Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Publication
- Publication Type
Articles 1 - 4 of 4
Full-Text Articles in Chemistry
Computationally-Driven Insights Into The Ligand Environments Of Materials For Catalysis And Separations, Stephen Vicchio
Computationally-Driven Insights Into The Ligand Environments Of Materials For Catalysis And Separations, Stephen Vicchio
All Dissertations
Designing new catalytic and sorption materials is necessary to limit global temperature rise below 1.5 ◦C by 2050, while also meeting global energy demands. Climate change and energy production are not mutually exclusive; global population growth has direct impacts on global energy demands and climate. In both catalysis and adsorption applications, new technologies are needed to address these challenges. Catalysis can provide alternate, low-energy routes for converting low-value gases into higher-value chemical commodities, thus altering our current energy production. Likewise, new sorption materials can capture previously emitted CO2 from decades of energy production from fossil fuels, thus helping to …
Dft Study Of NiM@Pt1AuN-M-1 (N=19, 38, 55, 79; M = 1, 6, 13, 19) Core-Shell Orr Catalyst, Wen-Jie Li, Dong-Xu Tian, Hong Du, Xi-Qiang Yan
Dft Study Of NiM@Pt1AuN-M-1 (N=19, 38, 55, 79; M = 1, 6, 13, 19) Core-Shell Orr Catalyst, Wen-Jie Li, Dong-Xu Tian, Hong Du, Xi-Qiang Yan
Journal of Electrochemistry
The slow kinetics of oxygen reduction reaction (ORR) limits the performance of low temperature fuel cells. Thus, it needs to design effective catalysts with low cost. Core-shell clusters (CSNCs) show promising activity because of their size-dependent geometric and electronic effects. The ORR activity trend of Nim@Pt1Aun-m-1(n = 19, 38, 55, 79; m = 1, 6, 13, 19) was studied using the GGA-PBE-PAW methods. The adsorption configurations of *O, *OH and *OOH were optimized and the reaction free energies of four proton electron (H+ + e-) transfer steps were calculated. Using …
Applying Bayesian Machine Learning Methods To Theoretical Surface Science, Shane Carr
Applying Bayesian Machine Learning Methods To Theoretical Surface Science, Shane Carr
McKelvey School of Engineering Theses & Dissertations
Machine learning is a rapidly evolving field in computer science with increasingly many applications to other domains. In this thesis, I present a Bayesian machine learning approach to solving a problem in theoretical surface science: calculating the preferred active site on a catalyst surface for a given adsorbate molecule. I formulate the problem as a low-dimensional objective function. I show how the objective function can be approximated into a certain confidence interval using just one iteration of the self-consistent field (SCF) loop in density functional theory (DFT). I then use Bayesian optimization to perform a global search for the solution. …
Electrochemical Catalysis: A Dft Study, Li Li, Zi-Dong Wei
Electrochemical Catalysis: A Dft Study, Li Li, Zi-Dong Wei
Journal of Electrochemistry
In this review, we focus on achievements in electro-catalysis based on the density function theory study. The relationships among the electrode potential, electronic structure of catalysts and electro-catalytic activity are summarized in three parts: the adsorption and desorption of species, electron transfer, and stability of catalysts. The electrode potential and the electronic structure (d-band center or Fermi (HOMO) energy) of catalysts significantly influence the formation, adsorption and desorption of surface species on electrode. The electro-catalytic activity can be improved by modulating the electrode potential and electronic structure of catalysts.