Investigation of Ni-Ceria Nanocatalysts: Improved Computational Methods Towards Hydrogen Production in Aquaprocessing of Heavy Oil

Barrios Herrera, Lizandra

Investigation of Ni-Ceria Nanocatalysts: Improved Computational Methods Towards Hydrogen Production in Aquaprocessing of Heavy Oil

dc.contributor.advisor	Salahub, Dennis
dc.contributor.author	Barrios Herrera, Lizandra
dc.contributor.committeemember	Kusalik, Peter
dc.contributor.committeemember	Pereira Almao, Pedro
dc.date	2023-06
dc.date.accessioned	2023-02-24T21:34:23Z
dc.date.available	2023-02-24T21:34:23Z
dc.date.issued	2023-02-21
dc.description.abstract	Ni-Ceria (Ni-CeO2) nanoparticles (NPs) are promising nanocatalysts for water splitting and water gas shift reactions due to the ability of ceria to temporarily donate oxygen to the catalytic reaction and accept oxygen after the reaction is completed. Therefore, elucidating how different properties of the nanoparticles, such as structure, size, composition, and electronic structure, relate to the activity and selectivity of the catalytic reaction is crucial for developing novel catalysts. In this thesis, we focus on the computational investigation of Ni-Ceria NPs to shed light on their structure/property relationships. From the fundamental point of view, the computational investigation of Ni-CeO2 systems presents a high degree of complexity because they are characterized by strong electron correlation, and it is known that first-principles calculations, such as Density Functional Theory (DFT), may fail to treat the correlation properly. To fill that gap, and with the motivation of increasing the functionality of the deMon2k software, we implemented the Hubbard model, commonly known as +U, in the framework of Auxiliary Density Functional Theory (ADFT) to treat the correlation explicitly. The formulation and implementation of our “ADFT+U” model are discussed in Chapter 3. On the other hand, in Chapter 4, we introduce new auxiliary functions for pseudo-potentials addressing the shortcomings of the auxiliary functions in previous versions of deMon2k, and validate ADFT for Ni-Ceria systems. It is known that the global optimization of nanoparticles characterized by complex potential energy surfaces with many local minima, such as Ni-Ceria NPs, remains challenging and time-consuming. In Chapter 5, we use machine learning (ML) regression and its uncertainty -- an active-learning (AL) strategy -- for the global optimization of the nanoparticles using DFT calculations. The method allows the learned structure-energy relationships to improve iteratively when more data are obtained from DFT calculations of promising structures indicated by the AL. Additionally, further investigation of the NPs by mass-scaled parallel-tempering Born-Oppenheimer molecular dynamics (PT-BOMD) resulted in the same global minima (GM) structures found by AL, demonstrating the robustness of our AL search. This AL strategy has shown to be a powerful tool for learning from small datasets and assists in the global optimization of complex electronic structure systems, such as Ni-Ceria NPs, allowing us to determine the structures of Ce(y-x)NixO(2y-x), with (x=1, 2, 3; y=4) and Ce10Ni3O26 NPs for the first time. Finally, in Chapter 6, we present a novel AL model to search for the catalytic sites of the NPs based on single-point calculations, which is an essential step to explore the reaction over the nanoparticle surface. In summary, this thesis provides crucial insights into the relationships between the structures, the electronic structures, and the catalytic properties of Ni-Ceria NPs for the water-splitting reaction. For instance, we found that Ni-Ceria NPs with a 3Ni/1Ce ratio exhibit a more favourable adsorption energy, which relates to the electronic properties of the cluster, with a computed HOMO-LUMO gap of about 2 eV, and so, better catalytic properties for the water-splitting reaction.	en_US
dc.identifier.citation	Barrios Herrera, L. (2023). Investigation of Ni-Ceria nanocatalysts: improved computational methods towards hydrogen production in aquaprocessing of heavy oil (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.uri	http://hdl.handle.net/1880/115880
dc.identifier.uri	https://dx.doi.org/10.11575/PRISM/40768
dc.language.iso	eng	en_US
dc.publisher.faculty	Science	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	Nanocatalysts	en_US
dc.subject	Ni-Ceria nanoparticles	en_US
dc.subject	Active learning	en_US
dc.subject	Global optimization	en_US
dc.subject	Density functional theory	en_US
dc.subject.classification	Education--Sciences	en_US
dc.subject.classification	Artificial Intelligence	en_US
dc.subject.classification	Engineering--Chemical	en_US
dc.title	Investigation of Ni-Ceria Nanocatalysts: Improved Computational Methods Towards Hydrogen Production in Aquaprocessing of Heavy Oil	en_US
dc.type	doctoral thesis	en_US
thesis.degree.discipline	Chemistry	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Doctor of Philosophy (PhD)	en_US
ucalgary.item.requestcopy	true	en_US