figure 2.6c, patellar reflex was monitored using capacitive type strain sensors. A sensor was mounted to the knee while the person was sitting with the lower leg relaxed naturally. Initially the sensor accommodated a large tensile strain across the knee. To test the patellar reflex, the patellar tendon ligament was tapped by a hammer. As a response, the lower leg should straighten involuntarily in a sudden kicking motion, as illustrated in Figure 2.6c, and then come to rest quickly. It can be seen that upon tapping, a sudden decrease in capacitance was observed, corresponding to the release of the capacitive sensor (from the tension state) as a result of the quick kicking movement. With the relaxation of the knee, the capacitance returned to the initial value. Figure 2.6d represents the capacitance change for a normal knee jerk, without the sign of absent or pendular knee-jerk. The amplitude and duration of the kicking motion in response to a given tapping could provide valuable information for early diagnoses of nervous system diseases[18]. By mounting the sen- sors on the knee, the response from strain sensors can also be used to detect other human motions, such as walking, running and jumping from squatting, as shown in Figure 2.6e.
In summary, capacitive type strain sensors are linear, stretchable and flexible strain sensors. The per- formances of the sensor are relied on the parallel overlap of two electrodes, bonding between electrodes and dielectric and electromechanical properties of both electrodes and dielectric layers. They are commonly used in pressure and touch detection applications. In addition, the capacitive type strain sensors are used for the human motion detection owning to their stretchability and flexibility. However, capacitance based strain sen- sors may not be used an accurate way of the human motion detection due to the capacitive interaction with the human body and some unpredicted responses from strain sensors due to the unstable overlaps of the capacitive area between two electrodes.
2.3 Resistive type strain sensors
Resistive type strain sensors transduce the mechanical deformation by the change of resistance. Sensitivity or gauge factor (GF) of strain sensors is given by:
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where R0 presents initial resistance of the sensor, ε is the applied strain and ∆R is resistance change of the sensor in the initial state and after mechanical deformation. Different principals and operation mechanisms are involved in the resistive type strain sensors. For instance, operation of conventional metal strain gauges relies on the geometrical changes (i.e. elongation in the longitudinal direction of stretching and shrinkage in the transvers direction of stretching). Elongation and shrinkage of sensors change the cross-area and length of the conduction path causing the resistance to increase. These types of strain gauges commonly offer GFs of 2-5 with stretchability of 5%. Other semi-conductor based strain gauges such as silicon based strain gauges, ex- ploit the piezoelectric characters of the materials and show the GF of larger than 100[15]. Even though both mentioned strain gauges have well-establish technologies and low cost of fabrication, limitations such as ri- gidity, very low stretchability (maximum of 5%) and bio-incompatibility of these strain gauges have restricted them in certain applications like human motion detection and biomedical strain measurements[12].
In the past few years, several novel piezoresistive mechanisms were pursued with different groups us- ing nanomaterials, nanotechnology and MEMS technology. CNTs-elastomer nanocomposites were fabricated by uniform dispersion of CNTs into the elastomer medium for the strain sensing applications. The nanocom- posite film could be conductive by either physical connection of neighboring CNTs or an effect known as “tunneling current” (i.e. if the distances between two neighboring CNTs are smaller than nanometer, 3 nm, electron can pass through polymer and make a quantum junction)[12]. When the nanocomposite film is stretched out, CNTs are disconnected due to elongation of the nanocomposite thin film, causing electrical dis- connection between some adjacent CNTs and increase of the sheet resistance consequently. Similarly, when the nanocomposite film is released, CNTs are repositioned to their initial state leading to resistance recovery. The piezoresistivity mechanism for the CNTs-elastomer nanocomposite strain sensors are schematically illus- trated in Figure 2.7. This mechanism is widely applied to CNTs based nanocomposite for various strain sens- ing applications.
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Figure 2.7: Schematic diagram showing the repositioning and reorientation of CNTs when PDMSCNTs nanocomposite is exposed to a tensile strain[12].
Nano-scale crack propagation is another approach of piezoresistivity. Thin films of nanomaterials are deposited on the surface of the flexible substrates. During the elongation of the flexible substrates, nano-scale cracks propagate on the surface of the thin film due to its brittleness. The length and gaps between cracks depend on the level of strain. Crack propagation reduces the electrical conduction passes throughout the thin film causing its resistance to increase. The facture mechanisms and crack propagation of aligned CNTs thin film on the surface of PDMS subjected to uniaxial loading is illustrated in Figure 2.8.
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Figure 2.8: |Fracturing mechanism of the CNT thin film deposited on PDMS. a-e) Images of the SWCNT film on initial loading; crack propagation on the surface of the thin film by the elongation. Scale bar, 100 µm. f) SEM image of the fractural structure of the SWCNT film at 100% strain. Scale bar, 5 µm. Inset: three-dimensional image at 100% strain. g) Low-resolution SEM image of homogeneous fracturing of the SWCNT film. Scale bar, 50 µm[9].
Another mechanism of piezoresistivity is disconnection between conductive materials in the percolation network. For example, when 2D graphene flakes are deposited on the surface of flexible substrates, current can pass through overlapped flakes in the percolation network. Under strain, 2D flake are separated causing decrease of the overlapped flakes and electrical pathways. Disconnection piezoresistive mechanism is schematically is depicted in Figure 2.9.
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Figure 2.9: Modeling of percolation through graphene flake network under strain. a) Representation of voltage drop at fixed current in a graphene film at different levels of strain. b) Resistance−strain curve for different graphene flake densities. (inset) GF as a function of initial resistance R0[15].
Reviewing of the previous literatures reveals that strain sensors based in the carbon nanomaterials (i.e. carbon black, CNT and graphene) have been investigated by many research groups. On the other hand, only a few groups investigated the piezoresistivity of other materials such as Ag nanoparticles and ZnO nanowires. That could be due to relatively simple and low cost of fabrication for carbon based nanomaterials and well-known methods for preparation of carbon nanomaterials/flexible substrate composites such as dispersion[12], lithography[12], spray coating[15], alignment[9]and etc. Herein, all strain sensors are classified into the carbon based strain sensors and other materials based strain sensors. The advantages and disadvantages of each type of strain sensors are explained in the next section with details.
2.3.1 Carbon based strain sensors
Recently, several alternatives have been pursued to achieve novel strain sensors by using nanomateri- als. Among them, carbon nanomaterial based sensors have shown outstanding performances due to their supe- rior mechanical and electrical properties. Highly sensitive strain sensors have been reported by using graphene sheets on the flexible substrates [10, 15, 20-21]. However, graphene based strain sensors show low stretcha- bility (ε<10%) due to the brittleness of the graphene sheets. So they are not appropriate for the human motion detection platform where a large strain (ε>50%) should be accommodated by the strain sensor. On the other hand, highly stretchable strain sensors were demonstrated by assembling the carbon nanotube (CNT) thin films on the flexible substrates [9, 22]. But these strain sensors suffered from low GFs, nonlinearity and large hysteresis. Very large nonlinearity and hysteresis of CNT based strain sensors are illustrated in Figure 2.10 and Figure 2.10, respectively. Large hysteresis in the CNTs-elastomer based strain sensors are largely due to the interaction between individual CNT and polymer medium. Friction between CNTs and polymer (e.g. caus- ing delay in the resistance recovery), stress-strain rate and strain history or time are main factors affecting the hysteresis response of CNT based strain sensors[23]. In overall, despite of numerous efforts to develop high performance strain sensors based on carbon nanomaterials with desirable properties (i.e. high GFs and lineari- ty coupled with a high stretchability) [9, 10, 12, 14, 15-17, 24-27], most previously reported strain sensors have only demonstrated high sensitivity coupled with relatively low stretchability and vice versa.
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Figure 2.10: Stress and relative resistance change as a function of strain for samples with various filler contents.
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Figure 2.11: Resistance change of a nanocomposite sample under a cyclic loading. It shows the direction of resistance change in the stretching/relaxing phases of testing with a maximal strain of 42.2%[23].
2.3.2 Other nanomaterials based strain sensors
Only a few research groups developed strain sensors using other type of nanomaterials. For example, stretchable and sensitive strain sensors were developed using Ag nanoparticle ink as a sensing material. Ag nanoparticle ink was transferred to the PDMS substrate using transfer/patterning technique. Stretchability of 5% with GFs of 12 was achieved using Ag nanoparticle based strain sensors. Very low stretchability is due to the large crack propagation throughout the thin film leading to electrically failure of the thin film in the lower stretch levels[28]. Highly stretchable and sensitive strain sensors were developed by growing the ZnO nanowire/polystyrene nanofiber (PSNF) hybrid structure on a PDMS film. Strain sensors with stretchability of 50% coupled with high GFs of 116 have been demonstrated[29].
2.3.3 Applications of resistive type strain sensors
Resistive type strain sensors are applicable in various applications such as rehabilitation and personal health monitoring [30-32], structural health monitoring [33-34], human motion detection [9, 29] and mass measurement [16, 36]. Herein, a brief description is given for each application.
- Rehabilitation and personal health monitoring: Measurement of biomechanical strain within hu- man body is an important parameter for many biomedical applications. For instance, post-surgical ther- apies are needed in the case of bone fracture treatment to monitor the treatment during the healing stage. In fact, approximately 10% of bone fractures still do not heal properly due to abnormal strain profiles during the healing process[30]. So it could be beneficial if the strain sensor could be attached to the fractured bone, during surgery, for continues strain monitoring after surgery, the schematic is illustrated in Figure 2.12. In addition, strain measurement data could also be useful in the studying of other medical conditions such as osteoporosis, bone tumors, as well as prosthetic implants. However, conventional strain gauges are rigid and bio-incompatible. As a result, flexible, biocompatible and sensitive strain sensors are needed to be developed for this specific application.
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Figure 2.12: A long-term goal of the implantable strain sensor with wireless sensing capability[30].
- Structural health monitoring (SHM): Infrastructures, such as highways, buildings, bridges, air- craft, ships, and pipelines are sometimes under severe loading conditions due to uneven events such as earthquakes, hurricanes, and other natural disasters during their lifetime which lead to damage, failure or facture of the structures. To prevent such catastrophic and uneven failures and subsequent loss of life, it is necessary to continuously monitor the state of the structures in real time by using SHM techniques, in par- ticular, by using strain sensing. SHM provides an autonomous way to track the structural changes in real- time using a combination of instrumentation systems and analytical methods. Among the quantities of in- terest for SHM, strain sensor is a local and direct measurement way of the state of the structures and is thus widely used as a reliable device for damage detection in the structures. Hence, strain sensors are used extensively in SHM applications. CNT based strain sensors are widely used in SHM application for con- tinues monitoring of static and dynamic loading profiles. For example, strain sensors are attached to a cantilever beam which is stimulated by external loads under certain frequencies. Strain profile of the can- tilever beam structure could be measured by strain sensors and is can be used for structure health evalua- tion. The experimental setup for SHM using CNT based strain sensors are shown in Figure 2.13[33]. High sensitive strain sensors with acceptable stretchability are desirable for SHM applications.
