Konrad Walus
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One of the smartest people I've had the privilege to work with, Konrad is also a gem of a person who is always ready to provide whatever support a student seeks/needs. All this embedded in an ever-smiling personality!
Graduate Student Supervision
Doctoral Student Supervision
Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.
This dissertation demonstrates the development of a novel printing platform with integrated mixing and dispensing capabilities for patterning compositionally graded thin film sample libraries of fluid material blends. The modular nature of the combinatorial print head enables the reusability of the mixing and dispensing modules while the elastomeric base structure may be replaced as desired due to its ease of handling and integration. Such a combinatorial print head is shown to consume smaller functional material volumes along with faster fabrication of thin films using such materials. The key advantage of these print heads is their ability to rapidly homogenize multiple fluid inputs which results in highly efficient multi-material thin film prototyping.An economical fabrication process for the disposable elastomeric base structure through a simple casting process using 3D printed molds is utilized. The requisite combinatorial functions of fluid proportioning and mixing are validated through extensive direct and indirect characterization. A sample preparation methodology is proposed and the combinatorial printing platform is assembled to validate operational performance electronic solution processable polymers that are typically used for fabricating sensor components. The intrinsically conductive electronic polymers are also tested for their microfluidic processability and inkjet printability.A statistical hypothesis testing framework is established for analyzing the characterization data which is then used for inferencing and validation. Case studies on multiple hypotheses involving the two types of intrinsically conductive polymers are performed which illustrates the utility of the combinatorial printing platform as a rapid thin film sample patterning tool with minimal material wastage. Analyses of the characterization data of such sample ensembles demonstrate the importance of the availability of a large number of functionally graded samples in the context of high throughput material screening. In addition, these tests are also used as an indirect performance evaluation of the combinatorial printing system when compared with benchmark processing of material blends. Conclusions regarding application-specific advantages and disadvantages of the two polymers are inferred in the context of standalone and temperature-dependent electrical conductivity performance as sensor materials and blending tests are used to determine the ideal operational niche of each material.
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Understanding the dynamic behaviour of nanoscale quantum-dot cellular automata (QCA) networks involves the simulation of large numbers of QCA devices, with the complexity of the full quantum treatment exponential in the network size. Previous attempts to limit this complexity introduce simplifying assumptions with known inaccuracies. In this thesis we investigate an alternate approach, extracting performance metrics through analysing the low energy eigenspectrum of a clocked network. We make two major contributions. In the first part of this thesis, we study the use of silicon dangling bonds (SiDBs) as a platform for combinatorial logic, and ultimately nanoscale QCA. We present models for understanding the preferred configurations and dynamics of charges in these structures. We consider the clocking of SiDB-based QCA wires, and reveal a complicated trajectory of charge states that serve as a challenge for QCA operation. By studying SiDB-based QCA from the framework of the familiar 3-state model, in which these preferred charge states translate to eigenstates of a system Hamiltonian, we determine conditions for which SiDB-QCA wires can cor-rectly operate when clocked. These conditions are potentially impractical unlessnet-neutral SiDB arrangements can be achieved. The remaining bulk of the thesis revolves around the link between QCA clocking and quantum annealing. We first investigate the adiabaticity of simple 2-state QCA networks under zone clocking. We present upper bounds on the clocking frequency beyond which adiabaticity falls below a 99% threshold, and demonstrate how we can efficiently estimate clocking performance using only a few of the energy eigenstates. Due to a natural mapping between QCA cells and superconduct-ing flux qubits, the potential for investigating performance using a physical quantum annealer is explored. Methods for embedding QCA networks onto the annealer are discussed and a selection of annealing results are analyzed. Finally, we establish a method for decomposing the system Hamiltonian into contributions from given components, and a means to identify meaningful components which critically affect clocking performance. This framework reveals a heuristic algorithm for approximating the low energy eigenspectra of large QCA networks, enabling future investigations into the performance of networks well beyond previous sizelimitations.
