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Applications of wearable electronic textiles (e-textiles) in smart clothing have attracted considerable attention on account of their immense potential to monitor vital signals, such as electrocardiogram (ECG) and respiratory rate. Embroidery is one of the most preferred ways to create e-textiles because the technique allows for flexible patterns, dimensional accuracy, mass production, and cost-effective production. However, the parameter design of embroidery with conductive yarns not only affects the aesthetics of the product but also results in incompetent electrical performance, such as short out of interconnects because the jammed stitches. This thesis proposes embroidery techniques for the design and construction of stretchable interconnects, electrodes and strain sensors used for ECG and respiration signals monitoring. The thesis focuses on several key aspects for design and fabrication of smart e-textile wearables. 1) After comparing needle thread loop formation performance of various commercial conductive yarns, the analysis of conductive tracks with multi-direction lock-stitch embroidery indicated embroidery direction and stitch length significantly affect size shrinkage, stitch length and embroidery speed significantly affect resistance of conductive tracks. Stretchable interconnects are designed with zigzag and horseshoe shaped structures, which fabricated by lock-stich and TFP embroidery techniques, respectively. The horseshoe-shaped interconnect proposed in this study (R =5mm and θ=45°) shows significant elongation over 130% and maintain stable resistance during stretching. 2) The electrodes are embroidered by silver-coated conductive yarns with different embroidery patterns, filling density and sizes. The skin contact impedance at 1 Hz, and signal quality of ECG monitoring compared with Ag/AgCl electrodes are evaluated. The EP2 pattern electrode shows the lowest surface resistance under 2.5 kPa wearing pressure since the conductive threads overlapped in filling stitch structures. 3) The embroidered strain sensors combining conductive yarns and elastane threads are designed in conjunction with lock-stitch and TFP embroidery techniques. The electromechanical performance of optimal sensor shows 32.4 GF at 40% elongation, less electro-mechanical hysteresis and stable stretching-releasing performance within 1000 cycles. The sensor has sufficient capability to clearly detect normal breathing and deeply slow breathing patterns. 4) Based on the design concept of embroidered electrodes and strain sensors, a wearable smart band prototype for physiological signal monitoring is developed. Wearing pressure at 1.5 kPa can ensure the quality of ECG and respiratory signals in body static and dynamic measurement conditions. Additionally, the walking tests demonstrate that the smart band under 1.5 kPa wearing pressure was able to measure representative respiratory and ECG waveform features, body temperature and humidity at various levels of treadmill speed. The outcome from this research provides guidelines for development and fabrication of tailored smart e-textiles wearables for health care with highly efficient embroidery techniques.

Graphene, a single layer of graphite, owing to its excellent mechanical, electrical, and thermal properties, has evolved as an exceptional nanomaterial in the past decade. It holds great promise in developing various novel applications from biomedical to structural composites. However, several challenges remain in realising the great potential of this material; one being the bulk scale production of graphene. This thesis has been concerned with production of pristine few-layer graphene (FLG) using liquid phase exfoliation (LPE) of graphite in various solvent media and exploring the applications of graphene-based composite coatings as optical Raman-strain sensors. LPE of natural graphite using bath sonication was used to produce highly stable pristine FLG in 1-methyl-2-pyrrolidinone (NMP) and N,N-dimethylformamide (DMF). Atomic force microscope (AFM) was used to analyse the exfoliation efficiency and lateral dimensions, while Raman spectroscopy provided an insight about the quality of the graphene flakes. Moreover, the potential for dynamic light scattering (DLS) as an efficient in situ characterisation technique for estimating the lateral dimensions of graphene flakes in dispersions was demonstrated. LPE was also employed to explore various routes to produce pristine graphene in aqueous media which can be used for toxicity studies. Aqueous dispersions were prepared by a solvent exchange method of graphene originally in organic solvents (NMP and DMF) using dialysis, achieving 0.1 v/v% organic solvent levels. Pristine aqueous graphene dispersions were also prepared by directly exfoliating graphite in biocompatible surfactant (TDOC- Sodium taurodeoxycholate) and biomolecules (Phosphatidylcholine and human serum albumin) solutions. Cell culture studies by collaborators revealed that solvent-exchanged and TDOC-exfoliated pristine FLG displayed minimal toxicity and albumin-exfoliated FLG hardly any cytotoxicity, whereas phosphatidylcholine-exfoliated FLG was cytotoxic. Raman spectroscopy is a well-established technique used to study the local deformation of carbon-based composites by following the shift rates of the Raman 2D band with strain. Raman active strain coatings were produced from epoxy composites made with the FLG produced by LPE in organic solvents and by electrochemical exfoliation method. The deformation experiments on these coatings revealed little or no strain sensitivity, due to several factors such as length of flakes, processing history, graphene loading, defects in graphene and alignment of flakes within the composites. As an alternative, composite coatings made from chemical vapour deposition (CVD) graphene were investigated. Excellent strain sensitivity was observed upon various cyclic deformational sequences and Raman mapping over 100 × 100 µm area. In comparison to the commercially available wide area strain sensors, CVD graphene composite coatings with a calculated absolute accuracy of ~ ± 0.01 % strain and absolute resolution of ~ 27 microstrains show promise for wide area Raman-based strains sensors.