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Figure 2.13: Test setup of the CNT based strain sensor for SHM[33].
- Human motion detection: Human motion detection is a promising application of highly stretcha- ble and sensitive strain sensors. Stretchable strain sensors can be attached to or bounded onto the human body for strain measurement. Figure 2.14 shows human motion detection (e.g. throat motion detection, knee motion detection and smart glove for motion detection of fingers) using stretchable strain sensors based on CNTs/PDMS composite[9]. Human motion detection can be useful in biomedical and healthcare/personal health monitoring[1], sport performance monitoring [35, 36], human motion captur- ing for entertainment fields (e.g. motion capture for games and animation) [14, 29, 37] and input for ro- botics and telerobotic devices [2, 3].
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Figure 2.14: a,d,f) Photographs of a bandage strain sensor (a), a strain sensor fixed to a stocking (d) and adata glove (f). Inset to a: Photograph of the sensor adhered to the throat. Inset to d: close-up of the device. b,c,e,g) Relative changes in resistance versus time for breathing, phonation (speech), knee motion and data glove configurations, respectively[9].
- Mass measurement: Deformation of a cantilever beam by external force or weight can be meas- ured using highly sensitive strain sensors. The response from strain sensors can be calibrated with corresponding weight. An experiment procedure for weight measurement using CNT array based strain sensors is illustrated in Figure 2.15a. Figure 2.15b shows the response from strain sensor to different level of force with a good linearity with negligible hysteresis[16].
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Figure 2.15: a) The schematic diagram of the CNT array based strain sensor. b) Curves of weight vs. current of the CNT array sensor upon loading and unloading cycles[16].
2.4 Human motion detection
Understanding human activities or behaviors has long been a goal of humans. In our daily lives, we are always curious the activities of people around us. For example, in home environment, it can remind users to do some missed activates or incomplete actions (e.g. taking medicine), help them recall information, or en- courage them to act safely. In the hospital environment, it can remind a doctor or nurse to perform certain tests before operating. There exist different approaches to know human activity information. Employing a person to monitor other person’s activity seems to be one simple way, but constant monitoring is not realistic. Therefore, automatic recognition of human activities by computers is important and necessary[38].
To build an automatic activity recognition system, as illustrated in Figure 2.16, sensors are needed to able system to feel the state of physical activities. Then, the sensed data will be processed by machine learning techniques to detect the human activity (e.g. sitting, raising hand, or raising foot).
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Figure 2.16: General activity recognition system architecture[38].
According to the sensing techniques, human motion detection can be roughly divided into two categories: (i) Video sensor based human motion detection (VSHMD) and (ii) Physical sensor based human motion detection (PSHMD).
2.4.1 Video sensor based human motion detection (VSHMD)
This method detects the motions by set of cameras placed in different locations. Three major steps involved in VSHMD method include: (i) Input video or sequence of image, (ii) Extraction features from images and (iii) Activity recognition based on the extracted features.
(i) Feature extraction: Feature extraction is the most important and challengeable part for the activity recognition. Extracting good feature is the promise of good recognition performance[38]. The features that are extracted from the image sequences can be classified into two categories: global representations and local representations.
- Global representations: Global representations are a top-down fashion so that a person is local- ized first in the image using background subtraction or tracking, as shown in in Figure 2.17. Then, the region of interest is encoded as a whole, which results in the image descriptor. The representations are powerful since they encode much of the information. However, they rely on accurate localization, back- ground subtraction or tracking. Also, they are more sensitive to viewpoint, noise and occlusions[7].
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Figure 2.17: Global representations approach, a person is localized first using background tracking[7].
- Local representations: As demonstrated in Figure 2.18, local representations describe the feature as a collection of independent patches. So the local representations are a bottom-up fashion in which spatio-temporal interest points are detected first, and then local patches are calculated around these points. Finally, the patches are combined into a final representation. After initial success of bag-of-feature approaches, there is currently more focus on correlations between patches. Local representations are less sensitive to noise and partial occlusion, and do not strictly require background subtraction or tracking. However, as they depend on the extraction of enough amounts of relevant interest points, pre-processing is sometimes needed, for example to compensate for camera movements[7].
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Figure 2.18: Local representations, some interest points are detected first and then proceed to whole of body[7].
2.4.1.1 Applications of VSHMD
- Behavioral biometric: Recognizing humans based on physical or behavioral cues is the mission of behavioral biometric. Conventional approaches generally use physical attributes (e.g. fingerprint, iris) for recognition. Recently behavioral biometrics has become popular because behavior is regarded as useful as human’s physical attributes for human recognition. This approach does not require human’s corporation. Human gait is the typical example of behavioral biometric. Figure 2.19 shows the human motion’s samples and their corresponding frames for biometric application[39].
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Figure 2.19: The bottom row ((f)-(j)) shows sample silhouette frames. The raw image corresponding to each silhouette is shown on the top row ((a)-(e))[39].
- Content based video analysis: Summarization and retrieval videos based on human activity related content (such as sport) is one of the most popular applications of VSHMD.
- Security and surveillance: Traditionally security and surveillance systems need a human to monitor the camera and be aware of the activity. However, in the environment with many cameras, it is hard for human operators to constantly monitor considering both efficiency and accuracy. Therefore, video based activity recognition systems are required to replace or assist the human operator to monitor anomalies and interested activities. H. Zhong et al. used video based activity recognition for detecting unusual activity in video which was taken across a road[40].
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Figure 2.20: Results for 20 hours long road surveillance video. Usual events consist of cars moving along the road. Correctly detected unusual events include: (A) cars pulling off the road, (B) cars stopping and backing up, (C) car making U-turns, and people walking on the road[40].
- Interactive applications: Interactive applications such as game need understanding the interaction with human’s gesture and activities are particular useful cues which can help computers better interact with human and enable interactive games. Another application is smart room, which can response to a us- er’s gesture by using video based activity recognition method. For example, A. Pentland developed com- puter systems that can follow people's actions, recognizing their faces, gestures, and expressions based on vision based tracking systems. Using this technology “smart rooms" and “smart clothes" can be made [41].
2.4.2 Physical sensor based human motion detection (PSHMD):
With the quick development of physical sensor techniques, PSHMD has become the other activity recognition method. In PSHMD, based on the location where the physical sensors are attached, it can be further divided into two categories:
- Wearable sensor based activity recognition (WSAR): In this method, physical sensors are at- tached to the body of human. This method is relatively new approach for the human activity detection. They are attached to relevant parts of the body to provide information about human motions. Other sen- sors which are usually used as complement of motion sensors include microphone, GPS and light sensors. They provide additional information about user’s environment to help improve the accuracy of activity recognition.
- Object usage based activity recognition (OUAR): This kind of PSHMD attaches sensors into ob- ject. With this design, system can know which object is accessed by the user and what is happening to it. Although OUAR does not explicitly monitor human activity like WSAR, it still works because many ac- tivities can be implicitly inferred by knowing the objects that have been utilized by human. For example, if we know the stove is on, we might infer the human is cooking. Radio Frequency Identification (RFID) based sensor and binary sensor are commonly used sensors in OUAR.
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Figure 2.21: A typical kitchen setup. The user is manipulating a waterjug (red rectangle) while wearing an RFID bracelet (blue rectangle). Some objects have RFID tags attached (green rectangle)[42].
RFID based sensor needs RFID tags and RFID reader. An RFID based sensor will generate object use events when a tagged object is manipulated during an activity. For example, tagged can be attached to an appliance (e.g. kettle) and human need wear the short ranged RFID reader (e.g. it can be worn on the user’s wrist), as shown in Figure 2.21. Whenever the user’s hand is close proximity to tagged object, then the reader indicates[42]. The other type of sensor which has received wide spread acceptance is binary sensor. Binary sensors are usually simple and anonymous. Whenever the sate of a certain context (object, movement) associated with a binary sensor is changed the value of the sensor changes to “1” from “0” when it is in a static state. Figure 2.22 demonstrate binary sensor which are installed in home environment. E. M. Tapia et al. used these sensors for the human activity recognition in the home environment[43].
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Figure 2.22: Human activity recognition using binary sensors which were installed in home[43].
2.4.3 Why wearable sensors and systems?
The motivation for the development of wearable sensors and systems is due to the tremendous benefits that could be associated with long-term monitoring of individuals in the home and community[44]. For ex- ample, advancements in wearable sensors and wireless technologies create huge impact on healthcare moni- toring system. Now we have facilities to monitor patients from remote location on continuous basis by using wearable sensors and wireless systems[45]. Different types of sensors are available for specific applications including:
A) Accelerometer:
Acceleration sensors or motion detection sensors are used to sense acceleration (i.e. change in body position), this acceleration might be linear or angular. Operational principle of accelerometer is based on an element named proof mass that attached to a suspension system with respect to a reference point and when force applied on proof mass, deflection is produced in it. Produced deflection can be measured electrically to sense changes in body location. Accelerometers are most commonly used sensors to monitor physical activities of persons who recently recovered from brain disease. As an example, activity recognition by accelerometers at multiple body locations was done by Huynh et al., see Figure 2.23[46].
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Figure 2.23: Left: User wearing sensors on wrist, hip and thigh. Right: The sensor platform, consisting of the power supply (bottom), the BSN node for logging (middle) and the sensor board (top)[46].
B) Electromagnetic tracking system (ETS) sensor:
ETS is a body position measurement sensor based on Faraday’s law of magnetic induction[42]. When a person or object that carry a sensor consists of coils perform a motion inside a controlled magnetic field, the induced voltage in sensor coils will change by the change of the objects position and orientation relative to source of controlled magnetic field. This controlled magnetic field is generated by a fixed transmitter and de- tected by a receiver fixed on an object. By using this phenomena position and orientation of moving object can be calculated[43]. ETS is an important sensor in gait analysis and in study of the body kinematics.
C) Wearable strain sensors:
Wearable strain sensors are newly developed types of sensors which can be used in many applications. Principle of these sensors is based on piezoresistivity. Whenever external strain is applied to the sensor its resistance or capacitance changes. Therefore, the amount of the applied strain can be calculated be measuring the signal changes. Wearable strain sensors are stretchable and highly flexible. These skin-like sensors can be easily implemented on the different parts of the body for the human activity recognition (e.g. on joints for angel measurement, in chest for breath rate measurement). T. Yamada at el. employed CNT based wearable strain sensor for the human motion detection in different locations (e.g. throat, chest, and waist)[9].