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In this thesis, we aim to answer one research question: What is the algorithmic role of classical computing in scaling up quantum computing applicability? Quantum computers have promising potential in offering more efficient solutions to some of the most demanding computational problems in the industry. There has been phenomenal progress in the theory and realization of quantum computers in the last few decades. However, quantum computing is a nascent field of technology and scaling up its computing capacity remains impeded by many open engineering/scientific challenges. One emerging solution to alleviate some of the present limitations is finding clever use of classical pre- and post-processing techniques in tandem with quantum algorithms. This helps to advantageously use scarce quantum computing resources for only a portion of a problem that benefits the most from a quantum computer. In other words, it leads to a hybrid framework in which quantum computers are considered special-purpose co-processors for accelerating specific computational tasks. We aim to develop a hybrid theoretical framework for the role of classical algorithms in scaling up quantum computing. We investigate this by focusing on three distinct limitations. We review each limitation in the context of a different application and propose an original hybrid classical-quantum solution to it. First, we focus on classical algorithms' role in a more resource-efficient compilation and embedding of quantum programs. We propose a classical algorithm that helps to embed larger optimization problems in a quantum annealer. Then we explore how a problem decomposition technique and preprocessing of the input problem can help solve significantly larger optimization problems on a quantum computer. Finally, we propose a completely hybrid quantum algorithm for preparing ground states of quantum Hamiltonians. We achieve orders of magnitude faster convergence by combining classical optimization with variational quantum state preparation.
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This thesis presents a novel concept, lab-on-a-printer, whereby microfluidic modules are directly integrated into an inkjet dispenser. To enable this concept, a novel inkjet dispenser that can be integrated with microfluidic modules is designed, fabricated, and characterized.To limit the risk of cross contamination which is critical to many targeted applications, the inkjet dispenser is designed to have a modular structure that enables reusing its actuation unit and the disposal of its microfluidic chip. Furthermore, a low-cost fabrication process for the disposable microfluidic chip, mainly based on simple Polydimethylsiloxane (PDMS) moulding and SU-8 epoxy-based negative photoresist casting processes, is developed to reduce its cost. The use of PDMS in the fabrication of the microfluidic chip creates a path for its integration with pre-existing PDMS-based microfluidic modules.The fabricated inkjet dispensers are characterized to understand their limitations and identify their potential applications. For instance, droplet-to-droplet variations and maximum printable ink viscosity are used as characterization metrics. Diameters of more than 50,000 droplets per tested device are found to have a coefficient of variation (CV) in a range of 0.8% - 2.5% for each of 10 tested devices. A water and glycerol mixture with a viscosity of ~19 mP·s is identified as the mixture with maximum printable viscosity.Numerical simulations are employed to identify the key design parameters for the inkjet dispenser. These simulation results, combined with manufacturability constraints, are used to derive techniques for improving performance, hence broadening the potential applications of the technology. This resulted in inkjet dispensers with improved performance, where the maximum printable viscosity is doubled, meeting and surpassing commercial devices.Finally, a novel approach to integrate microfluidics with the inkjet dispenser is presented. This approach is successfully implemented to integrate the inkjet dispenser with a microfluidic mixer to demonstrate the capability of the integrated lab-on-a-printer platform concept, specifically the capability of printing patterns with a configurable ink composition. The presented lab-on-a-printer concept has potential applications in multiple scientific fields including biology, chemistry, and printable electronics.
