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The free-base tetra-tolyl-porphyrin and the corresponding cobalt and iron porphyrin complexes were synthesized and characterized to show that this class of compound can be promising, tunable catalysts for carbon dioxide reduction. During cyclic voltammetry experiments, the iron porphyrin showed an on-set of ‘catalytic current’ at an earlier potential than the cobalt porphyrin’s in organic solutions gassed with carbon dioxide. The cobalt porphyrin yielded larger catalytic currents, but at the same potential as the electrode. This difference, along with the significant changes in the porphyrin’s electronic, optical and redox properties, showed that its capabilities for carbon dioxide reduction can be controlled by metal ions, allotting it unique opportunities for applications in solar fuels catalysis and photochemical reactions.
ContributorsSkibo, Edward Kim (Author) / Moore, Gary (Thesis director) / Woodbury, Neal (Committee member) / School of Molecular Sciences (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
ABSTRACT Peptide microarrays may prove to be a powerful tool for proteomics research and clinical diagnosis applications. Fodor et al. and Maurer et al. have shown proof-of-concept methods of light- and electrochemically-directed peptide microarray fabrication on glass and semiconductor microchips respectively. In this work, peptide microarray fabrication based on the abovementioned techniques were optimized. In addition, MALDI mass spectrometry based peptide synthesis characterization on semiconductor microchips was developed and novel applications of a CombiMatrix (CBMX) platform for electrochemically controlled synthesis were explored. We have investigated performance of 2-(2-nitrophenyl)propoxycarbonyl (NPPOC) derivatives as photo-labile protecting group. Specifically, influence of substituents on 4 and 5 positions of phenyl ring of NPPOC group on the rate of photolysis and the yield of the amine was investigated. The results indicated that substituents capable of forming a π-network with the nitro group enhanced the rate of photolysis and yield. Once such properly substituted NPPOC groups were used, the rate of photolysis/yield depended on the nature of protected amino group indicating that a different chemical step during the photo-cleavage process became the rate limiting step. We also focused on electrochemically-directed parallel synthesis of high-density peptide microarrays using the CBMX technology referred to above which uses electrochemically generated acids to perform patterned chemistry. Several issues related to peptide synthesis on the CBMX platform were studied and optimized, with emphasis placed on the reactions of electro-generated acids during the deprotection step of peptide synthesis. We have developed a MALDI mass spectrometry based method to determine the chemical composition of microarray synthesis, directly on the feature. This method utilizes non-diffusional chemical cleavage from the surface, thereby making the chemical characterization of high-density microarray features simple, accurate, and amenable to high-throughput. CBMX Corp. has developed a microarray reader which is based on electro-chemical detection of redox chemical species. Several parameters of the instrument were studied and optimized and novel redox applications of peptide microarrays on CBMX platform were also investigated using the instrument. These include (i) a search of metal binding catalytic peptides to reduce overpotential associated with water oxidation reaction and (ii) an immobilization of peptide microarrays using electro-polymerized polypyrrole.