2.4.3.1 Applications of wearable sensors:
In the following section applications of wearable sensors for the activity recognition systems or mobile settings are presented in details.
- Healthcare and assisted living: A major goal of current research in activity recognition and context- aware computing is to explore new health-related applications and technologies for the aging. Longer life expectancy and declining fertility rates are increasing the proportion of the elderly population in societies worldwide and posing challenges to existing healthcare systems. It is hoped that technology can help in these challenges, for instance by helping elderly people to live more independent lives and thus reducing the burden of care-givers.
One type of system designed for elderly people aims to detect potentially dangerous situations in a per- son’s life in order to call for external help automatically. For example, R. Jafari et al. proposed a method- ology to determine the falling from among other common human movements. The source data was col- lected by wearable and mobile platforms based on three-axis accelerometers to measure subject kinemat- ics, see Figure 2.24[47].
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Figure 2.24: Human activity detection using wearable sensors and processing the data; detecting emergency situations, assess needs and alert a remote clinical center when necessary[46].
Preventing age-related diseases or severe medical conditions before they actually happen is the goal of another class of applications, which employ long-term monitoring to detect changes or unusual patterns in a person’s daily life that may indicate early symptoms of diseases such as Alzheimer’s. While automatic detection of subtle behavioral changes is highly challenging and still a long-term goal of current research. A third type of health-related system aims to use context-information to promote a more active and thus healthy lifestyle, or to actively support elderly or disabled people in performing everyday activities. Maitlandet et al. used fluctuations in mobile phone signals to estimate and summarize a person’s activity levels in order to motivate and encourage reflection on daily activities[48].
- Industrial applications: In mobile industry, activity-aware applications have the potential to support workers in their tasks, help to avoid mistakes and increase work-place safety. For instance, wearable plat- forms supporting workers in tasks such as communication, access to information, or data collection, have been commercially available since the early 1990s from companies such as Xybernaut. Ac-cording to an overview by [Stanford 2002], early adopters of such (costly) systems were companies in which mobile knowledge workers construct, maintain and repair technically complex and costly systems such as ships, airliners and telecommunication networks[49].
Currently several research groups explore the next generation of industrial applications which, among other improvements, take better advantage of the multimodal sensing capabilities of wearable platforms by inferring context information such as the user’ current activity. For instance, Lukowiczet et al. investi- gated the use of wearable computing technology for scenarios in aircraft maintenance, car production, hospital environments and emergency response. In these cases, wearable technology and activity recogni- tion are used to provide interactive and hands-free access to information such as electronic manuals or pa- tient records, assist in training of new workers, provide summaries of performed activities, as well as to help in navigation and communication[50].
- Entertainment and games: Wearable systems using activity recognition are appealing for applications in the performing arts (e.g. by allowing dancers to augment their performance with interactive multimedia content that matches their motions). Two examples of gaming applications are the system described by Zhang and Hartmann, in which a motion-sensing clamp attached to the body or other objects is used to control video-games, or the system by Heinz et al., in which wearable inertial sensors are used to recognize moves to control martial arts games [51, 52]. In most recent research, Liaoet et al. used electroencephalographic (EEG) type wearable sensors as a brain-computer interface (BCI) communication system to control a game, as shown in Figure 2.25[53].
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Figure 2.25: The interface for the EEG-based BCI archery game[53].
Chapter 3. Sample Preparation
3.1 Introduction
This chapter presents material preparation and sample fabrication processes. Ag nanowires are synthe- sized by a solution process method. Simple drop-casting method is employed to deposit the Ag nanowire net- works on glass slides. Different types of solvents (e.g. methanol, ethanol, IPA, acetone etc.) are used for Ag nanowire dispersion. We found that in addition to type of solvent, type of heating source is an important pa- rameter for the uniform deposition of the Ag nanowire networks on the glass slides. Light heating is selected as a heating source. For comparison, two types of samples are fabricated: Ag nanowire thin film on PDMS substrate (Ag nanowires/PDMS) and sandwich structures with the Ag nanowire thin film between two layers of PDMS (PDMS/Ag nanowires/PDMS). Hereafter, we refer to these two different types of samples as “sim- ple” and “sandwich” structured samples, respectively. We found that simple structured samples cannot be uti- lized for strain sensing applications due to their irreversible change of resistance under repeated cyclic load- ing/unloading. However, sandwiched structured samples possess an excellent resistance recovery even to large strains (Ɛ>70%).
3.2 Why Ag nanowires as conductive nanomaterial?
Silver (Ag) nanowires, one-dimensional nanomaterial, have been widely used in flexible electronics due to their excellent electrical, optical and mechanical properties[11]. Mechanical strength of Ag nanowires is comparable with brittleness of graphene sheets and flexibility of CNTs. Uniform dispersion (necessary for the nanocomposite fabrication) of Ag nanowires in different solvent is much easier than that of CNTs (e.g. agglomeration and bundling of CNTs in all solvents). To this end, Ag nanowires are widely used in many applications. For example, they have been demonstrated in transparent and flexible devices [13, 54-61], solar cells [11, 62-63], and film heaters [64-65]. In spite of these promising results, Ag nanowires-based strain sen- sors have not been studied in depth largely because of the weak adhesion of Ag nanowires on flexible polymer substrates and surface buckling/wrinkling of the Ag nanowire thin film on the substrate leading to a perma- nent loss of contact between adjacent Ag nanowires [13, 55, 59, 61]. Under repeated strain/release cycles, the number of detached and buckled Ag nanowires increases, thereby causing the electrical resistance of film to increase irreversibly.
3.3 Why PDMS as elastomer?
To fabricate the nanocomposite thin films, highly stretchable elastomers are required to incorporate with Ag nanowires as fillers. Various type of polymeric materials such as poly(methyl methacrylate) (PMMA), polycarbonate (PC), poly(ethylene) (PE), poly(L-lactide) (PLLA) and poly(dimethylsiloxane) PDMS have been used for nanocomposite fabrication[12]. Among them PDMS is curable polymer with low viscosity and low surface energy which is necessary for penetration of the liquid PDMS into the Ag nanowire network to fabricate the Ag nanowires-PDMS nanocomposite[13]. PDMS owns superior mechanical elastici- ty and withstand to more than 100% of tensile strain. PDMS with these properties is quite suitable for the strain sensing applications. Its flexibility allows it to be easily attached to complex surface or directly mount- ed onto the human body, which is often necessary in biomedical sensors. Moreover, PDMS being a chemically inert and biocompatible material is widely used in microfluidics and biomedical areas[12].
3.4 Synthesis of Ag nanowire
Ag nanowires are synthesized by modified polyol method according to Korte et al.[58]. 50 ml of eth- ylene glycol was heated at 152 °C for 1 hour with a magnetic stirrer (stirring speed=260 rpm). 400 μl of 4 mM CuCl2 in ethylene glycol was added to the ethylene glycol that was heated beforehand. After heating the new solution for another 15 minutes, 15 ml of 0.147 M polyvinylpyrrolidone (PVP) in ethylene glycol was added to the system. Then, 15 ml of 0.094 M AgNO3 in ethylene glycol was injected drop by drop into the solution at the rate of 0.5 ml/min. After all of the AgNO3 solution was injected, the solution was heated for another 1.5 hours and quenched in a room temperature water bath to stop the reaction. After the Ag nanowire solution was cooled down, a large amount of acetone was added to the solution (with a ratio of 5:1). The solution was cen- trifuged at 5,000 rpm for 10 minutes and washed three times with ethanol to remove the excess PVP and eth- ylene glycol. Ag nanowires were suspendered in different solvent such as methanol, ethanol, acetone and iso- propyl alcohol (IPA). Ag nanowires in IPA gave us a uniform and stable suspension. So Ag nanowires were stored in IPA for further experiments. The average diameter and length of Ag nanowires were 150-200 nm and 10-20 μm, respectively.
3.5 Sample fabrication
3.5.1 Deposition of Ag nanowire thin film
Photograph of the synthesized Ag nanowire solution is illustrated in Figure 3.1a. Ag nanowire solu- tion with approximate 12 mg/ml concentration was cast onto the glass slides drop by drop; To evaporate the access IPA and deposit the Ag nanowire networks on the glass slides, glass slides were heated to 60 °C by hot plate and convection oven. We observed the none-uniformity of the Ag nanowire networks in both cases. That could be due to the localized and nonhomogeneous heating throughout the glass slides causing none-uniform evaporation of the Ag nanowire solution. Toward these objectives, light heating was used as a heating source. After drop-casting of the Ag nanowire solution, the glass slides were exposed to a lamp light (OSRAM DR 51 50W 12V with a luminous intensity of 1450 cd) to dry the Ag nanowire solution and deposit Ag nanowires onto the glass slide, as shown in Figure 3.1b. The light heating provided uniform and gradual heating throughout the deposited Ag nanowire thin film and made it more uniform and homogenous. Moreover, sam- ples with different sheet resistance were obtained by changing the concentration of the Ag nanowire solution as well as number of deposition.
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Figure 3.1: a) Ag nanowire solution with concentration of 12 mg/ml. b) Ag nanowire samples under light heating
3.5.2 Preparation of simple structured sample
Ag nanowire solution was first drop-cast onto a glass slide that was previously cleaned with acetone, ethanol, and DI water and patterned with a polyimide tape (with ~ 20×3 mm[2]rectangular pattern size). After drop-casting of Ag nanowire solution, the glass slide was exposed to a lamp light to dry the Ag nanowire solu- tion and deposit Ag nanowires onto the glass slide. After drying the solution, polyimide tape was removed from the glass slide and the patterned Ag nanowire thin film was thermally annealed at 200 ºC for 20 min to increase the electrical conductivity. The thermal annealing can reduce the resistance of the Ag nanowire thin film by removing the PVP surfactant and allowing the fusion between Ag nanowires [59, 61]. The simple structured samples were fabricated by casting the liquid PDMS with an approximate thickness of 0.5 mm on the pre-annealed AgNW thin film pattern and curing it at 70 ºC for 2 hours. After peeling-off the cured PDMS from the glass slide, it was flipped and copper wires were attached to the ends of the embedded Ag nanowire thin film by silver paste for further electromechanical tests. Fabrication process of the simple structured sam- ple is schematically illustrated in Figure 3.2a.