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The thermoelectric performance of electrodeposited, aluminum doped zinc oxide was assessed. In this work, wurtzite ZnO was first modelled using Mueller-Plathe to compare the effectiveness of different nanostructured configurations on reducing thermal conductivity. A new analysis technique, Local Vibrational Density of States Equilibrium Molecular Dynamics (LVDOS-EMD), was created to study localized lattice vibrations around nanostructural features of silicon and ZnO, and was used to predict thermal properties in materials of similar composition 17× faster than conventional thermal modelling methods. A 30% void density was determined to yield the best reduction in thermal conductivity by volume of voids in bulk Al:ZnO with a computed thermal conductivity of 0.77 W m-¹ K-¹ at room temperature, 3× below the threshold achieved through established experimental means with high electrical conductivity Al:ZnO. Thick film, electrodeposited Al:ZnO was grown using a nitrate system. Experiments on solution pH using various counter electrodes demonstrated that inert electrodes caused acidification of the growth solution, limiting film thickness. Chloride contamination from commonly used Ag/AgCl reference electrodes was also determined to affect thick film opacity, morphology, crystallinity, and electrical properties. Aluminum integration and activation was explored by adding Al(NO₃)₃ to the growth solution during film synthesis, yielding aluminum integration molar ratios of up to 1.72% (Al.₀₃₄Zn.₉₆₆₀). Partially doped films in excess of 95 µm thick, 4× the thickness reported elsewhere, were electrochemically grown and characterized. Sub-micron voids were integrated into the films using sacrificial material and annealing. A new electrochemical chromium etching methodology was developed and successfully used to free 20 films from their growth substrates for thermoelectric characterization. A new, reusable thermoelectric test apparatus for thin film thermoelectric testing was designed, implemented, calibrated, and successfully deployed to characterize ZnO and Al:ZnO thin films grown 79 – 95 µm in thickness. Extremely low thermal conductivity of 11 mW m-¹ K-¹ at room temperature was demonstrated concurrently with a Seebeck coefficient of -88 µV K-¹. Polycrystallinity and poor dopant activation yielded a low electrical conductivity of 0.75 mS/cm and corresponding low room temperature ZT of 1.3×10-⁵ for the Al:ZnO films.
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Quantum-dot Cellular Automata (QCA) provides a basis for classical computation without transistors. Many simulations of QCA rely upon the Intercellular Hartree Approximation (ICHA), which neglects the possibility of entanglement between cells. While simple and computationally efficient, the ICHA’s many shortcomings make it difficult to accurately model the dynamics of large systems of QCA cells. On the other hand, solving a full Hamiltonian for each circuit, while more accurate, becomes computationally intractable as the number of cells increases. This work explores an intermediate solution that exists somewhere in the solution space spanned by the ICHA and the full Hamiltonian.The solution presented in this thesis builds off of the work done by Toth et al., and studies the role that correlations play in the dynamics of QCA circuits. Using the coherence-vector formalism, we show that we can accurately capture the dynamical behaviour of QCA systems by including two-cell correlations.In order to capture the system’s interaction with the environment, we introduce a new method for computing the steady-state configurations of a QCA system using well-known stochastic methods, and use the relaxation-time approximation to drive the QCA system to these configurations. For relatively-low temperatures, we show that this approach is accurate to within a few percent, and can be computed in linear time.QCADesigner, the de facto simulation tool used in QCA research, has been used and cited in hundreds of papers since its creation in 2004. By implementing computationally accurate and efficient algorithms to the existing simulation engines present in QCADesigner, this research is expected to make a significant contribution to the future of QCA circuit design. In particular, researchers in the field will be able to identity a whole new set of design rules that will lead to more compact circuit design, realistic clocking schemes, and crosstalk-tolerant layouts. In addition, proper estimates on the power dissipation, pipelining, and limitations of room temperature operation will now be feasible for QCA circuits of any size; a huge step forward for QCA design.
