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Description
This thesis presents a study of Boron Nitride (BN) and Copper (Cu)/BN multilayer thin films in terms of synthesis, chemical, structural, morphological, and mechanical properties characterization.
In this study, the influence of Ar/N₂ flow rate in synthesizing stoichiometric BN thin films via magnetron sputtering was investigated initially. Post magnetron sputtering, the crystalline nature and B:N stoichiometric ratio of deposited thin films were investigated by X-ray diffraction (XRD) and X-ray Photoelectron Spectroscopy (XPS) respectively. Thicknesses revealed by ellipsometry analysis for nearly stoichiometric B:N thin films and their corresponding deposition times were used for estimating BN interlayer deposition times during the deposition of Cu/BN multilayer thin films. To characterize the microstructure of the synthesized Cu/BN multilayer thin films, XRD and scanning electron microscopy (SEM) have been used. Finally, a comparison of nanoindentation measurements on pure Cu and Cu/BN multilayer thin films having different number of BN interlayers were used for studying the influence of BN interlayers on improving mechanical properties such as hardness and elastic modulus.
The results show that the stoichiometry of BN thin films is dependent on the Ar/N₂ flow rate during magnetron sputtering. An optimal Ar/N₂ flow rate of 13:5 during deposition was required to achieve an approximately 1:1 B:N stoichiometry. Grazing incidence and powder XRD analysis on these stoichiometric BN thin films deposited at room temperature did not reveal a phase match when compared to hexagonal boron nitride (h-BN) and cubic boron nitride (c-BN) reference XRD patterns. For a BN thin film deposition time of 5 hours, a thickness of approximately 40 nm was achieved, as revealed by ellipsometry. XRD and microstructure analysis using scanning electron microscopy (SEM) on pure Cu and Cu/BN thin films showed that the Cu grain size in Cu/BN thin films is much finer than pure Cu thin films. Interestingly, nanoindentation measurements on pure Cu and Cu/BN thin films having a similar overall thickness demonstrated that hardness and Young’s modulus of the films were improved significantly when BN interlayers are present.
ContributorsCaner, Sumeyye (Author) / Rajagopalan, Jagannathan (Thesis advisor) / Oswald, Jay (Committee member) / Solanki, Kiran (Committee member) / Arizona State University (Publisher)
Created2023
Description
This project is centered around a decade-old video game called League of Legends, which is one of the most popular video games in esports. Due to its nature of being a complex team-based strategy game, intuitive human predictions of the game’s outcome are relatively unreliable. Many approaches have been adopted to assist intuitive human predictions in traditional team-based sports, such as the Least Squares Method and various supervised machine learning algorithms. These methods have been significantly outperforming human predictions. The objective of this research is, hence, to test whether the predictive models generated using these methods can achieve a similar level of reliability in a more complex game like League of Legends.
ContributorsWang, Jiahao (Author) / Zandieh, Michelle (Thesis director) / Lee, Inyoung (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / College of Integrative Sciences and Arts (Contributor)
Created2023-12
Description
The goal of this project is to measure the effects of the use of dynamic circuit technology within quantum neural networks. Quantum neural networks are a type of neural network that utilizes quantum encoding and manipulation techniques to learn to solve a problem using quantum or classical data. In their current form these neural networks are linear in nature, not allowing for alternative execution paths, but using dynamic circuits they can be made nonlinear and can execute different paths. We measured the effects of these dynamic circuits on the training time, accuracy, and effective dimension of the quantum neural network across multiple trials to see the impacts of the nonlinear behavior.