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Figure 3.2: a) Fabrication process of the simple structured sample. b) SEM image of the surface of simple structured sample; all Ag nanowires are embedded onto the surface of PDMS. c) Cross-sectional SEM image of simple structured sample; liquid PDMS penetrated into the porous network of bare Ag nanowires and made Ag nanowires-PDMS nanocomposite after annealing. d) A photograph of the fabricated simple structured sample.
When the liquid PDMS is cast onto the Ag nanowire film, the liquid PDMS penetrates into the inter- connected pores of the three-dimensional (3D) Ag nanowire network, owing to the low viscosity and low sur- face energy of the liquid PDMS. After curing the PDMS, Ag nanowires are buried below the surface of PDMS. Figure 3.2b shows the SEM image of Ag nanowires on the surface of PDMS. As the figure illustrates, all Ag nanowires are buried into the PDMS surface without any voids on the surface, showing a successful transfer of Ag nanowires from glass slide to the PDMS elastomer as well as good adhesion between Ag nan- owires and PDMS substrate. Figure 3.2c demonstrates the cross-sectional SEM images of simple structured sample. As the figure depicts, PDMS penetrated into the Ag nanowire thin film network and filled the gap between nanowires, forming a robust nanocomposite of Ag nanowires and PDMS. The penetration of PDMS enhanced the contact of neighboring Ag nanowires and made the total network mechanically robust. Figure 3.2d illustrates a photograph of the simple structured sample after peeling it off from glass slide.
3.5.3 Preparation of sandwich structured sample
Sandwich structured samples were prepared by drop-casting the Ag nanowire solution onto a glass slide that was previously cleaned with acetone, ethanol, and DI water and patterned with a polyimide tape (with ~ 20×3 mm2 rectangular pattern size). After drop-casting of Ag nanowire solution, the glass slide was exposed to a lamp light to dry the Ag nanowire solution and deposit Ag nanowires onto the glass slide. After drying the solution, polyimide tape was removed from the glass slide and the patterned Ag nanowire thin film was thermally annealed at 200 ºC for 20 min to increase the electrical conductivity. 0.5 mm layer of the liquid PDMS was cast onto the Ag nanowire thin film pattern on glass slide. After partially curing the PDMS layer at 70 ºC for 20 min, it was peeled-off and flipped. Then copper wires were attached to the two ends of the AgNW thin film by silver paste and another layer of the liquid PDMS with the same thickness (0.5 mm) was cast on the Ag nanowire embedded PDMS film and cured at 70 ºC for 2 hours. Here, the first PDMS layer was partially cured for a short period in order to minimize the mechanical property difference between first and second layers of PDMS since the mechanical characteristics of PDMS are highly dependent on the curing temperature and period[66].
Figure 3.3a depicts fabrication process of the proposed sandwich structured samples. Figure 3.3b and 3.3c show the fabricated sandwich structured samples with excellent flexibility, stretchability and bendability. As compared with most previously reported strain sensors fabricated by depositing or embedding the sensing materials on the flexible substrates in which the structure and performance of the strain sensors can be easily damaged even by touching [9, 15], our sandwich structured sample can be easily handled with a high reliabil- ity by complete encapsulation. They can be directly mounted on the skin and easily attached to complex sur- faces without any damage to the nanocomposite thin film. The top and cross-sectional optical images of the samples are shown in Figure 3.3d indicating well-patterned Ag nanowires-PDMS nanocomposite thin film with an average thickness of 5 μm.
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Figure 3.3: The fabrication processes and result of the sandwich structured PDMS/Ag nanowire/PDMS nanocomposite sample: a) Fabrication process of the sandwich structured sample. b) Photographs of the sandwich structured sample before and after 100% stretching. c) Photographs of the sandwich struc- tured sample under bending and twisting. d) Optical microscope images on top and cross-section of the sandwich structured sample.
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Figure 3.4: a) Cross-sectional SEM of the sandwiched structured sample. b) SEM image with higher resolution.
Figure 3.4 demonstrates the cross-sectional SEM image of the sandwich structured samples. As the figures depict, the liquid PDMS completely penetrated into the Ag nanowire network thin film in two sides and filled the gaps between NWs, forming a robust nanocomposite of Ag nanowires and PDMS.
Chapter 4. Electromechanical Characterization
4.1 Introduction
Chapter 4 presents different electromechanical tests such as static and dynamic loading/unloading cycling test, hysteresis tests, bending test, reliability test, environment effect tests and response time test on the simple and sandwich structured samples. We found that the resistance of the simple structured sample changes to the cyclic loading/unloading irreversibility. On the other hand, resistance of the sandwich structured samples fully recovers to its original value upon unloading. The phenomenon is further investigated by SEM analysis and optical microscope observations. Irreversible change of the resistance in the case of the simple structured sample could be due to the fracture and buckling/wrinkling of Ag nanowires in the Ag nanowiresPDMS nanocomposite thin film. Moreover, buckling/wrinkling of Ag nanowires in the case of the sandwich structured sample is prevented by the integrated and symmetrical structure. Finally, strain sensing performances of the sandwich structured strain sensors are investigated.
4.2 Experimental setup
Figure 4.1 shows the experimental setup for electromechanical tests contain computer, potentiometer and motorized moving stage. To test the strain sensing characteristics, two ends of the samples were clamped to a motorized moving stage (Future Sciemnce Motion Controller, FS100801A1P1) by an epoxy glue and then uniform strain/release cycles were applied to the samples while the current changes were measured using a potentiometer (CH Instruments, Electrochemical Workstation, CHI901D), see Figure 4.1. Hysteresis meas- urements for all samples were recorded at a displacement rate of 0.55 mm/s. For bending test, samples were attached on the bended polyethylene terephthalate (PET) carrier substrate whose two ends were hinge- connected to the motorized moving stage. For the characterization of the bending angle detection by the strain sensors, an artificial finger was developed. The strain sensor was attached to the joint of the artificial finger and the response of the strain sensors under different bending angles (from 0º to 120º) was investigated.
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Figure 4.1: Experimental setup for the electromechanical tests; inset, attached sandwiched structured sample on the moving stage by epoxy glue.
All microscopic optical and scanning electron microscope (SEM) images were taken by the MX-6RT (i-solution lite optical microscopes, ITM technology) and field-emission scanning electron microscope (FESEM) (Sirion, the Netherlands), respectively.
A smart glove made of five sandwich structured Ag nanowires-PDMS nanocomposite sensors, one sensor for each finger, was fabricated for the human fingers’ motion recognition. The smart glove is integrated with a costume-made data acquisition (DAQ) system with wireless communication on a chip. Moreover, de- signed chip is a multifunctional circuit which acquires data from sensors, calibrates the response of each sen- sor and transmits all the data to a computer by wireless communication system (Zigbee Module). The chip acquires the resistance changes based on a constant current (50 µA) which provides a long-time operation per battery charge (8.5 hours). The avatar hand was developed in LabVIEW (National Instruments) and was con- nected to the integrated glove systems.
4.3 Electromechanical characteristics of the simple structured samples
The response of the simple structured sample under 10% of dynamic loading/unloading is demonstrated in Figure 4.2a. The current of the sample did not return back to its original value even after the sample was completely released, indicating an irreversible increase in the number of disconnected Ag nanowires. The sudden drop of current in the first stretch/release cycle indicates the buckling and fracture of Ag nanowires on the PDMS layer leading to a permanent loss of Ag nanowires’ contact as well as detachment of some Ag nanowires from the PDMS surface due to out-of-plane buckling of nanowires.
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Figure 4.2: a) Response of the simple structured sample under 10% of stretch/release cycles. b) Hysteresis curves for the simple structured sample.
Figure 4.2b illustrates the hysteresis curve for simple structured samples. As the figure shows, the rel- ative change of resistance against the strain in the release cycle does not overlap with that in the stretch cycle for the simple structured sample, indicating a large hysteresis in the response. The resistance of simple struc- tured sample increased more than 100% in the first stretch/release cycle showing the irreversible change of resistance in the cyclic loading. Electromechanical behavior (i.e. irreversible change of resistance with a con- siderable hysteresis) of the simple structured sample is similar to that in the Ag nanowire based electrodes [61]and CNTs-PDMS nanocomposite[27].
4.4 Electromechanical characteristics of the sandwich structured samples
The response of the sandwich structured sample under dynamic load is demonstrated in Figure 4.3.a. The resistance was fully recovered for stretch/release cycles with maximum strain of ε=70%, showing the outstanding stretchability of our sandwich structured samples. Figure 4.3b illustrates the hysteresis curve for the sandwich structured sample. As shown in the figure, there is no hysteresis in the response of the sandwich structured strain sensor for over 40% of stretch/release cycles. The hysteresis performance of our sandwiched structured sample is much better than large hysteresis in the CNT based strain sensors [9, 22, 26]. For larger strain such as ε=60%, there exists hysteresis in the response due to the considerable hysteresis of PDMS[67].
However, even in this case, the original resistance of the sensor is fully recovered after releasing it from strain.
The excellent resistance recovery, linearity and negligible hysteresis by the sandwiched structure could be due to the structural robustness and integrity, reduction of buckling and fracture of Ag nanowires as well as very good adhesion between Ag nanowires and PDMS layers by the complete penetration of PDMS into the 3D Ag nanowire network from both sides.
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Figure 4.3: a) Response of the sandwich structured strain sensor under 70% stretch/release cycles. b) Hysteresis curve for the sandwich structured strain sensor.
4.5 Why are the electromechanical behaviors between simple and sandwich structured samples different?
The physical phenomenon behind the electromechanical difference between the simple and sandwich structured samples can be explained by the interactions between PDMS matrix and Ag nanowire fillers. Due to much lower Young’s modulus of the PDMS matrix (0.4-3.5 MPa) compared with that of Ag nanowires (81- 176 GPa) [66, 68-70], Ag nanowires can be regarded as rigid elements during the stretch/release cycles. In addition, mechanical properties of the Ag nanowires-PDMS nanocomposite are different with that in pure PDMS. The elastic modulus of the PDMS layers was considered to be 1.64 MPa, and that of the AgNWsPDMS nanocomposite layer was predicted by the Halpin-Tsai Equation[71].
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Here, lf and df are the length (20 μm) and the diameter (150 nm) of Ag nanowires, φ is the volume fraction of Ag nanowires in the PDMS matrix, Em is the shear modulus of the matrix and is given by:
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Where Ef is the elastic modulus of bulk Ag. The calculated elastic modulus for the Ag nanowires-PDMS nanocomposite with a volume fraction of φ=5% is 6.32 MPa, which is 3.85 times larger than that of the PDMS matrix.