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In this thesis, molecular dynamics and ab initio density functional theory/nonequilibrium Green’s function simulations are used to study the interaction between carbon nanotubes and amino acids. Firstly, rules for the proper choice of the parameters used in these simulations are established. It is demonstrated how the improper choice of these parameters (particularly the basis set used in ab initio simulations) can lead to quantitatively and qualitatively erroneous conclusions regarding the bandgap of the nanotubes.It is then shown that the major forces responsible for amino-acid adsorption on carbon nanotubes are van der Waals forces, and that hydrophobic interactions may accelerate the adsorption process, but are not necessaryfor it to occur. The mechanisms of interaction between carbon nanotubes and amino acids are elucidated. It is found that geometrical deformationsdo not play a major role in the sensing process, and that electrostatic interactions represent the major interaction mechanism between the tubes and amino acids. Fully metallic armchair tubes are found to be insensitive tovarious amino acids, while small-radius nanotubes are shown to be inadequate for sensing in aqueous media, as their response to the motion of the atoms resulting from the immersion in water is comparable to that of analyteadsorption. Short semi-metallic tubes are revealed to be sensitive to charged amino acids, and it is demonstrated that the conductance changes induced by the adsorption of the analyte in such tubes in a two-terminalconfiguration are bias-dependent. The effects of the length of the tube and adsorption-site position on the conductance of the tube are discussed. In addition, the adsorption near metallic electrodes is shown to have a negligible effect on the conductance of the tube due to the metal-induced gap states injected from the metal electrodes into the tube. Finally, the results areused to provide general guidelines for the design of carbon-nanotube-based biosensors, as well as to help explaining previously published experimental results.
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Master's Student Supervision
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
Paper, one of the oldest and greatest inventions, has and continues to play a significant role in many applications of day-to-day life. Recently, paper-based substrates have attracted significant attention in the realm of smart materials and electronics due to a growing desire for environmentally sustainable platforms, inexpensive flexible devices, and the opportunity to incorporate functional materials within the matrix. The possibility to integrate novel nanomaterials within the production methods of the paper industry is of current interest to enhance and to add new functionalities to conventional cellulose-fiber-based paper. Paper has been used as a substrate for flexible devices for several years now. However, the methods for preparing such paper-based devices are typically too complex for integration with large-scale paper manufacturing processes. This research proposes a simple process for manufacturing nanoparticle-incorporated paper-based piezoelectric composites with tailorable mechanical properties which is compatible with the conventional papermaking processes. This method utilizes a layer-by-layer process to electrostatically bind BaTiO3 particles (~300 nm diameter) to microfibrillated wood pulp with a significant particle loading of up to 72% yielding a high piezoelectric coefficient (d33) of up to 57.8 pC/N post corona poling. Such piezoelectric paper composites have potential in microelectromechanical systems (MEMS) and are used as a simple accelerometer and tactile sensor to demonstrate their efficacy in inertial sensing and touch applications. This work is part of a larger scope to develop a device which uses the functionalized paper as sensing strips for real-time particulate matter (PM) monitoring. In particular, PM2.5 (PM smaller than 2.5 μm) is targeted for detection as it is one of the main causes of air pollution related health issues, subsequently contributing to billions of dollars spent annually.
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The ability for dangling bonds (DBs) to encode bit-information and perform logic computation has been demonstrated in recent works. This was made possible by the ability to fabricate DBs with atomic precision as well as to observe and control their charge states. An observed tendency for charge reconfiguration to occur among an array of DBs as the system relaxes to the ground state was utilized to create logic wires and gates. Building on these novel advances, this thesis explores a breadth of topics surrounding the DB computation platform. A computer-aided design tool, SiQAD, was developed as part of a greater research effort to enable the rapid design and simulation of DB layouts. Among the simulation tools included in SiQAD, this work most extensively contributed to the development of multiple ground state charge configuration finders, enabling the exploration of prospective DB logic gate and circuit designs. Similarities have been drawn between DB circuit scaling properties with those of existing field-coupled nanocomputing (FCN) research, justifying the use of FCN architectures as blueprints for DB logic research. As such, this thesis proposes and analyzes DB logic implementations from the gate level to the application level.This work identifies hardware acceleration of machine learning inference as a novel prospective application on the DB computational platform. Matrix multiplication is a common operation in the inference stage of many neural network implementations, and presents a bottleneck to inference performance. Recent works have proposed, or even made commercially available, various hardware inference acceleration frameworks. Among them, the matrix multiply unit (MXU) found in Google’s Tensor Processing Unit (TPU) has been indentified to be architecturally favorable for implementation on the DB platform. This work proposes a DB adaptation of the MXU with logic layouts and clocking configurations optimized for the platform. Comparing the DB MXU to Google’s MXU, this work estimates an improvement of 1 order of magnitude in area efficiency and up to 7 orders of magnitude in power efficiency when pegged to the same clock rate.