ContributorsKumar, Pallav (Author) / Woodbury, Neal (Thesis advisor) / Allen, James (Committee member) / Johnston, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Meditation app usage is associated with decreases in stress, anxiety, and depression symptoms. Many meditation app subscribers, however, quickly abandon or reduce their app usage. This dissertation presents three manuscripts which 1) determined the behavioral, demographic, and socioeconomic factors associated with the abandonment of a meditation app, Calm, during the COVID-19 pandemic, 2) determined which participant characteristics predicted meditation app usage in the first eight weeks after subscribing, and 3) determined if changes in stress, anxiety, and depressive symptoms from baseline to Week 8 predicted meditation app usage from Weeks 8-16. In Manuscript 1, a survey was distributed to Calm subscribers in March 2020 that assessed meditation app behavior and meditation habit strength, and demographic information. Cox proportional hazards regression models were estimated to assess time to app abandonment. In Manuscript 2, new Calm subscribers completed a baseline survey on participants’ demographic and baseline mental health information and app usage data were collected over 8 weeks. In Manuscript 3, new Calm subscribers completed a baseline and Week 8 survey on demographic and mental health information. App usage data were collected over 16 weeks. Regression models were used to assess app usage for Manuscripts 2 and 3. Findings from Manuscript 1 suggest meditating after an existing routine decreased risk of app abandonment for pre-pandemic subscribers and for pandemic subscribers. Additionally, meditating “whenever I can” decreased risk of abandonment among pandemic subscribers. No behavioral factors were significant predictors of app abandonment among the long-term subscribers. Findings from Manuscript 2 suggest men had more days of meditation than women. Mental health diagnosis increased average daily meditation minutes. Intrinsic motivation for meditation increased the likelihood of completing any meditation session, more days with meditation sessions, and more average daily meditation minutes. Findings from Manuscript 3 suggest improvements in stress increased average daily meditation minutes. Improvements in depressive symptoms decreased daily meditation minutes. Evidence from this three-manuscript dissertation suggests meditation cue, time of day, motivation, symptom changes, and demographic and socioeconomic variables may be used to predict meditation app usage.
ContributorsSullivan, Mariah (Author) / Stecher, Chad (Thesis advisor) / Huberty, Jennifer (Committee member) / Buman, Matthew (Committee member) / Larkey, Linda (Committee member) / Chung, Yunro (Committee member) / Arizona State University (Publisher)
Created2022
Description
Traditional Reinforcement Learning (RL) assumes to learn policies with respect to reward available from the environment but sometimes learning in a complex domain requires wisdom which comes from a wide range of experience. In behavior based robotics, it is observed that a complex behavior can be described by a combination of simpler behaviors. It is tempting to apply similar idea such that simpler behaviors can be combined in a meaningful way to tailor the complex combination. Such an approach would enable faster learning and modular design of behaviors. Complex behaviors can be combined with other behaviors to create even more advanced behaviors resulting in a rich set of possibilities. Similar to RL, combined behavior can keep evolving by interacting with the environment. The requirement of this method is to specify a reasonable set of simple behaviors. In this research, I present an algorithm that aims at combining behavior such that the resulting behavior has characteristics of each individual behavior. This approach has been inspired by behavior based robotics, such as the subsumption architecture and motor schema-based design. The combination algorithm outputs n weights to combine behaviors linearly. The weights are state dependent and change dynamically at every step in an episode. This idea is tested on discrete and continuous environments like OpenAI’s “Lunar Lander” and “Biped Walker”. Results are compared with related domains like Multi-objective RL, Hierarchical RL, Transfer learning, and basic RL. It is observed that the combination of behaviors is a novel way of learning which helps the agent achieve required characteristics. A combination is learned for a given state and so the agent is able to learn faster in an efficient manner compared to other similar approaches. Agent beautifully demonstrates characteristics of multiple behaviors which helps the agent to learn and adapt to the environment. Future directions are also suggested as possible extensions to this research.