In the simple structured sample, the stiff nanocomposite thin film is highly cross-linked with the com- pliant PDMS layer, see Figure 4.4a. During stretching cycle, the nanocomposite thin film is under compres- sive stress in the transverse direction of stretching causing Ag nanowires to detach and buckle out of plane, permanently, as shown in Figure 4.4b and 4.4c. Moreover, in longitudinal direction of stretch, there are gaps between two ends of AgNWs and elongated PDMS matrix due to much larger deformation of PDMS, see Figure 4.4c. Ideally, all the nanowires should slide back to their initial positions after releasing. However, deformation of the nanocomposite thin film increases the friction force between nanowires and the PDMS matrix so that nanowires are buckled above a certain threshold friction force. As a result, Ag nanowires slide back by certain degree but cannot fully return to their initial positions. This causes some Ag nanowires to be buckled out of plane since residual stress exists in the nanocomposite layer upon unloading[13]. Furthermore, in a bilayer system such as the simple structured sample with the stiff Ag nanowires-PDMS nanocomposite layer on the compliant PDMS substrate, spontaneous wrinkle patterns emerge to release the compressive strain caused by mechanical instability [72, 73], as shown in Figure 4.5a. Buckling and facture of Ag nan- owires in the simple structured samples decrease the number of electrical pathways and therefore the electrical resistance of the Ag nanowire network film increases irreversibly. However, in the case of the sandwich struc- tured samples, the behavior of the nanocomposite layer is mechanically stable due to its symmetrical and inte- grated structure, enabling the nanowires to follow back by their defined paths without buckling. Therefore, the change of positions and orientations of nanowires under stretch/release cycles for the sandwich structured sample can be based on an affine transformation[74]. The behaviors of the simple and sandwich structured samples under stretch/release cycles are schematically illustrated in the Figure 4.5a and 4.5b, respectively.
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Figure 4.4: a) Highly cross-linked Ag NW-PDMS nanocomposite on the PDMS layer. b) Permanent deformation and surface instability of the nanocomposite layer during stretching/releasing cycle. c) Buckling and fracture of Ag NWs due to compressive strain in the transverse direction of stretch and friction force in the longitudinal direction of stretch.
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Figure 4.5: Schematics for the behavior of the simple (a) and sandwich (b) structured samples under cyclic stretch/release cycles, respectively; inset, SEM image on the surface of the simple structured sample before applying the strain and after releasing it from stretching; wrinkle patterns emerge on the surface.
4.5.1 Wire-PDMS composite model
Since the Ag nanowires-PDMS nanocomposite layer is covered by two layers of PDMS in the sand- wich structured sample, it was impossible to take the in-situ SEM images on the surface of nanocomposite to monitor the morphology changes under strain. Toward these objectives, we prepared both simple and sand- wich structured samples with a PDMS medium and copper (Cu) wires (average diameter=150 µm and length= 2 mm) as filler elements, as shown in Figure 4.6a. By using these samples, the movement of wires can be easily monitored with an optical microscope. Stretch/release cycles were then applied to samples while the morphological changes were recorded by an optical microspore. Figure 4.6b shows the random orientation of wires in the PDMS matrix. Figure 4.6c and 4.6d illustrate the morphological change of wires in the case of simple structured sample by the 40% of stretching. There are gaps between wires’ tip and PDMS matrix due to much larger elongation of PDMS. Although the wires should slide back to their initial positions after releas- ing, the friction force between PDMS and wires prevent the position recovery[13]. As Figure 4.6e shows, the wires could not completely return to their original positions and gaps between the wire tips and PDMS remain after strain release. These gaps cause compressive stress to wires inducing out-of-plane buckling and fracture under cyclic loads. As Figure 4.6f depicts, many wires were ripped-off from PDMS after releasing the simple structured sample from strain, indicating out-of-plane buckling of wires. However, in the case of sandwich structured sample, the structural integrity of the PDMS-wire composite is preserved. Figure panels 4.6g, h and j illustrate the morphology of wires in the case of sandwich structured sample before, under and after stretch (Ɛ=40%) at the same spot, respectively. Under stretch, gap exists between two ends of wires and PDMS matrix; but, all wires completely followed back by their paths without any buckling and fracture, as indicated in Figure 4.6k. Fully re-positioning and -re-orientation of Ag nanowires in the PDMS matrix as well as considerable reduction of buckling are reasons for the resistance recovery of the sandwich structured sample.
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Figure 4.6: Wire-PDMS Composite Model: a) Fabricated simple and sandwich structured samples by using PDMS as medium and Cu wires as fillers. b) Random orientation of wires in PDMS matrix. c and d) The morphology of wires’ orientation in the case of simple structured sample before and under stretch at the same spot. e) Image of wire tip which partially slide back to its position after releasing. f) Ripped PDMS due to out-of-plane buckling of wire. g, h and j) The morphology of wires’ orientation in the case of sandwich structured sample before, under and after stretch at the same spot. k) Orientation of wires after releasing in the case of sandwich structured sample; all wires slide back to their original locations.
4.5.2 SEM image analysis
We observed similar phenomenon by taking the SEM images on the surface of simple structured sam- ple before strain applying and after releasing it from stretching. Figure 4.7a show the SEM image on the sur- face of nanocomposite layer for the simple structured sample. As the figure depicts, all nanowires are just buried below the flat surface of PDMS; however, after releasing the sample from strain, wrinkled patterns emerged on the surface of nanocomposite layer indicating out-of-plane buckling of nanowires, as illustrated in Figure 4.7b. Figure 4.7c shows high resolution SEM image on the surface of simple structured sample after releasing it from stretching, illustrating the bucked, detached and fractured nanowires in the nanocomposite layer due to the permanent surface deformation of the PDMS. Figure 4.7d shows the cross-sectional SEM image of the simple structured sample. As the figure shows, there are wavy shape deformations of the PDMS causing the wrinkle patterns on the surface of sample.
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Figure 4.7: a and b) SEM images on the surface of simple structured sample before stretch and after re- leasing from strain. c) Buckled fractured and detached NWs in the case of simple structure sample after releasing it from 50%of stretch. d) Wrinkle patterns on the surface of nanocomposite layer after releas- ing from stretch.
4.6 Sandwich structured strain sensors
As the experimental results show, the sandwich structured samples can be utilized as high performance strain sensors due to their excellent stretchability with resistance recovery, linearity and negligible hysteresis. Figure 4.8a shows the current-voltage characteristics of a sandwich structured strain sensor under different strains. The sensor exhibits an ohmic behavior regardless of applied strains and the current monotonically decreases by the increase of the tensile strain. Strain sensors with different initial resistances and GFs can be prepared by controlling the concentration of the Ag nanowire solution and number of deposition. The re- sponses of the sensors with different initial resistances are illustrated in Figure 4.8b. When the Ag nanowire solution with a concentration of 6 g/L was deposited only once, the resistance of the strain sensor was rela- tively large (R0~246 Ω) due to the sparse network of fewer Ag nanowires whereas the GF is larger (GF~14). In contrast, much denser network of Ag nanowires is formed and the initial resistance is dramatically reduced (R0~7.5 Ω) by dual deposition of the 12 g/L Ag nanowire solution. This causes a reduced GF=2 with high linearity of R[2]=0.986. The linearity of our strain sensors is much better than those of previously reported gra- phene and CNT based strain sensors [10, 20, 22, 26] since the piezoresistivity of our strain sensors are not due to the fracture or crack propagation of the sensing materials. Moreover, Ag nanowires slide by the defor- mation of the PDMS matrix so that the number of disconnected Ag nanowires increases by gradual increase of the strain causing the resistance of the strain sensor to increase. Interestingly, the sensitivity, linearity and stretchability of the strain sensors can be tuned by adjusting the concentration of the Ag nanowire solution and deposition parameters to the need of individual applications. The strain sensors with high initial resistance are appropriate for high GF with low strain applications. On the other hand, for very high strain applications with acceptable GF, strain sensors with low resistance can be utilized. As one example, we could fabricate strain sensors with GF~5, linearity of R[2]=0.94 and stretchability of 60%. In comparison with conventional strain sensors (GF~2 with maximum stretchability of 5% and linear response), CNT/polymer composite (GF~0.82 with stretchability of 40% and linear response)[9], graphene/polymer composite (GF>1000 with stretchability of 5% and nonlinear response)[10],1 ZnO NWs/polymer composite (GF~116 with stretchability of 30% and linear response)[29]and carbon black/polymer composite (GF~20 with stretchability of 80% and nonlinear response)[75], our strain sensors provide excellent sensitivity, stretchability and linearity simultaneously. Furthermore, to the best of our knowledge, our strain sensors are the only reported flexible and stretchable strain sensors based on Ag nanowires to date.
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Figure 4.8: Electromechanical response of the sandwich structured AgNWs-PDMS nanocomposite strain sensors: a) Current-voltage curves of the strain sensor for different levels of strains. b) Relative change of resistance vs. strain for the sensors with different levels of initial resistance.
4.7 Reliability tests and long-time stability:
We tested the performance of the strain sensors to the cyclic loading with both low and high strain levels. The response of a strain sensor to cyclic loading from 0 to 10% of strain for more than 225 cycles is illustrated in Figure 4.9a. As the figure depicts, the strain sensor responds to the cyclic loading with a good stability and reproducibility. Furthermore, we investigated performance of the strain sensors to higher strain levels (ε=10 % to 40 %) for more than 1000 cycles. The electrical current of strain sensor gradually was de- creased by cyclic loading as shown in Figure 4.9b. The minimum resistance of the strain sensor after cyclic test was increased by ~ 6.25 %. We believe that this degradation is mainly caused by the fatigue of the PDMS substrate rather than the delamination of Ag nanowires from PDMS substrate since they are completely cov- ered by PDMS layers in the top and bottom surfaces. In order to overcome this problem, we plan to replace the PDMS matrix with more stretchable polymer materials.
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Figure 4.9: a) Cyclic test for low strain level (Ɛ=10%). b) Performance of a strain sensor to high level strains (from 10 to 40%).
4.8 Environment effects (i.e. temperature and humidity)
4.8.1 Temperature effect
Obviously all metal and semi-conductor materials are sensitive to temperature. However, the linear re- sistance-temperature dependency of the sensors is desirable since the effect can be easily compensated by calibrating the sensors with a Wheatstone bridge or temperature sensor. In the case of polymer nanocompo- sites, temperature sensitivity is caused by two mechanisms: (i) Sensitivity as a intrinsic properties of conduc- tive fillers and (ii) Elongation of polymer matrix by temperature due to thermal expansion in which change the tunneling characteristics between conductive elements. Temperature dependency of the CNTs-polymer composites was investigated in the literature [76-78]. CNTs-polymer nanocomposites show nonlinear behav- ior to temperature making calibration difficult. Here, we investigate the temperature effect on the performance of our strain sensor. A strain sensor was installed into the convection oven, as shown in Figure 4.10a. For comparison, the effect of temperature on the pure Ag nanowire network deposited on glass slide was investi- gated as well. Temperature of the convection oven was increased step-by-step (i.e. 10 °C for each) while the resistance changes of the samples were monitored. Figure 4.10b shows the performance of both pure Ag nan- owire network and nanocomposite. As the figure depicts, resistance of the pure Ag nanowire network quite linearly increases by the applied temperature due to its metallic properties. Moreover, resistance of the nano- composite sample linearly increases by more sensitivity due to the tunneling effect of polymer substrate. So the temperature effects on the performance of the sensors could be compensated by a temperature sensor.