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No abstract available.
Bioreactors capable of subjecting cells and tissues to time-varying mechanical strain are one aspect of simulating in vivo conditions. A bioreactor to impart arbitrary strain waveforms on cells or tissue scaffolds for loading conditions found in the airway was designed and developed and, in the process, it was determined that there are sources of experimental error which could invalidate bioreactor experiments if not properly mitigated. Without effective design and validation, bioreactors can impart significantly different stimuli than the assumed experimental conditions. Cyclic strain is thought to play a role in airway remodeling by mediating cytoskeletal contraction of the airway smooth muscle. In vitro experiments have demonstrated varying changes to the cytoskeleton depending on experimental conditions. Based on literature review, the strain waveform, magnitude, mechanical properties of the substrate, and anisotropy of the strain stimulus may all affect airway smooth muscle (ASM) differentiation. A bioreactor capable of imparting a broad range of strain stimulus was developed using stepper motors as actuators to allow open-loop control. Any changes in the cells subjected to cyclic strain in these bioreactors would be assumed to correlate with cyclic strain, but a poorly designed bioreactor could introduce confounding experimental stimuli which could easily invalidate the experiment. Heat generated by the actuators can overheat the cell cultures. Vibration might alter the cytoskeletal response. Strain response across the substrate can drastically vary from modeling predictions depending on the loading conditions and how the substrate has been constrained. Methods of mitigating heat generation and transfer were developed. The vibrations emitted by the two stepper motor options were evaluated. A method of mapping the substrate was developed such that nonplanar strains across the substrate surface could be characterized to validate the experimental conditions prior to testing. Finally, ASM cells were subjected to cyclic and static strain on PDMS substrates and cell realignment evaluated. Cells were noted to realign in the cyclic strain tests, as has been reported in several earlier publications, but also realigned under static strain conditions. The bioreactor design objectives were met.
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In recent years, three-dimensional (3D) printers have revolutionized the process of prototyping and manufacturing inanimate objects. Extending this technology to tissue engineering as a means of creating customized in vitro tissue constructs that mimic in vivo conditions is a relatively new idea that has the potential to transform the way biological research is conducted. Biological tissues are inherently complex 3D heterogeneous structures. Many of these tissues are made up of building blocks that vary in composition and morphology. These building blocks are organized into different levels and locations which allow them to interact with one another in unique ways such that the overall tissue structure exhibits a specific biological function. Designing and then printing 3D biological structures composed of multiple cell-encapsulated building blocks, each programmed by composition and architecture and printed using different properties, is a challenge in tissue engineering. This thesis presents the development of a 3D bioprinting software toolchain for the design and printing of software-programmable tissues. The 3D bioprinting software toolchain is built around a novel bottom-up tissue engineering design method. The Tissue Building Block Design (TBBD) method seeks to enable the assembling of complex biological structures from a set of simpler building blocks, each coded with unique material compositions, printing properties, and architectures. Algorithms were developed to generate the layer-by-layer heterogeneous process plans required to 3D print tissue models designed using the TBBD method. We evaluate the performance of our implementation of the TBBD method by analyzing execution times and performing a comparison against a more standard design approach. We then analyze and discuss the effect of design choices and printing parameters on the overall printing process and the challenges associated with our microfluidics-based method of bioprinting. We also demonstrate the functionality and asses the capabilities of the 3D bioprinting software toolchain by printing several different heterogeneous hydrogel structures using our 3D bioprinter.
