Date of Graduation
Eberly College of Arts and Sciences
Physics and Astronomy
James P. Lewis
Alan D. Bristow
Alan D. Bristow
Thiolate protected nanoclusters gold nanoparticles are gaining interest of many researchers due to their promising applications in a variety of fields the development of synthesizing techniques capable of producing atomically precise nanoclusters with high purity. Au25(SR)18 is one of the widely studied nanoclsuters due its remarkable stability. In this first part of this study, we explore the structural, electronic and catalytic properties of bimetallic Au25−xAgx(SR)18 (for x = 6, 7, 8). Due to the combinatorial enormity of the total number of possible alloyed isomers, we choose a randomly selected subset corresponding to each alloying level. Using SCH3 as the ligand, we find that the favorable Ag dopant locations are on the surface of the Au13 icosahedron. To study the catalytic activity in Au25(SR)18, we used SCH2CH2Ph and S-c-C6H11 ligands for 6-8 and 19-23 Ag alloyed systems respectively. Using the condensed-to-atom dual descriptor functions, we show that the reduction in the catalytic activity of Au25−xAgx(SR)18 is related to the reduction in the electron donating capability of the outer shell sites.
We also propose a machine-learning model, based on the random-forest method, to predict CO adsorption in thiolate protected nanoclusters. Two phases of feature selection and training, based initially on the Au25 nanocluster, are utilized in our model. One advantage to a machine-learning approach is that correlations in defined features disentangle relationships among the various structural parameters. For example, in Au25, we find that features based on the distribution of Ag atoms relative to the CO adsorption site are the most important in predicting adsorption energies. Our machine-learning model is easily extended to other Au-based nanoclusters, and we demonstrate predictions about CO adsorption on Ag-alloyed Au36 and Au133 nanoclusters.
In the final section of the thesis, Delafossite materials have been studied for a long time for photovoltaic and catalytic applications due to their wide band gaps and bipolar conductivities. These systems have forbidden fundamental band gaps which are much smaller than their apparent optical gaps. Making optical transitions permissible across the fundamental gap allows photocatalytic reactions to harness solar light in the visible region. Recently, it has been shown that B site doping can break the inversion symmetry of these delafossites allowing light absorption across the fundamental gap. We study structural and electronic properties of two Fe-doped delafossites, AgAl1−xFexO2 and AgGa1−xFexO2 (x=1%-5%) using high-throughput calculations. Preferable Fe dopant locations in both these systems are studied using two metrics. Based on the electronic density of states and unfolded band structures, we find that Fe impurities mainly affect the valence band edges of both these systems. New Fe states at the Γ point can result in lower energy optical transitions compared to the undoped delafossites. Using molecular orbital plots, we confirmed that Fe doping affects the states only at the valence band edge, resulting in valence and conduction band edges having different parities. Thus, Fe doping can permit optical transitions that are forbidden in the pure AgAlO2 and AgGaO2 .
Panapitiya, Gihan Uthpala, "Novel Computational Methods for Catalytic Applications" (2019). Graduate Theses, Dissertations, and Problem Reports. 4095.