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Figure 4.10: a) Assembly of the strain sensor inside a convection oven, b) Temperature sensitivity of the strain
4.8.2 Humidity effect
The stability of a strain sensor was studied by immersing it in water. We also checked the stability of the simple structured sample to compare its stability with our strain sensor. Both samples were immersed in water while their resistances were recorded (upset of Figure 4.11). As Figure 4.11 illustrates, the resistance of the strain sensor decreased less the 14% by more than 21 hour immersion whereas the resistance of the simple structured samples decreased more than 30%. Moreover, the resistance of the strain sensor was stable after 10 hour immersion. On the other hand, the resistance of the simple structured sample was gradually de- creased by the immersion time. The better performance could be due to the better insulation of the Ag nan- owires in the case of the sandwich structured sample by the complete penetration of the liquid PDMS into the 3D network of Ag nanowires. However, we observed the penetration of the water droplets inside the PDMS matrix. The environment stability of the strain sensor can be improved by a better matrix material.
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Figure 4.11: Relative change of the resistance for the both simple and sandwich structured samples against the immersion time; upset, Photograph of the sandwich structured strain sensor immersed in water.
4.9 Response speed
To calculate response time of the strain sensor, we assumed that our strain sensor is a first order system. For a first order system:
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where τ is the time constant of the system, and C(S) and R(S) are the Laplace function for output and input, respectively.
For a ramp input:
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Then:
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By solving the equation:
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Ramp strain from 0% to 40% (with strain rate of 6.65 mm.s-[1]) was applied to the strain sensor (see Figure 4.12a) while the response of the strain sensor was measured (see Figure 4.12b). Then, data was fitted by a first order system response as follow:
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Therefore 90% time constant (τ90%) isThe time constant τ for our strain sensors is 200 ms. If we consider the unknown delay of the measurement system, the actual time constant for the sensor itself would be shorter than this.
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Figure 4.12: a) Ramp strain applied to the strain sensor. b) Experimental data from a strain sensor, best fitted curve and ideal response.
Chapter 5. Numerical Studies on the Piezoresistivity
5.1 Introduction
This chapter presents a computational studying on the piezoresistivity of the Ag nanowire-PDMS nanocomposite. Ag nanowires are randomly assigned into the PDMS matrix and electromechanical properties of the composite are investigated. Furthermore, the computational results are compared with experimental results. We could find an excellent agreement between our simulations and experiments showing successful prediction of piezoresistivity. Piezoresistive mechanisms for the Ag nanowires-PDMS nanocomposite are investigated by considering the interactions between neighboring nanowires and PDMS elastomer as well as topology changes of the network upon stretching. Strong piezoresistivity of the Ag nanowires-PDMS nano- composite is mainly due to disconnection between nanowires by elongation of the PDMS matrix. Number of disconnected nanowires increases by the applied stain causing decrease of the electrical pathways and increase of the sheet resistance.
5.2 Modeling of piezoresistivity
The mechanism for the piezoresistivity of the Ag nanowires-PDMS nanocomposite based stretchable strain sensors was investigated by numerical simulation. First, a 3D unit cell network model was generated by randomly orientated Ag nanowires in the PDMS matrix, as shown in Figure 5.1. We assumed that approxi- mately 1,500 Ag nanowires with a constant diameter (D=150 nm) and length (L=20 μm) were initially as- signed to random positions and orientations within the PDMS matrix with a width of Ly=62 μm, length of Lx=62 μm and thickness of Lz=5 μm. The orientation of each nanowire was assigned by using a spherical co- ordinate (Figure 5.2). A network resistor model was then constructed by junction identification between all pairs of nanowires in the network and the resistance of the total network was calculated by using Kirchhoff’s current law and Ohm’s law.
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Figure 5.1: Computational model of the Ag nanowire network in the PDMS matrix for numerical simulation of piezoresistivity of the Ag nanowires-PDMS nanocomposite: Randomly orientated Ag nanowires in the PDMS matrix
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Figure 5.2: Coordinates of single nanowire in the 3D space.
We classified junctions between two nanowires into three categories depending on their distances in- cluding (i) complete contact with no contact resistance, (ii) tunneling junction within a certain cut-off distance (C), and (iii) complete disconnection between nanowires. If the shortest distance (d) between the centerlines of two neighboring nanowires is smaller than or equal to the diameter (D) of nanowire, they are considered to be fully connected with no contact resistance. The tunneling current between two non-connected nanowires is defined when distance d is larger than D and smaller than a cut-off distance (C) (ゔ150.58 nm; distance at which the resistance between two neighboring nanowires is 30 times higher than the resistance of single nan- owire), such that the electrons can tunnel through the polymer matrix and can form a quantum conductive junction [71, 79]. The tunneling resistance between two neighboring nanowires can be approximately estimat- ed as follows[71]:
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where J is tunneling current density, V is the electrical potential difference, e is the single electron charge, m is the mass of electron, h is Planck’s constant, d is the distance between nanowires, λ is the height of the ener- gy barrier (1 eV for PDMS), and A is the cross-sectional area of the tunnel, which is assumed to be the same as the cross-sectional area of single nanowire. Furthermore, it is assumed that no current can pass through two adjacent nanowires when the distance between nanowires exceeds the cut-off distance (C) and their electrical path is fully disconnected. The corresponding electrical circuits for each configuration are shown in Figure 5.3.
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Figure 5.3: Different electrical interconnections between two adjacent nanowires: (i) complete ohmic connection with zero contact resistance, (ii) tunneling current between neighboring nanowires and (iii) complete disconnection of nanowires.
To obtain the total conductance change under strain, the positions and orientations of all nanowires were re-calculated and the connectivity between nanowires was analyzed again. The re-positioning and reorientation of nanowires under the applied strain were simplified by rigid body motions of nanowire within the PDMS matrix. Therefore, the change of nanowire positions and orientations caused by the mechanical strain can be evaluated using the 3D fiber reorientation model, see Figure 5.4[79].
The relative change of the resistance for the strains up to 100% calculated by the above mentioned model is illustrated in Figure 5.5a. As the figure shows, there is an excellent agreement between our experi- mental and computational results. Figure 5.5b illustrates the number of non-current flowing nanowires and tunneling junctions while the strain is continuously increased and uniformly applied to the structure. Here, the number of tunneling junctions is very low (averageゔ8) and not significantly affected by the applied strain. On the other hand, the number of non-current flowing nanowires increases gradually by the applied strain, increasing the resistance of the film.
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Figure 5.4: 3D fiber reorientation model: a) Orientation and position of neighboring nanowires before mechanical strain. b) Re-orientation and re-position of neighboring nanowires after mechanical strain.
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Figure 5.5: a) Response of the Ag nanowires-PDMS nanocomposite to the applied strain by experimental measurement and numerical simulation. b) The number of non-current flowing nanowires and tunneling junctions against the applied strain.
Figure 5.6a shows the morphology of the Ag nanowire network. Current flowing and non-current flowing nanowires are separated with yellow and blue color, respectively. As the figure depicts, lots of current flowing nanowires are connected through percolation network and contribute in the electrical conductivity. There are few non-current flowing nanowires in the entire network. Figure 5.6b illustrates the top projection view of the current (yellow) and non-current flowing nanowires in the Ag nanowire network when the network is subjected to 100% of uniaxial strain. The number of non-current flowing nanowires increased dramatically due to elongation of the network increasing the sheet resistance.
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Figure 5.6: The morphology of the Ag nanowire network: a) Current flowing (yellow) and non-current flowing nanowires (blue) before stretching. b) Current flowing (yellow) and non-current flowing nanowires (blue) after 100% of stretching.
5.3 Nonlinearity and linearity
To investigate the nonlinearity in the high resistance strain sensors and linear response in the case of low resistance strain sensors, the number of current flowing nanowires and topology changes of networks for both strain sensors are investigated. As shown in Figure 5.7a, the resistance of high resistance strain sensor increases nonlinearly by the applied strain. On the other hand, low resistance strain sensor shows a linear be- havior. Furthermore, the number of current flowing nanowires is linearly decreased by the applied strain for both high and low resistance strain sensors. However, the resistance of the low density Ag nanowire network (~ 1500 nanowires) is not dominated by the total number of current flowing nanowires but by the topology of percolating nanowire clusters. As shown in Figure 5.7c, the Ag nanowire network transforms from “homoge- neous network” to “inhomogeneous network” with emerging bottleneck locations that critically limit the elec- trical current. This results in the nonlinear response of high resistance strain sensor to the applied strain as shown in Figure 5.7a. In contrast, highly linear behavior is observed in the strain sensor with low resistance due to dense Ag nanowire network ((~ 2800 nanowires) as shown in Figure 5.7a. In this case, no bottleneck locations for electrical current are observed in the Ag nanowire network even for high strains up to 100% due to high number density of Ag nanowires as shown in Figure 5.7d. The Ag nanowire network with high num- ber density of nanowires exhibits better connectivity between nanowires. As a consequence, the emergence of bottleneck locations is less probable and the electrical resistance is linearly dependent on the number of cur- rent flowing nanowires. Therefore, the strain sensor with low resistance shows highly linear response to the applied strain.
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Figure 5.7: a) Piezoresistive response for high and low resistance strain sensors-both simulation and experiment. b) Decrease of the current flowing nanowires for high and low resistance strain sensors. c) Top projected view of the Ag nanowire network at different level of strain for a high resistance strain sensor with bottleneck location. d) Top projected view of the Ag nanowire network at different level of strain for a low resistance strain sensor.