ContributorsLynch, Brian (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-12
Description
Scientific research encompasses a variety of objectives, including measurement, making predictions, identifying laws, and more. The advent of advanced measurement technologies and computational methods has largely automated the processes of big data collection and prediction. However, the discovery of laws, particularly universal ones, still heavily relies on human intellect. Even with human intelligence, complex systems present a unique challenge in discerning the laws that govern them. Even the preliminary step, system description, poses a substantial challenge. Numerous metrics have been developed, but universally applicable laws remain elusive. Due to the cognitive limitations of human comprehension, a direct understanding of big data derived from complex systems is impractical. Therefore, simplification becomes essential for identifying hidden regularities, enabling scientists to abstract observations or draw connections with existing knowledge. As a result, the concept of macrostates -- simplified, lower-dimensional representations of high-dimensional systems -- proves to be indispensable. Macrostates serve a role beyond simplification. They are integral in deciphering reusable laws for complex systems. In physics, macrostates form the foundation for constructing laws and provide building blocks for studying relationships between quantities, rather than pursuing case-by-case analysis. Therefore, the concept of macrostates facilitates the discovery of regularities across various systems. Recognizing the importance of macrostates, I propose the relational macrostate theory and a machine learning framework, MacroNet, to identify macrostates and design microstates. The relational macrostate theory defines a macrostate based on the relationships between observations, enabling the abstraction from microscopic details. In MacroNet, I propose an architecture to encode microstates into macrostates, allowing for the sampling of microstates associated with a specific macrostate. My experiments on simulated systems demonstrate the effectiveness of this theory and method in identifying macrostates such as energy. Furthermore, I apply this theory and method to a complex chemical system, analyzing oil droplets with intricate movement patterns in a Petri dish, to answer the question, ``which combinations of parameters control which behavior?'' The macrostate theory allows me to identify a two-dimensional macrostate, establish a mapping between the chemical compound and the macrostate, and decipher the relationship between oil droplet patterns and the macrostate.
ContributorsZhang, Yanbo (Author) / Walker, Sara I (Thesis advisor) / Anbar, Ariel (Committee member) / Daniels, Bryan (Committee member) / Das, Jnaneshwar (Committee member) / Davies, Paul (Committee member) / Arizona State University (Publisher)
Created2023
The Intercoupling of Thermal Transport and Mechanical Properties in Colloidal Nanocrystal Assemblies
Description
Colloidal nanocrystals (NCs) are promising candidates for a wide range of applications (electronics, optoelectronics, photovoltaics, thermoelectrics, etc.). Mechanical and thermal transport property play very important roles in all of these applications. On one hand, mechanical robustness and high thermal conductivity are desired in electronics, optoelectronics, and photovoltaics. This improves thermomechanical stability and minimizes the temperature rise during the device operation. On the other hand, low thermal conductivity is desired for higher thermoelectric figure of merit (ZT). This dissertation demonstrates that ligand structure and nanocrystal ordering are the primary determining factors for thermal transport and mechanical properties in colloidal nanocrystal assemblies. To eliminate the mechanics and thermal transport barrier, I first propose a ligand crosslinking method to improve the thermal transport across the ligand-ligand interface and thus increasing the overall thermal conductivity of NC assemblies. Young’s modulus of nanocrystal solids also increases simultaneously upon ligand crosslinking. My thermal transport measurements show that the thermal conductivity of the iron oxide NC solids increases by a factor of 2-3 upon ligand crosslinking. Further, I demonstrate that, though with same composition, long-range ordered nanocrystal superlattices possess higher mechanical and thermal transport properties than disordered nanocrystal thin films. Experimental measurements along with theoretical modeling indicate that stronger ligand-ligand interaction in NC superlattice accounts for the improved mechanics and thermal transport. This suggests that NC/ligand arranging order also plays important roles in determining mechanics and thermal transport properties of NC assemblies. Lastly, I show that inorganic ligand functionalization could lead to tremendous mechanical enhancement (a factor of ~60) in NC solids. After ligand exchange and drying, the short inorganic Sn2S64- ligands dissociate into a few atomic layers of amorphous SnS2 at room temperature and interconnects the neighboring NCs. I observe a reverse Hall-Petch relation as the size of NC decreases. Both atomistic simulations and analytical phase mixture modeling identify the grain boundaries and their activities as the mechanic bottleneck.
