| Grant number: | 22/12778-5 |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| Start date: | November 01, 2022 |
| End date: | March 29, 2024 |
| Field of knowledge: | Physical Sciences and Mathematics - Chemistry - Physical-Chemistry |
| Agreement: | BG E&P Brasil (Shell Group) |
| Principal Investigator: | Fabio Coral Fonseca |
| Grantee: | Albert Frederico Barbosa Bittencourt |
| Host Institution: | Instituto de Química de São Carlos (IQSC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
| Company: | Secretaria de Desenvolvimento Econômico (São Paulo - Estado). Instituto de Pesquisas Energéticas e Nucleares (IPEN) |
| Associated research grant: | 17/11937-4 - A sustainable path to methane conversion by advanced electrochemical technologies, AP.PCPE |
Abstract The properties of heterogeneous catalysts ranging in size from "single-atom" to nanoparticles have been a longstanding interest of both experimental and computational researchers. Although atomistic understanding of the molecular-level properties of such catalysts has improved considerably in the past two decades, the mechanisms by which the catalyst structure is coupled to the chemical environment, and the impact of this coupling on catalytic properties, remain incompletely understood, limiting the opportunities for the discovery of new and exciting materials. To extend the range of applications for real-life catalysis, this research project focuses on the development and extension of available methods to predict how the structure of multielemental catalytic nanoparticles, including primarily metal alloys but not limited to those systems, is altered by in-situ reaction conditions in order to expand the range of applications for real-world catalysis. In addition, how these modifications affect the ability of catalysts to promote industrially relevant reactions. To this purpose, the computational strategy will use periodic DFT calculations of multielemental surface structures with varying compositions and local structures to give raw data for input into machine learning algorithms based on crystal graph convolution neural network formalisms. These formalisms will regress the DFT data to yield compact potential energy expressions, which will then be exploited by global optimization to predict ensemble average nanocatalyst geometries at given reactor temperatures. The conversion of alkene compounds, which occurs at high temperature, will be used as a model to address how thermal effects can lead to surface restructuring of nanoparticles. The main activities for this project involve the selection of the materials for industrially relevant reactions, density functional theory calculations for surfaces and finite-size particles to be used as training data for neural-network algorithms; combination of global and local optimization algorithms to address the interrelation between structure and physical-chemical properties of unary and binary finite-site particles; simulations to address the structural changes under different environments and their effects on catalytic activity; and, as possible, comparison with experimental results. From these results, this project aims to contribute to the atomistic understanding of the structural changes in finite-size nanoparticles at high-temperature conditions and their effects on chemical reactions. (AU) | |
| News published in Agência FAPESP Newsletter about the scholarship: | |
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