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<a name="x_m_-6102164529963294376_x_Chapter1" style="font-family: "Times New Roman", serif; font-size: 12pt; color: inherit;"><b><span style="font-size:14.0pt; line-height:150%; color:black">NAME:</span></b></a><span style="font-family: "Times New Roman", serif; font-size: 12pt;"></span><span style="font-family: "Times New Roman", serif; font-size: 14pt; line-height: 150%; color: black;"> Xiao
Li</span><br>
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<b><span style="font-size:14.0pt; line-height:150%; color:#201F1E">COMMITTEE CHAIR: </span></b><span style="font-size:14.0pt; line-height:150%; color:#201F1E">Dr. Lars Grabow</span><span style="font-size:14.0pt; line-height:150%; color:#222222"></span></p>
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<b><span style="font-size:14.0pt; line-height:150%; color:#201F1E">DATE:</span></b><span style="font-size:14.0pt; line-height:150%; color:#201F1E"> </span><span style="font-size:14.0pt; line-height:150%; color:#222222">Monday, November 30, 2020</span></p>
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<b><span style="font-size:14.0pt; line-height:150%; color:#201F1E">TIME</span></b><span style="font-size:14.0pt; line-height:150%; color:#201F1E">: </span><span style="font-size:14.0pt; line-height:150%; color:#222222">2:00 PM (CST)</span></p>
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<b><span style="font-size:14.0pt; line-height:150%; color:#201F1E">LOCATION:</span></b><span style="font-family:"Times New Roman",serif; color:#201F1E"> </span><a href="https://urldefense.com/v3/__https://uofh.zoom.us/j/91735130621?pwd=WlZ4R3ErVnFBenQ0ZkxkYUJYeHJndz09__;!!LkSTlj0I!Wu4aV0EqS9H8nUpwPP6xDq3tVMKYjkLphGF4yBGAlnPMZNaD9otpoXGq4mOjF10XBBg$" target="_blank"><span style="color:#1155CC; background:white">https://uofh.zoom.us/j/91735130621?pwd=WlZ4R3ErVnFBenQ0ZkxkYUJYeHJndz09</span></a></p>
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<b><span style="font-size:14.0pt; line-height:150%; color:#201F1E">TITLE: </span></b><span style="font-size:14.0pt; line-height:150%; color:#222222"></span></p>
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<b><span style="font-size:14.0pt; color:#201F1E">Prediction of Acid Site Evolution and Distribution Using First-Principles Kinetic Monte Carlo and Genetic Algorithms: Implications for Catalytic Hydrotreating of Biomass</span></b></p>
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<span style="font-size:14.0pt">Abstract</span></h6>
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<span style="color:black">Computational design of multifunctional catalysts and active sites that dynamically respond to changes in reaction conditions is a formidable challenge. Microkinetic models (MKM) are popular for catalytic activity and trend predictions,
but the underlying mean field approximation cannot account for the spatial distribution of multiple active sites. In contrast, lattice-based kinetic Monte Carlo (kMC) simulations using a cluster expansion representation of adsorbate−adsorbate interactions
can capture the intricacies and complexities of a real catalytic system. I have evaluated the practical advantages of MKM and kMC simulations and found that MKM is faster and more robust when used with limited energetic input information, as it would be common
for catalyst screening.</span></p>
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<span style="color:black">The advantages of first-principles kMC simulations are better demonstrated for selective hydrodeoxygenation (HDO) over Ru/TiO<sub>2</sub>(110) catalysts. My kMC results qualitatively reproduce experimental results and explain the observed
effect of co-feeding water on HDO activity. As the water partial pressure increases, the surface coverage of hydroxyl/water increases and the dominant HDO pathway switches from a reverse Mars van Krevelen mechanism to a proton-assisted pathway. There exists
no comparable model with the ability to discriminate between metal, oxide and interfacial activity, and assign a dominant catalytic role to each component.</span></p>
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<span style="color:black">While dynamic active site changes under reaction conditions are important, the speciation of active sites is primarily determined by the synthesis conditions. To this end, I developed a genetic algorithm to quickly determine the global
minimum energy structure (GMES) of aluminosilicates, and demonstrated a ca. 35% reduction in computational effort compared to a brute force approach.</span></p>
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<span style="color:black">The approaches and outcomes described in this thesis are leading computational catalysis beyond the design of a static active site and towards the ability to develop multifunctional catalysts that dynamically respond to changes in
the reactive environment. Such knowledge is necessary for the discovery of highly active and selective catalysts to sustainably produce fuels and chemicals.</span></p>
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