Coming dissertations at Uppsala university
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Intricacies, Endurance, and Performance Enhancement in Graphene Devices : Towards 2D electronic and spintronic circuits
Link: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-526689
Graphene, the atomically thin material of carbon atoms, first isolated experimentally in 2004, exhibits remarkable properties and holds potential for applications in quantum, electrical, and spin-based devices. The chemical vapor deposition (CVD) method enables graphene production on a large scale, merging its exceptional characteristics with scalability and high-quality implementation. Despite the extraordinary promise of CVD graphene with structural imperfections, the main challenge for graphene electronics and spintronics lies in achieving reliability at the device and circuit levels with scalable materials and interfaces. To address these, it is essential to understand the intricacies, endurance, and performance issues in graphene devices. In this thesis, to understand graphene interfaces in devices, we first explored a critical aspect of graphene's interaction with metal oxides, particularly titanium oxide (TiOx) and aluminum oxide (AlOx), and their implications for graphene-based nanoelectronic and spintronic devices. Investigating the electrical characteristics of graphene, both with and without oxides, uncovers the distinct behaviors of TiOx and AlOx when interfaced with graphene, highlighting the charge transfer-induced p-type doping and the formation of sp3 defects, traps, and impurities, especially at the AlOx/graphene interface. These findings bring new insights for graphene spintronic devices while opening possibilities for novel functionalities such as hybrid resistive switching devices. Advancing further towards van der Waals heterostructures in these studies, we could also observe the impact of monolayer MoS2 on graphene’s properties. Next, we explored how CVD graphene devices withstand high current stress to elucidate device durability and resilience. We examine the impact of extreme electric currents on channel structures and resistive tunnel barrier interfaces, focusing on their feasibility for high-capacity electronic and spintronic applications. Here, despite the polycrystalline nature of CVD graphene, we could observe the highest current density of 5.2×108 Acm-2 in graphene on Si/SiO2 substrates, elevating it further to 1.7×109 Acm-2 on diamond substrates, remarkably exceeding previous reports. Performing systematic cyclic electrical measurements, with a gradual increase in the applied high current, we could determine the limits of the reversible regime for safe device operation of both channels and contacts. This knowledge of high current limits and oxide interfaces with graphene leads to an innovative current-treated passive graphene (CTPG) system, where we passivated graphene with metal oxide and applied high current to enhance quality. This method addresses the challenge of interfacial defects and remarkably improves carrier mobility, thereby reducing Coulomb scattering while mitigating electromigration issues. The CTPG presents a scalable platform for stable nanoelectronic and spintronic circuits. The experiments and systems studied in this thesis open possibilities for the exploration of temperature-dependent charge and spin transport measurements via new heterostructures and interfaces with different material combinations.
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Deep learning assisted phase retrieval and computational methods in coherent diffractive imaging
Link: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-526527
In recent years, advances in Artificial Intelligence and experimental techniques have revolutionized the field of structural biology. X-ray crystallography and Cryo-EM have provided unprecedented insights into the structures of biomolecules, while the unexpected success of AlphaFold has opened up new avenues of investigation. However, studying the dynamics of proteins at high resolution remains a significant obstacle, especially for fast dynamics. Single-particle imaging (SPI) or Flash X-ray Imaging (FXI) is an emerging technique that may enable the mapping of the conformational landscape of biological molecules at high resolution and fast time scale. This thesis discusses the potential of SPI/FXI, its challenges, recent experimental successes, and the advancements driving its development. In particular, machine learning and neural networks could play a vital role in fostering data analysis and improving SPI/FXI data processing. In Paper I, we discuss the problem of noise and detector masks in collecting FXI data. I simulated a dataset of diffraction patterns and used it to train a Convolutional Neural Network (U-Net) to restore data by denoising and filling in detector masks. As a natural continuation of this work, I trained another machine learning model in Paper II to estimate 2D protein densities from diffraction intensities. In the final chapter, corresponding to Paper III, we discuss another experimental method, time-resolved Small Angle X-ray Scattering (SAXS), and a new algorithm recently developed for SAXS data, the DENsity from Solution Scattering (DENSS) algorithm. I discuss the potential of DENSS in time-resolved SAXS and its application for structural fitting of AsLOV2, a Light-Oxygen-Voltage (LOV) protein domain from Avena sativa.
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Aspects of generalised joint hypermobility on pelvic girdle pain and physical disability during and after pregnancy
Link: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-526724
Pelvic girdle pain is common during pregnancy, but its multifactorial mechanisms are not completely understood. Generalised joint hypermobility is associated with musculoskeletal pain due to fragile connective tissue. The increased laxity of ligaments during pregnancy may be related to pelvic girdle pain. Women with fragile connective tissue may be more prone to developing pain during pregnancy. The overall aim was to examine whether generalised joint hypermobility increases the likelihood of experiencing pelvic girdle pain and physical disability during and after pregnancy. In Study I, the inter- and intra-rater reliability of joint mobility measurements in 49 adults were investigated, evaluating 12 joints. Study II explored the association between self-reported generalised joint hypermobility and pelvic girdle pain during pregnancy involving 2,217 women. In study III, the association between clinically assessed generalised joint hypermobility and pelvic girdle pain during and after pregnancy were examined, involving 356 women. Study IV focused on the development of physical disability from early pregnancy to nine months postpartum considering pelvic girdle pain, generalised joint hypermobility and overweight, and wheather the onset of pelvic girdle pain and the level of physical disability during pregnancy was associated with physical disability nine months postpartum. The findings in this thesis indicated good-to-excellent inter- and intra-rater reliability in most mobility measurements. Women who self-reported generalised joint hypermobility exhibited higher odds of experiencing pelvic-girdle pain, particularly in early pregnancy. Clinically assessed generalised joint hypermobility was associated with increased pain intensity in early pregnancy and, when combined with overweight, an increased risk of pelvic girdle pain. Physical disability increased during pregnancy irrespective of pelvic girdle pain status, but the overall prognosis was positive. Pelvic girdle pain had the strongest influence on disability, in contrast to generalized joint hypermobility and overweight. Early-onset pelvic girdle pain during pregnancy and a higher disability index during pregnancy were associated with physical disability nine months postpartum. Women experiencing pelvic girdle pain early in pregnancy may benefit from evaluation for generalised joint hypermobility, as they may experience more intense pain. It is crucial to develop methods for preventing and managing pelvic girdle pain, given its strong association with physical disability during and after pregnancy. Early-onset pelvic girdle pain and a higher disability index during pregnancy were predictors of physical disability nine months postpartum.