ContributorsVora, Kevin Jatin (Author) / Zhang, Yu (Thesis advisor) / Yang, Yezhou (Committee member) / Praharaj, Sarbeswar (Committee member) / Arizona State University (Publisher)
Created2021
Description
Computational models have long been used to describe and predict the outcome of complex immunological processes. The dissertation work described here centers on the construction of multiscale computational immunology models that derives biological insights at the population, systems, and atomistic levels. First, SARS-CoV-2 mortality is investigated through the lens of the predicted robustness of CD8+ T cell responses in 23 different populations. The robustness of CD8+ T cell responses in a given population was modeled by predicting the efficiency of endemic MHC-I protein variants to present peptides derived from SARS-CoV-2 proteins to circulating T cells. To accomplish this task, an algorithm, called EnsembleMHC, was developed to predict viral peptides with a high probability of being recognized by CD T cells. It was discovered that there was significant variation in the efficiency of different MHC-I protein variants to present SARS-CoV-2 derived peptides, and countries enriched with variants with high presentation efficiency had significantly lower mortality rates. Second, a biophysics-based MHC-I peptide prediction algorithm was developed. The MHC-I protein is the most polymorphic protein in the human genome with polymorphisms in the peptide binding causing striking changes in the amino acid compositions, or binding motifs, of peptide species capable of stable binding. A deep learning model, coined HLA-Inception, was trained to predict peptide binding using only biophysical properties, namely electrostatic potential. HLA-Inception was shown to be extremely accurate and efficient at predicting peptide binding motifs and was used to determine the peptide binding motifs of 5,821 MHC-I protein variants. Finally, the impact of stalk glycosylations on NL63 protein dynamics was investigated. Previous data has shown that coronavirus crown glycans play an important role in immune evasion and receptor binding, however, little is known about the role of the stalk glycans. Through the integration of computational biology, experimental data, and physics-based simulations, the stalk glycans were shown to heavily influence the bending angle of spike protein, with a particular emphasis on the glycan at position 1242. Further investigation revealed that removal of the N1242 glycan significantly reduced infectivity, highlighting a new potential therapeutic target. Overall, these investigations and associated innovations in integrative modeling.
ContributorsWilson, Eric Andrew (Author) / Anderson, Karen (Thesis advisor) / Singharoy, Abhishek (Thesis advisor) / Woodbury, Neal (Committee member) / Sulc, Petr (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation research project developed as an urgent response to physical inactivity, which has resulted in increased rates of obesity, diabetes, and metabolic disease worldwide. Incorporating enough daily physical activity (PA) is challenging for most people. This research aims to modulate the brain's reward systems to increase motivation for PA and, thus, slow the rapid increase in sedentary lifestyles. Transcranial direct current stimulation (tDCS) involves brain neuromodulation by facilitating or inhibiting spontaneous neural activity. tDCS applied to the dorsolateral prefrontal cortex (DLPFC) increases dopamine release in the striatum, an area of the brain involved in the reward–motivation pathways. I propose that a repeated intervention, consisting of tDCS applied to the DLPFC followed by a short walking exercise stimulus, enhances motivation for PA and daily PA levels in healthy adults. Results showed that using tDCS followed by short-duration walking exercise may enhance daily PA levels in low-physically active participants but may not have similar effects on those with higher levels of daily PA. Moreover, there was a significant effect on increasing intrinsic motivation for PA in males, but there were no sex-related differences in PA. These effects were not observed during a 2-week follow-up period of the study after the intervention was discontinued. Further research is needed to confirm and continue exploring the effects of tDCS on motivation for PA in larger cohorts of sedentary populations. This novel research will lead to a cascade of new evidence-based technological applications that increase PA by employing approaches rooted in biology.
ContributorsRuiz Tejada, Anaissa (Author) / Katsanos, Christos (Thesis advisor) / Neisewander, Janet (Committee member) / Sadleir, Rosalind (Committee member) / Buman, Matthew (Committee member) / Arizona State University (Publisher)
Created2023
Description
The relationship between sleep and physical activity is an area of growing scientific interest, particularly in the context of older adults. The importance of examining long sleep duration and its influence on physical activity in this demographic becomes increasingly relevant given rising healthcare costs. This dissertation aims to investigate this intricate relationship via secondary analysis by examining the effects of moderate time-in-bed (TIB) restriction (60 minutes per night)) on various intensities of physical activity (sedentary, light, moderate, vigorous, moderate-vigorous physical activity) in older adults classified as long sleepers and average duration sleepers. It was hypothesized that moderate TIB restriction would result in differential changes in physical activity levels across various intensities, with long sleepers exhibiting increased physical activity and average sleepers displaying decreased activity, potentially influenced by alterations in TST (total sleep time) and SE (sleep efficiency). Utilizing a randomized controlled trial design, this study examined the effect of treatment changes in objectively measures activity (waist actigraphy) and subjects physical activity levels as measured by the Godin Leisure-Time Exercise Questionnaire . Eligible participants were long sleepers (sleeping > 9 hours per night) and average sleepers (sleeping 7-9 hours per night). Both types of sleepers were either randomized to TIB restriction or asked to maintain their average sleep patterns. Mean TIB restriction compared with baseline was 39.5 minutes in average sleepers and 52.9 minutes in long sleepers randomized to TIB restriction . Contrary to the original hypothesis, no significant effect of TIB restriction was observed across all physical activity levels in either long sleepers or average sleepers. However, a notable association was found between increased sleep efficiency (+0.09% [SD = ± 4.64%]) and light physical activity (±31 minutes [SD = ± 104.81, R=0.445, P < 0.007]) in long sleepers undergoing TIB restriction. While this study presents several methodological limitations, including its nature as a secondary analysis and the less-than-intended achievement of TIB restriction, it adds a valuable layer to the existing body of research on sleep and physical activity in older adults. The findings suggest that moderate TIB restriction may not be sufficiently impactful to change behavior in physical activity levels, thus highlighting the need for more nuanced, targeted research in this domain.
