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Ab Initio Based Computational Studie...

Ding, Zhutian.

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Ab Initio Based Computational Studies of Metal Oxide - Water Interfaces.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Ab Initio Based Computational Studies of Metal Oxide - Water Interfaces./
作者:	Ding, Zhutian.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	121 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
標題:	Computational chemistry. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30487077
ISBN:	9798379716875

Ab Initio Based Computational Studies of Metal Oxide - Water Interfaces.
Ding, Zhutian.

Ab Initio Based Computational Studies of Metal Oxide - Water Interfaces. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 121 p.

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--Princeton University, 2023.

This item is not available from ProQuest Dissertations & Theses.

The aqueous interfaces of metal oxides play an important role in geochemical processes, catalysis, and electrochemical applications. Despite the intense research efforts over the past few decades, there are many open questions to be answered about metal oxide - water interfaces such as the nature of adsorbed water, the mechanism of electron transfer, the behavior of charged species, as well as the structure and dynamics of interfacial water at amorphous metal oxide - water interfaces. This dissertation aims to investigate some of the open questions via first principles based simulations for the aqueous interfaces of SiO2, MgO, and amorphous TiO2, all of which have important technical applications and are prototypical metal oxides.By using ab initio molecular dynamics (AIMD) based on a hybrid density functional, we investigate the detailed adsorption structure and dynamics of water at the aqueous interface of pristine and defected MgO. Also using AIMD based on a hybrid density functional, we study the behavior of a lithium impurity at the aqueous interface of SiO2 and its interaction with a localized electron donated by the lithium impurity in terms of electronic energy levels. Since the interplay between electronic energy levels and water dynamics governs interfacial charge transfer, we further study the behavior of an excess localized electron donated by an Al dopant at and out of equilibrium at the aqueous interface of Al-doped MgO. We identify two pathways of electron transfer from the conduction band of MgO to interfacial product states and characterize the pathways using Marcus theory.Recognizing the lack of understanding on the surfaces and aqueous interfaces of amorphous TiO2, we develop deep neural network potentials (DPs) trained on density functional theory and investigate the structure and dynamics of interfacial water at amorphous TiO2 interfaces using a combination of DP-based and ab initio molecular dynamics simulations. Overall, this dissertation has provided greater insights into the aqueous interfaces of the metal oxides studied, and has developed computational framework and procedure that can be broadly applicable to more general systems.

ISBN: 9798379716875Subjects--Topical Terms:

3350019
Computational chemistry.
Subjects--Index Terms:

Aqueous interfaces

Ab Initio Based Computational Studies of Metal Oxide - Water Interfaces.
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245 1 0 $a Ab Initio Based Computational Studies of Metal Oxide - Water Interfaces.
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300 $a 121 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
500 $a Advisor: Selloni, Annabella.
502 $a Thesis (Ph.D.)--Princeton University, 2023.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
520 $a The aqueous interfaces of metal oxides play an important role in geochemical processes, catalysis, and electrochemical applications. Despite the intense research efforts over the past few decades, there are many open questions to be answered about metal oxide - water interfaces such as the nature of adsorbed water, the mechanism of electron transfer, the behavior of charged species, as well as the structure and dynamics of interfacial water at amorphous metal oxide - water interfaces. This dissertation aims to investigate some of the open questions via first principles based simulations for the aqueous interfaces of SiO2, MgO, and amorphous TiO2, all of which have important technical applications and are prototypical metal oxides.By using ab initio molecular dynamics (AIMD) based on a hybrid density functional, we investigate the detailed adsorption structure and dynamics of water at the aqueous interface of pristine and defected MgO. Also using AIMD based on a hybrid density functional, we study the behavior of a lithium impurity at the aqueous interface of SiO2 and its interaction with a localized electron donated by the lithium impurity in terms of electronic energy levels. Since the interplay between electronic energy levels and water dynamics governs interfacial charge transfer, we further study the behavior of an excess localized electron donated by an Al dopant at and out of equilibrium at the aqueous interface of Al-doped MgO. We identify two pathways of electron transfer from the conduction band of MgO to interfacial product states and characterize the pathways using Marcus theory.Recognizing the lack of understanding on the surfaces and aqueous interfaces of amorphous TiO2, we develop deep neural network potentials (DPs) trained on density functional theory and investigate the structure and dynamics of interfacial water at amorphous TiO2 interfaces using a combination of DP-based and ab initio molecular dynamics simulations. Overall, this dissertation has provided greater insights into the aqueous interfaces of the metal oxides studied, and has developed computational framework and procedure that can be broadly applicable to more general systems.
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650 4 $a Physical chemistry. $3 1981412
650 4 $a Materials science. $3 543314
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653 $a Density functional theory
653 $a Electronic structure
653 $a Machine learning
653 $a Metal oxides
653 $a Molecular dynamics
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690 $a 0494
690 $a 0794
710 2 $a Princeton University. $b Chemistry. $3 2094742
773 0 $t Dissertations Abstracts International $g 84-12B.
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791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30487077