GaN is a monocrystalline material that can be grown using metallo-organic chemical vapour deposition (MOCVD), and has desirable mechanical and semiconducting properties for operating as a sensor. It has a Young’s modulus of 250 to 350 GPa, which shows little decrease with respect to temperature beyond 400°C. GaN also exhibits piezoelectric and piezoresistive effects, meaning that it will generate a charge and its electrical resistance will change when the material is strained respectively. In this PhD, GaN has been used as the base material for pressure sensors that potentially can be used in excess of 400°C and at a pressure in excess of 50 bar (5 MPa), with potential applications in aerospace and oil exploration. The pressure sensor is a circular diaphragm created from a GaN/sapphire wafer, and was designed and tested in order to determine if GaN can act as a sensing material in these environments. In addition to the diaphragm sensor, GaN templates that can potentially be used for sensors were grown using an epitaxial layer overgrowth (ELOG) method. These sensors are potentially more mechanically robust than similar templates etched out of GaN/sapphire wafers because they will have less inbuilt strain due to lower dislocation densities. It was possible to release beams and cantilevers from GaN ELOG templates. Mechanical probe tests were undertaken on these devices to see if they were fully released and robust. GaN single crystal growth requires a substrate material, such as (111) silicon or (0001) sapphire, meaning that the thermal properties of the substrate are important for a device operating in excess of 400°C. GaN high electron mobility transistors are heat sensitive, experiencing a decrease in current between the drain and source terminals as the temperature increases. Therefore a GaN-based sensor needs a substrate with the highest possible thermal conductivity to act as a heat sink, which means removing as much heat as possible from the GaN sensor. Diamond has superior thermal conductivity to both sapphire and silicon, so a novel silicon/polycrystalline diamond composite substrate has been developed as a potential GaN substrate. Polycrystalline diamond (PD) can be grown on 4 inch diameter wafers using hot filament chemical vapour deposition (CVD), on (111) silicon (Si) from which single crystal GaN epitaxy can also be grown. In order for the (111) Si/PD composite substrates to be useful heat sinks, the Si layer needs to be less than 2 m. PD was initially grown on 525 to 625 m thick Si wafers that required thinning to 2 m. Achieving this Si layer thickness is difficult due to the presence of tensile stress in the Si caused by a mismatch in the coefficients of thermal expansion (CTEs) between Si and PD. This stress causes the wafer to bow significantly and has been modelled using ANSYS FE software. The models show that the bow of the wafer increases when it is thinned, which will eventually cause the Si layer to delaminate at the Si/PD interface due to poor adhesion and a build up for shear stress. When the Si layer is mechanically thinned, the Si layer can crack due to clamping. The experimental wafer bow and micro-Raman measurements validate the model for when the silicon layer is thicker than 100 m and these results show that an alternative processing route is required.

