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1. BOROHYDRIDE OXIDATION OVER THE AU(111) AND PT(111) SURFACES: A FIRST PRINCIPLES STUDY OF THE REACTION MECHANISM

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3. Ab initio study of surface chemistry for fuel processing: adsorptive desulfurization and carbon resistant steam reforming

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4. PHOTOINDUCED ACTIVATION OF CO2 ON TiO2 SURFACES: QUANTUM CHEMICAL MODELING OF GROUND AND EXCITED STATES, AND IN SITU EPR STUDIES

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5. Application of density functional theory calculations to elucidate the structure of lignin linkages and the intermolecular interactions among proxies of lignin, hemicellulose, and cellulose

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7. Theory and Simulation of Atomic Hydrogen, Fluorine, and Oxygen on Graphene

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8. Sulfur tolerance enhancement of steam reforming by binary metal catalysts

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9. Novel Ti based mixed metal oxide systems for adsorptive desulfurization: A combined ab initio and experimental study

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10. Development of Multi-scale Computational Methods for Modeling Phase Formation in Pd-Based Catalysts

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11. Describing the Electronic Structure of Molecules on Metal Surfaces

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12. A computational study of the effects of alloying elements on the thermodynamic and diffusion properties of Mg alloys

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13. Direct fluorination of graphene: A theoretical and computational study of its formation and of the resulting magnetic and electronic properties

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14. INTERPRETING THE SPECTRAL SIGNATURES OF SURFACE-ENHANCED RAMAN SCATTERING

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15. Multi-scale Atomistic Modeling for Electrocatalytic Applications

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16. Testing the Viability of Supercritical Carbon Dioxide as a Fracking Fluid by Computing its Chemical Interaction with Illite

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17. Development of a knowledge base of Ti-alloys from first-principles and thermodynamic modeling

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18. Study of phase equilibria and defect chemistry of the Cu-Zn-Sn-S system from first-principles and computational thermodynamics towards photovoltaic applications

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19. Quantum–Continuum Modeling of Electrodeposition

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20. Predicting Stability of Modified Oxide Surfaces with Functional Atomic-Layers for Nano-engineered Catalysts through First Principles Calculations and Statistical Learning

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