Cyclical chemical looping involves the thermal reduction of metal oxide to release O2 at high temperatures, followed by its oxidation using O-containing molecules like O2, H2O, or CO2. This process is a promising method for solar thermochemical water splitting (STCH), oxygen separation, and thermochemical energy storage (TCES). The efficiency and economic viability of this process hinge on the thermodynamics of metal oxide reduction. This dissertation presents innovative methods to enhance the performance of these processes, with a specific focus on STCH and TCES by advancing thermodynamic characterization and screening of potential metal oxides, thereby reducing H2 costs.A novel CALPHAD approach, the CrossFit Compound Energy Formalism (CEF), integrates theoretical (density functional theory) and experimental (thermogravimetric) data to develop thermodynamic models for desired materials. The CrossFit-CEF was applied to BaxSr1-xFeO3-δ identifying matching thermodynamics and off-stoichiometric values to literature (~100-180 kJ/mol O2 reduction enthalpies across the BaxSr1-xFeO3-δ compositional range). Comparisons with the traditional van ‘t Hoff thermodynamic extraction technique reveal that the CrossFit-CEF method significantly outperforms conventional methods. For instance, the CEF method was employed to extract thermodynamic data for CaFexMn1-xO3-δ and identify optimal TCES CaFexMn1-xO3-δ compositions. The CrossFit-CEF method found the same thermodynamic trends on less than half the data utilized in a van ‘t Hoff approach and determined that the optimal CaFexMn1-xO3-δ composition had the minimal Fe concentration synthesized (x=0.625), achieving ~60 kJ/mol TCES. Bayesian Inference was employed was employed to expedite data collection. When combined with the CrossFit-CEF method, Bayesian Inference assesses the likelihood that the current model accurately describes the data, providing confidence intervals for the model. This approach reduces the amount of data needed for accurate thermodynamic modeling by 50%. Finally, the CrossFit-CEF and Bayesian methods are integrated into a system-level STCH model to optimize and accelerate materials design for specific plant operating conditions. Overall, this dissertation introduces methods that yield more accurate thermodynamic models with reduced data requirements. The time saved in data collection enables screening of more materials, expediting material identification and optimization. The materials identified through these techniques are expected to enhance chemical looping cycles, leading to increased H2 production efficiency and reduced costs.

Anthropogenic processes have increased the concentration of toxic Se, As and N in water. Oxo-anions of these species are poisonous to aquatic and terrestrial life. Current remediation techniques have low selectivity towards their removal. Understanding the chemistry and physics which control oxo-anion adsorption on metal oxide and the catalytic nitrate reduction to inform improved remediation technologies can be done using Density functional theory (DFT) calculations. The adsorption of selenate, selenite, and arsenate was investigated on the alumina and hematite to inform sorbent design strategies. Adsorption energies were calculated as a function of surface structure, composition, binding motif, and pH within a hybrid implicit-explicit solvation strategy. Correlations between surface property descriptors including water network structure, cationic species identity, and facet and the adsorption energies of the ions show that the surface water network controls the adsorption energy more than any other, including the cationic species of the metal-oxide. Additionally, to achieve selectivity for selenate over sulphate, differences in their electronic structure must be exploited, for example by the reduction of selenate to selenite by Ti3+ cations. Thermochemical or electrochemical reduction pathways to convert NO3- to N2 or NH3, which are benign or value-added products, respectively are examined over single-atom electrocatalysts (SAC) in Cu. The activity and selectivity for nitrate reduction are compared with the competitive hydrogen evolution reaction (HER). Cu suppresses HER but produces toxic NO2- because of a high activation barrier for cleaving the second N-O bond. SACs provide secondary sites for reaction and break traditional linear scaling relationships. Ru-SACs selectively produce NH3 because N-O bond scission is facile, and the resulting N remains isolated on SAC sites; reacting with H+ from solvating H2O to form ammonia. Conversely, Pd-SAC forms N2 because the reduced N* atoms migrate to the Cu surface, which has a low H availability, allowing N atoms to combine to N2. This relation between N* binding preference and reduction product is demonstrated across an array of SAC elements. Hence, the solvation effects on the surface critically alter the activity of adsorption and catalysis and the removal of toxic pollutants can be improved by altering the surface water network.

Behavior-driven obesity has become one of the most challenging global epidemics since the 1990s, and is presently associated with the leading causes of death in the U.S. and worldwide, including diabetes, cardiovascular disease, strokes, and some forms of cancer. The use of system identification and control engineering principles in the design of novel and perpetually adaptive behavioral health interventions for promoting physical activity and healthy eating has been the central theme in many recent contributions. However, the absence of experimental studies specifically designed with the purpose of developing control-oriented behavioral models has restricted prior efforts in this domain to the use of hypothetical simulations to demonstrate the potential viability of these interventions. In this dissertation, the use of first-of-a-kind, real-life experimental results to develop dynamic, participant-validated behavioral models essential for the design and evaluation of optimized and adaptive behavioral interventions is examined. Following an intergenerational approach, the first part of this work aims to develop a dynamical systems model of intrauterine fetal growth with the prime goal of predicting infant birth weight, which has been associated with subsequent childhood and adult-onset obesity. The use of longitudinal input-output data from the “Healthy Mom Zone” intervention study has enabled the estimation and validation of this fetoplacental model. The second part establishes a set of data-driven behavioral models founded on Social Cognitive Theory (SCT). The “Just Walk” intervention experiment, developed at Arizona State University using system identification principles, has lent a unique opportunity to estimate and validate both black-box and semiphysical SCT models for predicting physical activity behavior. Further, this dissertation addresses some of the model estimation challenges arising from the limitations of “Just Walk”, including the need for developing nontraditional modeling approaches for short datasets, as well as delivers a new theoretical and algorithmic framework for structured state-space model estimation that can be used in a broader set of application domains. Finally, adaptive closed-loop intervention simulations of participant-validated SCT models from “Just Walk” are presented using a Hybrid Model Predictive Control (HMPC) control law. A simple HMPC controller reconfiguration strategy for designing both single- and multi-phase intervention designs is proposed.