Nonlinearities exist in all real-life systems and can be caused by behaviors such as large deformations, yielding, contact, etc. which can result in phenomena not found in linear testing. Considering nonlinear behavior in dynamic analyses can be beneficial to increase model predictability and can accurately characterize system responses. Generally, accelerometers are commonly used in linear and nonlinear vibration testing to obtain the acceleration response of a system. However, some systems may render the use of accelerometers problematic, or accelerometers may introduce limitations to measurement capabilities such that it can be beneficial to obtain experimental data using different sensors. In the context of experimental data, strain gauges can alternatively be used to obtain dynamic properties both in the laboratory and in the field. Strain gauges are additionally capable of quantifying damage that is directly estimated from strain measurements, such as stresses, that accelerometers cannot obtain. But, measuring strain in structural dynamics can be disincentivizing and expensive due to the complexity of data acquisition, lack of portability, high costs, and the requirement of prior knowledge and training required for a proper installation. The purpose of this research is to introduce an alternative resource to measure nonlinear dynamics of structures using strain in a cost-effective and streamlined platform. This thesis accomplishes the goal of developing and testing a low-cost, efficient, wireless, intelligent sensor for strain (LEWIS-S) for the nonlinear dynamic assessment of a simple structure. The LEWIS-S sensor functions on a platform of various Arduino hardware components and free Integrate Development Environment (IDE) software. Two different sensor configurations were used in the validation testing in this research, namely, a uniaxial friction-magnetic strain checker and a traditional pasted uniaxial strain gauge. Static and dynamic validation tests were conducted on a small cantilever beam where the LEWIS-S was compared to a commercial DAQ system to verify the accuracy and dependability of the sensor. The LEWIS-S sensor was then used in nonlinear dynamics tests to experimentally characterize the softening behavior of a cantilever beam with geometric and inertial nonlinearities produced by large deformations. Two experiments were performed on a nonlinear cantilever beam with measurements obtained at the base with the LEWIS-S sensor and at the tip using an accelerometer. The first test was a sine sweep through the fundamental resonance of the system and the second test was a ring-down from an initial static deformation. Based on the results of the validation and nonlinear experimental testing, the LEWIS-S sensor demonstrated various streamlined sensing capabilities. Namely, the sensor is approximately 95% cheaper than standard commercial equipment and the compact design reduces the plan-view-footprint of the equipment by approximately 75%. Furthermore, the small footprint and wireless function enhances the portability which increase sensing capabilities. The versatility of the sensor also allows for the compatibility of different strain gauge attachments which can be useful for sensing optimization during testing. Additionally, the LEWIS-S has an inherently simple design such that limited knowledge is required to manually assemble and use the sensor. Future research proposes deploying the LEWIS-S in field testing as well as advancing the performance of the LEWIS-S by addressing sensor limitations.

The development of resistive-based strain sensors comprising nanomaterials has become of interest for the past decade. Some of the interest in these types of sensors are due to their notable sensitivity, enabled by sensing mechanisms stemming from size confinement not seen in the bulk form of these materials. Apart from their sensitivity, these nanomaterials capable of detecting mechanical strain are also capable of being transferred to various hard, flexible and stretchable substrates. The ability to be used in compliant substrates overcomes the limitations seen in MEMS strain sensors, which can expand the array of their potential utility to applications such as wearable sensors. By developing an ultra-sensitive nanomaterial capable of detecting a wide strain range, it is possible to develop devices capable of monitoring human mechanical strain activity (from the cellular level to human motion). The ability to detect mechanical activity of biological phenomenon can be relevant in the medical field by developing deployable devices capable of giving clinicians reliable and actionable data. This thesis investigates the performance of a material comprising a subcontiguous film of noble metal supported by single-layer graphene (referred throughout as nanoislands, or Gr/M where M is the metal used), when used in devices for the detection of biomechanical deformations originating from human physiological activity. Chapter 1 introduces the Gr/M material and gives an overview of the various sensing modalities Gr/M possesses, while discussing recently developed sensing devices using this material. Chapter 2 and Appendix B present an iteration of a device, comprising Gr/Pd on a flexible substrate, for use as a wearable device to monitor swallowing activity in head and neck cancer patients. This study involves a 14-patient cohort study of head and neck cancer patients after radiation or surgery and the development of a machine learning algorithm to analyze the data given by the wearable strain sensor. Chapter 3 and Appendix C incorporates a layer of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on top of the Gr/Pd film, which increases the dynamic range of strain detectable by the composite film while retaining its sensitivity. Chapter 4 and Appendix D presents an empirical study comparing the material properties of Gr/M and composites comprising subcontiguous metal films on hexagonal boron nitride (hBN). The goal of this study is to help elucidate the possible sensing mechanism(s) responsible for the material’s sensitivity to strains as low as 0.0001% (1 ppm) strain.

Optical fiber Bragg gratings are unique among embedded strain sensors due to their potential to measure strain distributions with a spatial resolution of a few nanometers over gage lengths of a few centimeters. This thesis presents a genetic algorithm for the interrogation of optical fiber Bragg grating strain sensors. The method calculates the period distribution along the Bragg grating which can then be directly related to the axial strain distribution. The period distribution is determined from the output intensity spectrum of the grating via a T-matrix approach. The genetic algorithm inversion method presented requires only intensity information and reconstructs non-linear and discontinuous distributions well, including regions with significant gradients. The method is demonstrated through example reconstructions of Bragg grating sensor simulated data. The development of this algorithm will permit the use of Bragg grating sensors for damage identification in regions close to localized damages where strong strain non-linearities occur. A second application of the genetic algorithm search procedure is also discussed, the optimization of sensor locations for a particular structural application. An implementation of the genetic algorithm to a discrete sensor location problem is presented and its performance evaluated. A combination of the developed GA with available damage identification systems can be effectively used to find the optimal sensor distribution for damage detection.