5.4 Fracture mechanism of the Ag nanowire network
We defined electrical fracture of the thin film as non-conductivity of the thin film after a strain thresh- old. As our experimental results in Figure 5.8 shows, the nanocomposite thin film was non-conductive at ap- proximately 100% of strain. It is noteworthy to say that even at very high strain (ε>100%), the Ag nanowires- PDMS nanocomposite was still mechanically robust. Mechanical fracture is completely related to the mechan- ical properties of the polymer matrix. Interestingly, the electrical fracture in our computational model hap- pened at 95% of strain with excellent agreement with our experimental results. It is notable to mention that nonlinear increase of the resistance of the nanocomposite thin film at high strains (ε>60%) is not mainly due to rate of non-current flowing nanowires because their number increases as a linear function of strain. Instead, the topology of percolating nanowire cluster changes from “homogeneous network” to “inhomogeneous net- work” which plays as a bottleneck, increasing the resistance sharply. The topology of percolating nanowire cluster network for different strains is illustrated in Figure 5.9. Here, the top projectile view of nanowires in the nanocomposite film under 0, 50 and 100 % of strains are shown. For ε=0%, the nanowires are randomly dispersed into the PDMS matrix with almost uniform distribution of orientations (average angle with respect to the y-axis avgĂ44°, standard deviation =25.9°). As the strain is increased (i.e. ε=50%), Ag nanowires are re-oriented and re-positioned causing more disconnected nanowires. Also, the histogram of the orientation shows that Ag nanowires are aligned more along the direction of stretching (avgĂ33°, standard deviation =24.9°). For ε=100%, the Ag nanowires are more aligned along the direction of strain (avgĂ26°, standard deviation =23.3°) (see Figure 5.9). Furthermore, small clusters of sparse Ag nanowires dramatically con- tribute to the electrical conductivity. At an even higher strain (ε>100 %), Ag nanowires are more aligned along the direction of strain, the numbers of disconnected nanowire junctions are further increased and the bottle- neck through sparse network of nanowires completely loses the electrical contact, causing the film to be non- conductive.
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Figure 5.8: Relative change of the resistance for the nanocomposite thin film till electrical fractureboth experiment and simulation.
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Figure 5.9: Topology of the nanocomposite thin film under different levels of strain: a) Bottlenecks emerges under high strain causing higher electrical resistance of the thin film. b) Histogram of orientations of Ag nanowires with respect to the y-axis under various strains.
5.5 Alignment of nanowires
SEM images were taken on the surface of the simple structured sample under 0% and 50% of stretch. We observed the alignment of Ag nanowires parallel to stretching direction of the PDMS matrix, as shown in Figure 5.10a and 5.10b [80, 81]. Also, the optical images of the Cu wire-PDMS based sandwich structured sample in Figure panels 5.10c and 5.10d verify the alignment of wires along the direction of stretch. We found that our simulation result shows a good agreement with the experimental results. As Figure 5.10e and 5.10f show, nanowires are more aligned in the direction of stretch as compared to totally random orientation in the strain of 0% (the average orientation changed from 44° to 33° with respect to the stretching direction after 50% of stretching).
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Figure 5.10: a and b) SEM images on the surface of the simple structured sample for 0% and 50% of stretch. c and d) The morphology of wires’ orientation before and after 40% of stretch at the same spot. e and f) Topological changes of the simulated thin film under 0% and 50% of stretch.
5.6 Size-dependency of piezoresistivity
The effects of the aspect ratio (L/D) of nanowires on the sensitivity and stretchability of the strain sensor were investigated by our computational model. Nanowires with the aspect ratios of 115, 85.7, and 61.3 were randomly signed into the PDMS matrix. Size of the samples and volume fractions of nanowires are assumed to be all the same. The relative change of resistance against the applied strain for the different aspect ratios is illustrated in Figure 5.11. As the figure shows, the sensitivity of sensor gains by the decrease of aspect ratio. On the other hand, both linearity and stretchability of the sensor enhance by the increase of aspect ratio. Fur- thermore, stretchability improvement by the higher aspect ratios could be due to the better connection of long nanowires in the network for larger strains. Since the nanowire-nanowire disconnection rates for longer nan- owires are slower than that of short nanowires, reasonably the sensors possess lower sensitivity.
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Figure 5.11: Relative change of the resistance versus strain for the different aspect ratios.
Chapter 6. Human Motion Detection
6.1 Introduction
It was verified in the previous chapters that sandwich structured samples can be used as highly stretch- able strain sensors. The performance of the Ag nanowire-PDMS nanocomposite (i.e. stretchability of 70%) meets requirements for the human motion detection where a large strain (ε>50%) should be accommodated by the strain sensors. In this chapter, we perform human motion detection experiments. Then, the strain sensors are utilized for the wrist motion detection. Finally, an integrated smart glove system is developed by assem- bling the strain sensors on five fingers, one for each, for the motion detection of fingers. The bending of fin- gers accommodates strain to the strain sensors. The resistance change of the strain sensors are measured by a costume-made data acquisition (DAQ) and they are used as parameter for an avatar control in the virtual envi- ronment.
6.2 Human motion detection
Since our sandwich structured Ag nanowires-PDMS nanocomposite strain sensor is highly stretchable and sensitive, it can be used for the wearable and flexible human motion detection platform where a large strain (ε > 50 %) and bending angle (θ>150°) by the movement of the human body need to be accommodated by the sensor. In order to characterize the capability of our sensor to measure the bending angles of the human joints, we performed a bendability test for the strain sensors. A strain sensor was attached to a bended PET carrier whose two ends were hinged to a motorized moving stage, as shown in the inset of Figure 6.1. Cyclic bending and releasing were then applied to the sensor (maximum and minimum curvature radii were 88 and 41 mm, respectively). As shown in Figure 6.1, the resistance of sensor changes according to the bending cur- vature with a highly reproducible manner. The maximum and minimum resistances were maintained constant and stable sensing of the bending curvature was realized. Furthermore, the relative change of resistance in- creased slightly in the first cycle. It could be due to the sliding of the sensor on the PET substrate. As an ex- ample of the human motion detection, we conducted the sensing of wrist bending by mounting a sensor on the wrist joint. The response of sensor for the downward and upward bending motion of hand was recorded and illustrated in Figure 6.2. When the hand moves downward, a tensile strain is generated in the strain sensor, leading to an increase of resistance change. On the other hand, upward motion relaxes the strain sensor, causing a decrease of resistance.
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Figure 6.1: Bendability test of the strain sensor by attaching it on a bended PET carrier.
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Figure 6.2: Wrist bending measurement by mounting the strain sensor on the wrist joint.
For more precision measurement, we attached a strain sensor on an artificial finger device as shown in the inset of Figure 6.3a. The artificial finger was attached to a linear moving stage so that its angle could be adjusted by the distance between the jigs. A sandwich structured Ag nanowires-PDMS composite strain sensor was mounted on the joint of the artificial finger while the response of strain sensor under different bending angles was measured. Figure 6.3a illustrates the response of the sensor for the bending angle from 0° to 120°. 0.63 rad-1) and acceptable linearityThe sensor responses to the bending angle with a good sensitivity (∆R/R0 (R[2]=0.96). A small nonlinearity in the sensor response could be due to the sliding of the sensor on the PET carrier. For the dynamic test, repeated bending/relaxation cycles (angle ranges from 10° to 90°) were applied to the artificial finger while the current through the sensor at a constant voltage of 0.5 V was measured. The response of strain sensor to the dynamic loading profile is demonstrated in Figure 6.3b. As the figure shows, there is an excellent agreement between loading profile and the response of sensor without considerable drifting or hysteresis. Therefore, the sensor can be employed for accurate detection of human joints’ angle due to its excellent bendability and sensitivity.
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Figure 6.3: Bending angel measurement using an artificial finger: a) Response of Ag nanowires-PDMS nanocomposite strain sensor to the bending angles from 0° to 120°; inset, photograph of the artificial finger. b) Response of the strain sensor under repeated bending/relaxation cycles (10°-90°).
6.3 Smart glove system
Recently many researchers are interested in the hand gesture recognition since it is the natural way of human-machine interaction. Hand gesture recognition enables human to interact with machine very easily and conveniently. Gestures of hand could be applied to robot control and intelligent home systems through virtual reality[82]. Hand’s gesture can be recognized through two major types of technologies for the human motion detection namely physical sensory based and vision based human motion detection[83].
Some examples were provided earlier for vision based human activity recognition. Complex configuration and implementations as well as high cost are drawbacks of this method. Many of researchers used wearable sensory based method for human activity recognition (e.g. accelerometers, EEG sensors, RFID tags). However, mentioned devices can be uncomfortable for user since they require physical contact with the user, still having a verge over the accuracy of recognition.
Herein, we applied wearable and stretchable strain sensor assembled on five figures of a glove for the hand gesture recognition. Motion of figure is detected by resistance change of sensors due to stretching. High accuracy in hand motion recognition, very low cost and human-friendly are among key benefits of our smart glove system. The smart glove can be used for avatar control in the virtual environment, teleportation and intelligent applications.
6.3.1 Applications of smart glove system
- Virtual reality applications: Hand gesture recognition for virtual reality applications is one of the key applications of smart glove systems. Virtual reality interactions use gestures to enable realistic manipulations of virtual objects using ones hands. Avatar control and virtual reality based gaming are among virtual environment applications of smart glove systems. S. S. Rautaray et al. used hand gesture recognition technique to control a game in the virtual environment, see Figure 6.4[83].
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Figure 6.4: Gaming using hand gesture recognition[83].
- Teleopration: Due to advancements in computer science and electoral engineering, nowadays, re- mote controlling is one of the interesting topics for researches. An artificial hand can be control by smart glove device for multifunctional tasks. Artificial hands have potential applications in industry as well as medi- cal science for surgery (e.g. micro-hand can be control by smart glove). For example, J. P. Wachs et al. em- ployed human hand gesture recognition for telerobotic control, as illustrated in Figure 6.5[84].
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Figure 6.5: User interface with real robot[84].
6.4 Integrated smart glove system
Herein, an integrated smart glove system is developed by our flexible, stretchable and wearable strain sensors. The smart glove is integrated with a custom-made data acquisition (DAQ) system with wireless communication modulus on a chip, see appendix A for details. Moreover, designed chip is a multifunctional circuit which acquires data from sensors, calibrates the response of each sensor and transmits all the data to a computer by wireless communication system. In computer environment, we developed the LabVIEW (Na- tional Instruments) programing for the signal processing and avatar control, see appendix B and C for more details.
6.4.1 Custom-made data acquisition (DAQ) system
Figure 6.6 shows the overall illustration of our custom-made data acquisition (DAQ) system composed of 5 important sections include: Current sourcing/amplifying, gain calibration, reference voltage generator, subtractor and Zigbee communication module.