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Conductive composites consist of a conductive filler dispersed within an insulating matrix. These composite materials have been known for many years and are regularly produced experimentally and commercially for a variety of applications. Novel techniques are now being found for creating composites that exhibit conductivity with less conductive filler material than classical physics suggests is sufficient if the particles are uniformly distributed. Several parties have offered physical explanations for the characteristics of their composites by incorporating a blend of classical and quantum physics but few attempts have been made to compare explanations or develop any mechanism to simulate the physics. The model presented in the present work incorporates first principles physics and semi-empirical theory to account for the distribution of particles within a composite and calculate resultant conductivity using three dimensional network analysis. Results from several model iterations are presented and they are compared with published experimental results. The model demonstrates that a random distribution of spherical particles smaller than 200 nm at 3% loading, given realistic wave function decay rates and reasonable tunnelling barrier heights, cannot explain experimentally observed conductivities in these composite materials. The final model, using a Voronoi tessellation approach, duplicates the behaviour trend of the composites being simulated and illustrates some gaps in the present material science knowledge of conductive composites.
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A dynamic strain sensor using piezoelectric zinc oxide nanowires was demonstrated for potential application in structural health monitoring. Simulations and reviews of literature determined that strain of the nanowires by uniaxial compression yields the largest piezoelectric potential and that the piezoelectric coefficient of zinc oxide nanowires is enhanced due to nanoscale size effects. The fabrication of zinc oxide nanowires on various substrates was investigated in order to determine the ideal materials and seed layer deposition methods to yield high quality vertically-aligned nanowires. Nanowires were grown on indium tin oxide-coated glass slides. The tips of the nanowires were electrically connected using poly(3,4-ethylenedioxythiophene) poly(styrenesulfonate) conductive polymer, which formed a Schottky barrier with zinc oxide allowing for the separation of charge across the nanowire-electrode junction. The piezoelectric coefficients of several fabricated devices were measured by applying pressure to the top of the nanowires and measuring the charge. Variations in performance between the different sensors were observed due to differences in the fabrication of each sensor. The highest coefficient measured was 11.5 pC/N, which is 16% higher than the bulk value for zinc oxide. The charge and voltage sensitivity to quasistatic pressure loading of the best performing sensor was calculated to be 1.32 pC/kPa and 16.7 mV/kPa. The response to clamped pressure stimulation from 1-90 kHz was evaluated using a piezoelectric stack actuator coupled with the zinc oxide nanowire sensor. The sensor showed excellent linearity to different amplitude vibrations at 1 kHz, and reasonably constant magnitude of charge output over the 1-90 kHz range for a constant vibration amplitude. The resonant frequency of the sensor and the response to free vibration could not be measured due to limitations in the available measuring equipment. The fabrication process for the nanowire sensor was found to be simple but inconsistent and could be improved by using repeatable processes such as photolithography for precisely defining electrode and seed layer geometries. The as-fabricated nanowire sensor shows promise as a dynamic strain sensor for structural health monitoring applications or pressure sensing but requires further characterization and optimization through modeling in order to compete with commercial sensors.