ContributorsPerry, Christopher (Author) / Youngstedt, Shawn D (Thesis advisor) / Petrov, Megan (Committee member) / Swan, Pamela (Committee member) / Buman, Matthew (Committee member) / Ringenbach, Shannon (Committee member) / Arizona State University (Publisher)
Created2023
Description
The purpose of this investigation was to evaluate the influence of tap water safety perceptions on plain water intake (PWI) and hydration status in US Latinx adults. Participants (n=492; age, 28±7 y; 37.4% female) completed an Adapted Survey of Water Issues in Arizona and household watersecurity experience-based scales. A sub-sample (n=55; age, 33±14 y; body mass index, 27.77±6.60 kg·m2) completed dietary recalls on two weekdays and one weekend day via Automated Self-Administered 24-hour Dietary Assessment Tool to determine average PWI and total water intake (TWI). A 24-h urine sample was collected on one recall day and analyzed for urine osmolality (UOsm). Binary logistic regression determined odds ratios (OR) for the odds of perceiving tap water to be unsafe. Hierarchical linear regression was employed with 24-h UOsm and PWI as primary outcomes for the sub-sample. Overall, 51.2% of all participants and 52.7% of the sub-sample mistrust their tap water safety. The odds of mistrusting tap water were significantly greater (P<0.05) for each additional favorable perception of bottled over tap water (OR=1.94, 95% CI=1.50, 2.50), each additional negative home tap water experience (OR=1.32, 95% CI=1.12, 1.56), each additional use of alternatives and/or modifications to home tap water (OR=1.25, 95% CI=1.04, 1.51), and decreased water quality and acceptability (OR=1.21, 95% CI=1.01, 1.45). The odds of mistrusting tap water were significantly lower (P<0.05) for those whose primary source of drinking water is the public supply (municipal) (OR=0.07, 95% CI=0.01, 0.63) and for those with decreased water access (OR=0.56, 95% CI=0.48, 0.66). There were no differences (n=55, P>0.05) in TWI (2,678±1,139 mL), PWI (1,357±971), or 24-h UOsm (460±234 mosm·kg-1). Tap water safety perceptions did not significantly explain variance in PWI or 24-h UOsm (P > 0.05). In conclusion, Latinx mistrust in tap water safety is prevalent. Mistrust appears to be influenced by organoleptic perceptions and to lead to reliance on alternatives to the home drinking water system. Perceptions of tap water safety do not appear to be related to PWI, TWI, or hydration status in Latinx adults.