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Figure 6.6: Overall architecture of interface circuit
- Current sourcing/amplifying: In this section, constant current (100 µA) passes thought all sensors via multiplexer (MUX) switches. Here, 16x1 MUX is used for handling of 16 sensors. The switching is per- formed in regular sequences by counting up MUX channels from channel 0 to channel 15 with an average speed of 1.3 ms. Moreover, to acquire the sensors’ data more smoothly; sampling speed of each sensor should be fast enough. The interface circuit provides sampling speed over 90 samples per second for each channel. The resistance of the strain sensors could be measured by the voltage changes. Since the voltages are very small (few order of mv with variation of 1 mv) for analog to digital converter (ADC) where the ADC resolu- tion is around 3.6 mv, voltages are amplified with the gain of 1000 V/V.
- Gain calibration: For our specific application, the voltage changes were magnified 1000 time. However, for other applications, the gain value can be control by a non-inverting amplifier, as shown in Figure 6.7. The binary weighted resistor gives summation combination of R, 2R, 4R, 8R by the parallel switching. Therefore, the corresponding gain could be calculated by:
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where the resistance of the binary weighted resistor is given by x value:
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Figure 6.7: Programmable amplifier based on the binary weighted resistor model.
- Reference voltage generator: Reference voltage is defined by the initial amplified voltage for each sensor when the strain sensor is relaxed without any strain in the structure. Therefore, the reference voltage is determined by the initial resistance of each sensor.
- Subtractor: The electrical circuit for the subtractor is illustrated in Figure 6.8 where the amplified voltages from sensors are subtracted with the defined reference voltages. The function of subtractor for a strain sensor illustrated in Figure 6.9 named as offset calibration. As shown in the figure, the initial resistance of the strain sensor is shifted to the 0 v. As a result, the output signals from our chip are only as a function of the sensitivities (GFs) of strain sensors.
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Figure 6.8: Electrical circuit for the subtractor.
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Figure 6.9: Offset calibration of a strain sensor.
- Zigbee communication module: The amplified and calibrated signals are then digitized through analog to digital converter (ADC) in microprocessor control unit (MCU), and the digital data is transmitted wirelessly by Zigbee module. A receiver is also designed as a data receiver. It can be plugged in computers or other devices for data transmission. The photographs of our DAQ chip and receiver are shown in Figure 6.10.
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Figure 6.10: Photographs of (a) DAQ system and (b) receiver.
The overall specifications of our designed interface circuit are depicted in Figure 6.11.
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Figure 6.11: Overall specifications of designed interface circuit.
6.4.2 Integration of smart glove with interface circuit
Since our developed DAQ system is very small (4.5x5.5 cm), it can easily combined with our glove system which made of five Ag nanowires-PDMS sensors, one sensor for each finger. The integrated smart glove system is illustrated in Figure 6.12. The device can acquire the data from strain sensors and perform the offset calibration and send to the computer by wireless communication.
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Figure 6.12: Integrated smart glove system.
Figure 6.13a shows the motion detection for index and middle fingers. The more bending finger gen- erated, the more increase in the resistance of sensor occurred. Also, the sensor exhibited a good stability, re- sponse speed and repeatability. Here, an avatar control, as an application of the integrated smart glove system, was demonstrated by using our smart glove device. The resistance change of the strain sensor was employed as a parameter to control the finger motion of avatar in the computer virtual environment. As the Figure 6.13b illustrates, the bending of finger leads to the bending of avatar finger in the virtual environment.
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Figure 6.13: a) Motions detection of index and middle fingers. b) Control of avatar fingers in the virtual environment using smart glove system.
Chapter 7. Conclusions and Future Remarks
7.1 Conclusion
In summary, we developed new types of the strain sensors with high sensitivity, stretchability stability, linearity with simple and low cost of fabrication process based on the sandwich structured Ag nanowires- PDMS nanocomposite. The tunable gauge factors and stretchability of the sensors are in the ranges of 2 to 14 and 70%, respectively, both of which are higher than those of the conventional strain sensors. The response of the sensors can be predicted very well by computational model based on the resistive network of Ag nan- owires within the PDMS medium. We have found that the sandwich structured strain sensors have a good response to the bending and joint angle measurement. Finally, an integrated smart glove made of the stretcha- ble strain sensors assembled in each finger was fabricated and used for the real-time motion detection of fin- gers. As an application, an avatar control in the virtual environment has been demonstrated by the finger pos- ture detection using our smart glove device. We believe that our strain sensor devices will open up new fields of applications in flexible, stretchable and wearable electronics due to their excellent performances; especial- ly, in human motion detection applications where very large strain should be accommodated by the strain sen- sor.
7.2 Future remarks
- Reducing polymer plastic deformation by choosing better performance polymers like EcoFlex
- Human body motion detection (i.e. other part such as knee, elbow and etc.)
- Humanoid robotic control using our integrated smart glove system
Appendix A: Electrical circuits of the interface circuit.
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Appendix B: LabVIEW programing for DAQ (NI DAQ system)
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Appendix C: Avatar control LabVIEW programming
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Summary
Stretchable Strain Sensors based on the Ag Nanowires-elastomer Nano- composite
In summary, we developed new types of the strain sensors with high sensitivity, stretchability stability, linearity with simple and low cost of fabrication process based on the sandwich structured Ag nanowires- PDMS nanocomposite. The tunable gauge factors and stretchability of the sensors are in the ranges of 2 to 14 and 70%, respectively, both of which are higher than those of the conventional strain sensors. The response of the sensors can be predicted very well by computational model based on the resistive network of Ag nan- owires within the PDMS medium. We have found that the sandwich structured strain sensors have a good response to the bending and joint angle measurement. Finally, an integrated smart glove made of the stretcha- ble strain sensors assembled in each finger was fabricated and used for the real-time motion detection of fin- gers. As an application, an avatar control in the virtual environment has been demonstrated by the finger pos- ture detection using our smart glove device. We believe that our strain sensor devices will open up new fields of applications in flexible, stretchable and wearable electronics due to their excellent performances; especial- ly, in human motion detection applications where very large strain should be accommodated by the strain sen- sor.
Keywords: Stretchable strain sensor, silver nanowire, nanocomposite, piezoresistivity and human motion detection.
Acknowledgement
First of all, I have to sincerely thank my supervisor, Prof. Park, for his guidance and warm support throughout of my M.Sc. studies. I also would like to thank Prof. Lee for serving as one of my thesis committee members.
I would like to deeply appreciate Prof. Ryu’s advise and help for the numerical modeling section in my M.Sc. thesis, his kind acceptance for joining his research group and working with group members as well as his valuable comments as a member of my thesis committee.
I would like to thank Prof. Yoo from department of electrical engineering at KAIST for his discussions and collaboration with our group in development of the costume-made data acquisition (DAQ) system.
To all of my colleagues in both Multifunctional & Integrated Nanosystems Technology (MINT) group at department of mechanical engineering and Mobile Sensor and IT Conver- gence (MOSAIC) center at KAIST Institute (KI) for the Nanocentury, thank you very much for your kind help and support not only for my research and studies, but also for my living in Ko- rea.
Finally, I express my heartfelt gratefulness to all of my dear friends, in particular, Ms. Maryam Ghahremani, Mr. Hamid Souri, and Ms. Elmira Yadollahi, for their understanding, help, support and encouragement. Their friendship makes my life a wonderful experience.
Curriculum Vitae
Name: Morteza Amjadi
Date of Birth: 1989/04/28
Education
2012-2014 Korea Advanced Institute of Science & Technology (KAIST), South Korea
2007-2012 Iran University of Science & Technology (IUST), Iran
Career
2012-2014 Graduate research assistant, Multifunctional & Integrated Nanosystems Technology (MINT) group, Department of Mechanical Engineering, Korea Advanced Institute of Science & Technology (KAIST), South Korea
2012-2014 Graduate research assistant, Mobile Sensor and IT Convergence (MOSAIC) center, KAIST Insti- tute (KI) for the Nanocentury, Korea Advanced Institute of Science & Technology (KAIST), South Korea͑
Academic Activities.
Honor & Awards
Best presentation award in the 16th Korea MEMS (KMEMS2014) conference
Publications
Journals:
1) Morteza Amjadi, Aekachan Pichitpajongkit, Sangjun Lee, Seunghwa Ryuand Inkyu Park, “Highly Stretchable and Sensitive Strain Sensor based on the Ag NWs-PDMS nanocomposite”, ACS Nano, 2014, 8 (5), pp 5154-5163.
2) Hyeonjin Eom, Jaemin Lee, Aekachan Pichitpajongkit, Morteza Amjadi, Jun-Ho Jeong, Eungsug Lee, Jeongyoung Lee and Inkyu Park, "Ag@Ni Core-Shell Nanowire Network for Robust
Transparent Electrodes Against Oxidation and Sulfurization, Small, accepted.
Conference proceedings:
1) Morteza Amjadi, Aekachan Pichitpajongkit, Seunghwa Ryu and Inkyu Park ”Piezoresistivity of the Ag NWs-PDMS nanocomposite”, IEEE MEMS2014, pp. 785-788.
2) Morteza Amjadi and Inkyu Park “Sensitive and Stable Strain Sensors based on the Wavy Struc- tured Electrodes”, IEEE NANO2014 (accepted).
3) Morteza Amjadi, Min Seong Kim and Inkyu Park “Flexible and Sensitive Foot Pad for Sole Dis- tributed Force Detection”, IEEE NANO2014 (accepted).
International & Domestic conferences:
1) Morteza Amjadi, Jihoon Suh, Seunghwa Ryu, Hyung-Joun Yoo, Inkyu Park “High Performance Flexible Strain Sensors” 16th Korea MEMS Conference (KMEMS2014) (best paper award).
2) Morteza Amjadi, Aekachan Pichitpajongkit, Seunghwa Ryu and Inkyu Park “A Wearable Strain Sensor by Piezoresistivity of the Ag NW Network”, the Korean Sensors Society Meeting (KSSM), South Korea.
3) M. Amjadi, A. Pichitpajongkit, and I. Park “Silver Nanowire Network-Elastomer Composite based Stretchable Strain Sensor”, the 7th World Congress on Biomimetics, Artificial Muscles and Nano-Bio (BAMN2013), Jeju Island, South Korea.
4) Aekachan Pichitpajongkit, Morteza Amjadi, Daejong Yang, Jaehwan Lee, Hyeonjin Eom, Werapon Kamonkhantikul, Inkyu Park “Silver Nanowire Network based Stretchable Strain Sensor”, 15th Korea MEMS (KMEMS2013) Conference, Jeju Island, South Korea.
- Arbeit zitieren
- Morteza Amjadi (Autor:in), 2014, Stretchable Strain Sensors Based on the Ag Nanowires-elastomer Nanocomposite, München, GRIN Verlag, https://www.grin.com/document/286221
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