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Carbon nanotubes (CNTs) are a fascinating material with a diverse set of electrical and mechanical properties. In particular, they exhibit either metallic or semiconducting behaviour depending on their size and molecular configuration. Thisability to tune their electrical properties makes CNTs applicable to a diverse range of electronics applications. One of the long-standing barriers of their application is in the difficulty of controlling their position and orientation on a substrate. This is not only true for applications utilizing individual CNTs, but also for large-scaleelectronics such as sensors and displays, where CNT thin films are of great interest. The CNTs in such films are typically randomly entangled and do not exhibit the same excellent properties observed in individual CNTs. However, when the CNTs possess long range mutual alignment the electrical properties of the film can be improved. The objective of this research was to design a process for fabricating films of mutually aligned CNTs with a controlled orientation using solely inkjet printing. The innate lyotropic liquid crystallinity of highly concentrated CNT suspensions was used here as a mechanism of achieving long-range mutual alignment. The CNT orientation was found to depend on the evaporation behaviour, which was dictated by the printed pattern. A unique printing scheme was developed in order to achieve the necessary high concentrations for a lyotropic liquid crystalline phase transition, and the morphology of the resulting films was studied using scanning electron microscope (SEM) and polarized light microscopy. It was found that the alignment does not necessarily persist throughout the depth of the film, but is strikingly evident on its surface. In order to both isolate the aligned surface layerand investigate the sub-surface morphology, a method was developed for removing thin consecutive layers from the film using a polydimethylsiloxane (PDMS) stamp. SEM results indicated that the CNT film morphology was one of stacked layers, each exhibiting a decreasing degree of alignment. These results were supported by polarized light microscopy and are suggestive of a smectic liquid crystal structure.
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All-polymer flexural plate wave (FPW) sensors based on piezoelectric polyvinylidene fluoride (PVDF) thin-film with interdigital transducer (IDT) electrodes composed of poly(3,4-ethylenedioxythiophene) poly-(styrenesulfonate) (PEDOT:PSS) are studied, optimized, and assessed for their potential in various sensing applications. PVDF offers unique opportunities as a substrate material due to its low stiffness, low cost, low density, and ease of preparation compared with many other piezoelectric materials commonly used in acoustic sensing applications. Substrates are prepared using a variety of material thicknesses of PVDF through a stretching and poling process, followed by conductive IDT patterning by inkjet printing using a PEDOT:PSS-based ink. Sensor behaviour is studied using electrical and optical measurement techniques. Material and gas loading tests are performed to demonstrate gas sensing and polymer characterization applications. The devices demonstrate good adherence to analytical and FEA models, and although the high attenuation and low coupling coefficients of the substrate material reduce signal to noise ratio and quality factor, vapour sensing and polymer/absorbent material characterization applications are realized experimentally. Other factors such as environmental influences are also considered, demonstrating a very high sensitivity to temperature and humidity changes. The sensors also demonstrate high sensitivity to variations in substrate and sensing layer stiffness, reducing their effective mass sensitivity, but also increasing their potential for simultaneous mass and stiffness measurements. Parameter sensitivity studies are generated to better optimize the design and improve performance of the sensor for specific applications, suggesting benefits from thinner substrates, lower in-plane stress, and more IDT fingers.
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A disposable all-polymer micronozzle was designed and fabricated by merging the two different technologies of microfluidics and microneedles together. Polymer micronozzles (polyimide and SU-8) were fabricated using different steps of spin casting and one step of photolithography. Microfluidic devices consisting of one input channel and one output channel each with a 500µm diameter, and connected with a channel 100µm in width, were fabricated using the PDMS polydimethylsiloxane (PDMS) casting. To achieve a thin PDMS membrane, spin casting of PDMS over the mold is required. The fabricated thin PDMS microfluidic layers were bonded to polymer nozzles using oxygen plasma treatment and precisely aligning the two layers together.The resulting polymer nozzles were connected to the pressure system of a custom made inkjet printer, by the means of a plastic holder device. The holder device was designed in SolidWorks and printed using a 3D printer. Finally a solenoid actuator was attached to the setup. Different solenoid plunger tips were designed to maximize the deformation of the PDMS membrane which is used to attempt liquid ejection and printing. First the internal pressure was tuned. The effect of frequency, duty cycle and input voltage of the solenoids input pulse on the created pending droplet’s volume was characterized experimentally. The maximum displaced volume was found with actuation for a 12V input pulse with 10% duty cycle. For a 50µm nozzle diameter this volume is 4.78×10⁻¹¹ L and for a 200µm it is 3.83×10⁻¹ºL. Reducing the surface tension of water using surfactant resulted in flow of ink onto the hydrophilic plasma-treated SU-8 surface, and total surface wetting.
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