ContributorsColburn, Abigail (Author) / Kavouras, Stavros (Thesis advisor) / Buman, Matthew (Committee member) / Ohri-Vachaspati, Punam (Committee member) / Vega-Lopez, Sonia (Committee member) / Wutich, Amber (Committee member) / Arizona State University (Publisher)
Created2022
Description
A remarkable phenomenon in contemporary physics is quantum scarring in classically chaoticsystems, where the wave functions tend to concentrate on classical periodic orbits. Quantum
scarring has been studied for more than four decades, but the problem of efficiently detecting
quantum scars has remained to be challenging, relying mostly on human visualization of
wave function patterns. This paper develops a machine learning approach to detecting
quantum scars in an automated and highly efficient manner. In particular, this paper exploits Meta
learning. The first step is to construct a few-shot classification algorithm, under the
requirement that the one-shot classification accuracy be larger than 90%. Then propose a
scheme based on a combination of neural networks to improve the accuracy. This paper shows that
the machine learning scheme can find the correct quantum scars from thousands images of
wave functions, without any human intervention, regardless of the symmetry of the underlying
classical system. This will be the first application of Meta learning to quantum systems. Interacting spin networks are fundamental to quantum computing. Data-based tomography oftime-independent spin networks has been achieved, but an open challenge is to ascertain the
structures of time-dependent spin networks using time series measurements taken locally
from a small subset of the spins. Physically, the dynamical evolution of a spin network under
time-dependent driving or perturbation is described by the Heisenberg equation of motion.
Motivated by this basic fact, this paper articulates a physics-enhanced machine learning framework
whose core is Heisenberg neural networks. This paper demonstrates that, from local measurements, not only the local Hamiltonian can be recovered but the Hamiltonian reflecting the interacting structure of the whole system can
also be faithfully reconstructed. Using Heisenberg neural machine on spin networks of a
variety of structures. In the extreme case where measurements are taken from only one spin,
the achieved tomography fidelity values can reach about 90%. The developed machine
learning framework is applicable to any time-dependent systems whose quantum dynamical
evolution is governed by the Heisenberg equation of motion.
ContributorsHan, Chendi (Author) / Lai, Ying-Cheng (Thesis advisor) / Yu, Hongbin (Committee member) / Dasarathy, Gautam (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2022
Description
Few-layer black phosphorous (FLBP) is one of the most important two-dimensional (2D) materials due to its strongly layer-dependent quantized bandstructure, which leads to wavelength-tunable optical and electrical properties. This thesis focuses on the preparation of stable, high-quality FLBP, the characterization of its optical properties, and device applications.Part I presents an approach to preparing high-quality, stable FLBP samples by combining O2 plasma etching, boron nitride (BN) sandwiching, and subsequent rapid thermal annealing (RTA). Such a strategy has successfully produced FLBP samples with a record-long lifetime, with 80% of photoluminescence (PL) intensity remaining after 7 months. The improved material quality of FLBP allows the establishment of a more definitive relationship between the layer number and PL energies.
Part II presents the study of oxygen incorporation in FLBP. The natural oxidation formed in the air environment is dominated by the formation of interstitial oxygen and dangling oxygen. By the real-time PL and Raman spectroscopy, it is found that continuous laser excitation breaks the bonds of interstitial oxygen, and free oxygen atoms can diffuse around or form dangling oxygen under low heat. RTA at 450 °C can turn the interstitial oxygen into dangling oxygen more thoroughly. Such oxygen-containing samples show similar optical properties to the pristine BP samples. The bandgap of such FLBP samples increases with the concentration of the incorporated oxygen.
Part III deals with the investigation of emission natures of the prepared samples. The power- and temperature-dependent measurements demonstrate that PL emissions are dominated by excitons and trions, with a combined percentage larger than 80% at room temperature. Such measurements allow the determination of trion and exciton binding energies of 2-, 3-, and 4-layer BP, with values around 33, 23, 15 meV for trions and 297, 276, 179 meV for excitons at 77K, respectively.
Part IV presents the initial exploration of device applications of such FLBP samples. The coupling between photonic crystal cavity (PCC) modes and FLBP's emission is realized by integrating the prepared sandwich structure onto 2D PCC. Electroluminescence has also been achieved by integrating such materials onto interdigital electrodes driven by alternating electric fields.
ContributorsLi, Dongying (Author) / Ning, Cun-Zheng (Thesis advisor) / Vasileska, Dragica (Committee member) / Lai, Ying-Cheng (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2022