Computational models of nanostructured materials for energy storage and conversion
| dc.contributor.author | Henderson, Brett | |
| dc.contributor.supervisor | Paci, Irina | |
| dc.date.accessioned | 2025-05-05T21:36:28Z | |
| dc.date.available | 2025-05-05T21:36:28Z | |
| dc.date.issued | 2025 | |
| dc.degree.department | Department of Chemistry | |
| dc.degree.level | Doctor of Philosophy PhD | |
| dc.description.abstract | This dissertation develops and benchmarks computational methods for the rational design of materials for energy storage and conversion. The first portion of the text presents three projects aimed at introducing computationally efficient methods for studying the mechanisms of capacitive energy storage in nanocomposite dielectric materials. Studying model systems consisting of alkaline earth metal oxides with nanoscopic silver inclusions using Density Functional Theory reveals that manipulating the composition and morphology of a nanocomposite’s components permits large increases in electric permittivity. A continuum model for such composites is introduced and shown to reproduce many of the effects of inclusion morphology on permittivity. Finally, a model based on inducible atomic dipoles is studied for several types of inorganic cluster, and its accuracy is shown to be dependent in part upon the degree of charge transfer within the clusters. Together, these projects advance the understanding of the mechanisms underlying capacitive energy storage in nanostructured dielectrics and add efficient new methodologies to the simulation toolkit for designing novel dielectrics for energy storage. The second portion of the dissertation benchmarks the performance of various Density Functional Approximations in the prediction of the activity of metal–nitrogen–carbon (M–N–C) catalysts for the oxygen reduction reaction. The calculated activity trends of M–N–C catalysts—specifically metalloporphyrins—are found to by highly method-dependent. The primary drivers of this dependence are explored, and best practices for similar systems are suggested while also highlighting the importance of benchmarking for new systems. This work is necessary for advancing the field of single-atom catalysts, since it helps practitioners avoid common pitfalls in the computational protocols used to design and screen catalysts. | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.bibliographicCitation | Brett Henderson, Sofia Donnecke, Scott N. Genin, Ilya G. Ryabinkin, and Irina Paci. Key Role of Density Functional Approximation in Predicting M–N–C Catalyst Activities for Oxygen Reduction. The Journal of Physical Chemistry C 2024 128 (38), 15899-15911. DOI: 10.1021/acs.jpcc.4c03322 Brett Henderson, Archita N S Adluri, Jeffrey T Paci and Irina Paci. Dielectric metal/metal oxide nanocomposites: modeling response properties at multiple scales. Modelling Simul. Mater. Sci. Eng. 31 065015. DOI: 10.1088/1361-651X/ace540 | |
| dc.identifier.uri | https://hdl.handle.net/1828/22135 | |
| dc.language | English | eng |
| dc.language.iso | en | |
| dc.rights | Available to the World Wide Web | |
| dc.subject | Chemistry | |
| dc.subject | Nanocomposites | |
| dc.subject | Computational | |
| dc.subject | Quantum mechanics | |
| dc.subject | Energy storage | |
| dc.subject | Nanostructures | |
| dc.title | Computational models of nanostructured materials for energy storage and conversion | |
| dc.type | Thesis |