Oxygen transfer reactions are central to many catalytic processes, including those underlying automotive exhaust emissions control and clean energy conversion. The catalysts used in these applications typically consist of metal nanoparticles dispersed on reducible oxides (e.g., Pt/CeO2), since reducible oxides can transfer their lattice oxygen to reactive adsorbates at the metal-support interface. There are many outstanding questions regarding the atomic and nanoscale spatial variation of the Pt/CeO2 interface, Pt metal particle, and adjacent CeO2 oxide surface during catalysis. To this end, a range of techniques centered around aberration-corrected environmental transmission electron microscopy (ETEM) were developed and employed to visualize and characterize the atomic-scale structural behavior of CeO2-supported Pt catalysts under reaction conditions (in situ) and/or during catalysis (operando). A model of the operando ETEM reactor was developed to simulate the gas and temperature profiles during conditions of catalysis. Most importantly, the model provides a tool for relating the reactant conversion measured with spectroscopy to the reaction rate of the catalyst that is imaged on the TEM grid. As a result, this work has produced a truly operando TEM methodology, since the structure observed during an experiment can be directly linked to quantitative chemical kinetics of the same catalyst. This operando ETEM approach was leveraged to investigate structure-activity relationships for CO oxidation over Pt/CeO2 catalysts. Correlating atomic-level imaging with catalytic turnover frequency reveals a direct relationship between activity and dynamic structural behavior that (a) destabilizes the supported Pt particle, (b) marks an enhanced rate of oxygen vacancy creation and annihilation, and (c) leads to increased strain and reduction in the surface of the CeO2 support. To further investigate the structural meta-stability (i.e., fluxionality) of 1 – 2 nm CeO2-supported Pt nanoparticles, time-resolved in situ AC-ETEM was employed to visualize the catalyst’s dynamical behavior with high spatiotemporal resolution. Observations are made under conditions relevant to the CO oxidation and water-gas shift (WGS) reactions. Finally, deep learning-based convolutional neural networks were leveraged to develop novel denoising techniques for ultra-low signal-to-noise images of catalytic nanoparticles.

Attaining a sufficiently large critical current density (Jc) in magnetic-barrier Josephson junctions has been one of the greatest challenges to the development of dense low-power superconductor memories. Many experimentalists have used various combinations of superconductor (S) and ferromagnetic (F) materials, with limited success towards the goal of attaining a useful Jc. This trial-and-error process is expensive and time consuming. An improvement in the fundamental understanding of transport through the ferromagnetic layers and across the superconductor-ferromagnetic interface could potentially give fast, accurate predictions of the transport properties in devices and help guide the experimental studies.

In this thesis, parameters calculated using density functional methods are used to model transport across Nb/0.8 nm Fe/Nb/Nb and Nb/3.8 nm Ni /Nb/Nb Josephson junctions. The model simulates the following transport processes using realistic parameters from density functional theory within the generalized gradient approximation: (a) For the first electron of the Cooper pair in the superconductor to cross the interface- conservation of energy and crystal momentum parallel to the interface (kll). (b) For the second electron to be transmitted coherently- satisfying the Andreev reflection interfacial boundary conditions and crossing within a coherence time, (c) For transmission of the coherent pair through the ferromagnetic layer- the influence of the exchange field on the electrons’ wavefunction and (d) For transport through the bulk and across the interfaces- the role of pair-breaking from spin-flip scattering of the electrons. Our model shows the utility of using realistic electronic-structure band properties of the materials used, rather the mean-field exchange energy and empirical bulk and interfacial material parameters used by earlier workers. [Kontos et al. Phys. Rev Lett, 93(13), 137001. (2004); Demler et al. Phys. Rev. B, 55(22), 15174. (1997)].

The critical current densities obtained from out model for Nb/0.8 nm Fe/Nb is 104 A/cm2 and for Nb/3.8 nm Ni/Nb is 7.1*104 A/cm2. These values fall very close to those observed experimentally- i.e. for Nb/0.8 nm Fe/Nb is 8*103 A/cm2 [Robinson et al" Phys. Rev. B 76, no. 9, 094522. (2007)] and for Nb/3.8 nm of Ni/Nb is 3*104 A/cm2 [Blum et al Physical review letters 89, no. 18, 187004. (2002). This indicates that our approach could potentially be useful in optimizing the properties of ferromagnetic-barrier structures for use in low-energy superconducting memories.