ContributorsWang, Zhongyong (Author) / Wang, Robert RW (Thesis advisor) / Wang, Liping LW (Committee member) / Newman, Nathan NN (Committee member) / Arizona State University (Publisher)
Created2021
Description
Over the past few years, research into the use of doped diamond in electronics has seen an exponential growth. In the course of finding ways to reduce the contact resistivity, nanocarbon materials have been an interesting focus. In this work, the transfer length method (TLM) was used to investigate Ohmic contact properties using the tri-layer stack Ti/Pt/Au on nitrogen-doped n-type conducting nano-carbon (nanoC) layers grown on (100) diamond substrates. The nanocarbon material was characterized using Secondary Ion Mass Spectrometry (SIMS), Scanning electron Microscopy (SEM) X-ray diffraction (XRD), Raman scattering and Hall effect measurements to probe the materials characteristics. Room temperature electrical measurements were taken, and samples were annealed to observe changes in electrical conductivity. Low specific contact resistivity values of 8 x 10^-5 Ωcm^2 were achieved, which was almost two orders of magnitude lower than previously reported values. The results were attributed to the increased nitrogen incorporation, and the presence of electrically active defects which leads to an increase in conduction in the nanocarbon. Further a study of light phosphorus doped layers using similar methods with Ti/Pt/Au contacts again yielded a low contact resistivity of about 9.88 x 10^-2 Ωcm^2 which is an interesting prospect among lightly doped diamond films for applications in devices such as transistors. In addition, for the first time, hafnium was substituted for Ti in the contact stack (Hf/Pt/Au) and studied on nitrogen doped nanocarbon films, which resulted in low contact resistivity values on the order of 10^-2 Ωcm^2. The implications of the results were discussed, and recommendations for improving the experimental process was outlined. Lastly, a method for the selective area growth of nanocarbon was developed and studied and the results provided an insight into how different characterizations can be used to confirm the presence of the nanocrystalline diamond material, the limitations due to the film thickness was explored and ideas for future work was proposed.
ContributorsAmonoo, Evangeline Abena (Author) / Thornton, Trevor (Thesis advisor) / Alford, Terry L (Thesis advisor) / Anwar, Shahriar (Committee member) / Theodore, David (Committee member) / Arizona State University (Publisher)
Created2023
Description
Artificial intelligence (AI) and machine learning (ML) algorithms are revolutionizing the field of healthcare by offering new opportunities for improved diagnosis and treatment planning. These technologies have the potential to transform the way medical professionals approach patient care by analyzing vast amounts of data, identifying patterns, and making predictions. This overview highlights the current state of research and development in the field of AI and ML for diagnosis and treatment planning, as well as explore the ethical benefits and challenges associated with their implementation.
ContributorsShankar, Kruthy (Author) / Arquiza, Jose (Thesis director) / Sobrado, Michael (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2024-05
ContributorsShankar, Kruthy (Author) / Arquiza, Jose (Thesis director) / Sobrado, Michael (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2024-05
ContributorsShankar, Kruthy (Author) / Arquiza, Jose (Thesis director) / Sobrado, Michael (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2024-05
Description
Integrating advanced materials with innovative manufacturing techniques has propelled the field of additive manufacturing into new frontiers. This study explores the rapid 3D printing of reduced graphene oxide/polymer composites using Micro-Continuous Liquid Interface Production (μCLIP), a cutting-edge technology known for its speed and precision. A printable ink is formulated with reduced graphene oxide for μCLIP-based 3D printing. The research focuses on optimizing μCLIP parameters to fabricate reduced graphene composites efficiently. The study encompasses material synthesis, ink formulation and explores the resulting material's structural and electrical properties. The marriage of graphene's unique attributes with the rapid prototyping capabilities of μCLIP opens new avenues for scalable and rapid production in applications such as energy storage, sensors, and lightweight structural components. This work contributes to the evolving landscape of advanced materials and additive manufacturing, offering insights into the synthesis, characterization, and potential applications of 3D printed reduced graphene oxide/polymercomposites.
ContributorsRavishankar, Chayaank Bangalore (Author) / Chen, Xiangfan (Thesis advisor) / Bhate, Dhruv (Committee member) / Azeredo, Bruno (Committee member) / Arizona State University (Publisher)
Created2024