ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: chemical engineering
- Creators: Muhich, Christopher L
Description
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.
ContributorsGupta, Srishti (Author) / Muhich, Christopher L (Thesis advisor) / Singh, Arunima (Committee member) / Emady, Heather (Committee member) / Westerhoff, Paul (Committee member) / Deng, Shuguang (Committee member) / Arizona State University (Publisher)
Created2023
Description
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.
ContributorsVincent, Joshua Lawrence (Author) / Crozier, Peter A (Thesis advisor) / Liu, Jingyue (Committee member) / Muhich, Christopher L (Committee member) / Nannenga, Brent L (Committee member) / Singh, Arunima K (Committee member) / Arizona State University (Publisher)
Created2021
Description
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.
ContributorsWilson, Steven A (Author) / Muhich, Christopher L (Thesis advisor) / Rivera, Daniel E (Committee member) / Stechel, Ellen B (Committee member) / Lin, Jerry (Committee member) / Deng, Shuguang (Committee member) / Arizona State University (Publisher)
Created2024