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Grand Canonical Approach to Modeling Dynamic Catalysts : = From Thermal to Electro-Catalysis, From Clusters to Surfaces.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Grand Canonical Approach to Modeling Dynamic Catalysts :/
Reminder of title:	From Thermal to Electro-Catalysis, From Clusters to Surfaces.
Author:	Zhang, Zisheng.
Description:	1 online resource (284 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Computational chemistry. -
Online resource:	click for full text (PQDT)
ISBN:	9798382769103

Grand Canonical Approach to Modeling Dynamic Catalysts : = From Thermal to Electro-Catalysis, From Clusters to Surfaces.
Zhang, Zisheng.

Grand Canonical Approach to Modeling Dynamic Catalysts :From Thermal to Electro-Catalysis, From Clusters to Surfaces. - 1 online resource (284 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2024.

Includes bibliographical references

Dynamic structural rearrangement has been observed in a wide range of heterogeneous catalysts and functional materials when they are in operation. Such fluxional behaviors underlie the reactivity, activation, or deactivation in various catalytic systems. However, experimentally resolving their atomic structures has been challenging due to the transient and minority nature of the metastable motifs and surface phases. The role of theory in investigating those dynamic systems hence remains singular.This dissertation will focus on the development and application of a grand canonical (GC) approach to model catalysts that undergo significant off-stoichiometric restructurings in reaction conditions. Grand canonical genetic algorithm (GCGA), an efficient global optimization algorithm, is implemented and used to explore the vast chemical space of cluster isomerization, surface atoms rearrangement, mixed coverage and configuration of adsorbates, and to locate the global and relevant local minima. The found minima constitute a GC ensemble of catalyst states that are diverse in structure, stoichiometry, and reactivity. By ab initio thermodynamics and grand canonical density functional theory (GC-DFT) calculations, the dependence on reaction conditions (temperature, partial pressures, pH, electrode potential, solute concentrations, etc.) can be included into the free energetics of the states, to probe how the distribution of states responds to varying conditions.This approach has been applied to investigate multiple systems ranging from thermal to electro-catalysis, and from supported clusters to extended surfaces. This talk will cover a few representative systems, including boron nitride in thermal oxidative dehydrogenation conditions, supported sub-nanometer metal clusters in electrocatalysis, and copper electrodes in electroreduction conditions. The dissertation works will illustrate how the GC approach not only helps interpret complex experimental observations, but also provides rich atomic insights into the structure and reactivity of catalytic species, and lays the foundation to build a new paradigm for reaction kinetics, catalyst optimization, non-equilibrium behaviors, and more.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382769103Subjects--Topical Terms:

1190332
Computational chemistry.
Subjects--Index Terms:

CatalysisIndex Terms--Genre/Form:

554714
Electronic books.

Grand Canonical Approach to Modeling Dynamic Catalysts : = From Thermal to Electro-Catalysis, From Clusters to Surfaces.
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520 $a Dynamic structural rearrangement has been observed in a wide range of heterogeneous catalysts and functional materials when they are in operation. Such fluxional behaviors underlie the reactivity, activation, or deactivation in various catalytic systems. However, experimentally resolving their atomic structures has been challenging due to the transient and minority nature of the metastable motifs and surface phases. The role of theory in investigating those dynamic systems hence remains singular.This dissertation will focus on the development and application of a grand canonical (GC) approach to model catalysts that undergo significant off-stoichiometric restructurings in reaction conditions. Grand canonical genetic algorithm (GCGA), an efficient global optimization algorithm, is implemented and used to explore the vast chemical space of cluster isomerization, surface atoms rearrangement, mixed coverage and configuration of adsorbates, and to locate the global and relevant local minima. The found minima constitute a GC ensemble of catalyst states that are diverse in structure, stoichiometry, and reactivity. By ab initio thermodynamics and grand canonical density functional theory (GC-DFT) calculations, the dependence on reaction conditions (temperature, partial pressures, pH, electrode potential, solute concentrations, etc.) can be included into the free energetics of the states, to probe how the distribution of states responds to varying conditions.This approach has been applied to investigate multiple systems ranging from thermal to electro-catalysis, and from supported clusters to extended surfaces. This talk will cover a few representative systems, including boron nitride in thermal oxidative dehydrogenation conditions, supported sub-nanometer metal clusters in electrocatalysis, and copper electrodes in electroreduction conditions. The dissertation works will illustrate how the GC approach not only helps interpret complex experimental observations, but also provides rich atomic insights into the structure and reactivity of catalytic species, and lays the foundation to build a new paradigm for reaction kinetics, catalyst optimization, non-equilibrium behaviors, and more.
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