Browsing by Author "Chen, Han"

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    Experimental Adsorption and Reaction Studies on Transition Metal Oxides Compared to DFT Simulations
    Chen, Han (Virginia Tech, 2021-06-11)
    A temperature-programmed desorption (TPD) study of CO and NH₃ adsorption on MnO(100) with complimentary density functional theory (DFT) simulations was conducted. TPD reveals a primary CO desorption signal at 130 K from MnO(100) in the low coverage limit giving an adsorption energy of -35.6 ±2.1 kJ/mol on terrace sites. PBE+U gives a more reasonable structural result than PBE, and the adsorption energy obtained by PBE+U and DFT-D3 Becke-Johnson gives excellent agreement with the experimentally obtained ΔEads for adsorption at Mn²⁺ terrace sites. The analysis of NH₃-TPD traces revealed that adsorption energy on MnO(100) is coverage-dependent. At the low-coverage limit, the adsorption energy on terraces is -58.7±1.0 kJ/mol. A doser results in the formation of a transient NH₃ multilayers that appears in TPD at around 110K. For a terrace site, PBE+U predicts a more realistic surface adsorbate geometry than PBE does, with PBE+U with Tkatchenko-Scheffler method with iterative Hirshfeld partitioning (TSHP) provides the best prediction. DFT simulations of the dehydrogenation elementary step of the ethyl and methyl fragments on α-Cr2O₃(101̅2) were also conducted to complement previous TPD studies of these subjects. On the nearly-stoichiometric surface of α-Cr₂O₃(101̅2), CD₃₋ undergoes dehydrogenation to produce CD₂=CD₂ and CD₄. Previous TPD traces suggest that the α-hydrogen (α-H) elimination of methyl groups on α-Cr₂O₃(101̅2) is the rate-limiting step, and has an activation barrier of 135±2 kJ/mol. DFT simulations showed that PBE gives reasonable prediction of the adsorption sites for CH3- fragments in accordance with XPS spectra, while PBE+U did not. Both PBE and PBE+U failed to predict the correct adsorption sites for CH₂=. When the simulation is set in accordance with the experimentally observed adsorption sites for the carbon species, PBE gives very accurate prediction on the reaction barrier when an adjacent I adatom is present, while PBE+U failed spectacularly. When the simulation is set in accordance with the DFT-predicted adsorption sites, PBE is still able to accurately predict the reaction barrier (<1% to 8.7% error) while PBE+U is less accurate. DFT is also used to complement the previous study of the β-H elimination an ethyl group on the α-Cr₂O₃(101̅2) surface. The DFT simulation shows that absent surface Cl adatoms, PBE predicts an activation barrier of 92.6 kJ/mol, underpredicting the experimental activation barrier by 28.7%, while PBE+U predicts a barrier of 27.0 kJ/mol, under-predicting the experimental barrier by 79.2%. The addition of chlorine on the adjacent cation improved the prediction on barrier by PBE+U marginally, while worsened the prediction by PBE marginally. Grant information: Financial support provided by the U.S. Department of Energy through grant DE-FG02 97ER14751.
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    High-Dimensional Functional Graphs and Inference for Unknown Heterogeneous Populations
    Chen, Han (Virginia Tech, 2024-11-21)
    In this dissertation, we develop innovative methods for analyzing high-dimensional, heterogeneous functional data, focusing specifically on uncovering hidden patterns and network structures within such complex data. We utilize functional graphical models (FGMs) to explore the conditional dependence structure among random elements. We mainly focus on the following three research projects. The first project combines the strengths of FGMs with finite mixture of regression models (FMR) to overcome the challenges of estimating conditional dependence structures from heterogeneous functional data. This novel approach facilitates the discovery of latent patterns, proving particularly advantageous for analyzing complex datasets, such as brain imaging studies of autism spectrum disorder (ASD). Through numerical analysis of both simulated data and real-world ASD brain imaging, we demonstrate the effectiveness of our methodology in uncovering complex dependencies that traditional methods may miss due to their homogeneous data assumptions. Secondly, we address the challenge of variable selection within FMR in high-dimensional settings by proposing a joint variable selection technique. This technique employs a penalized expectation-maximization (EM) algorithm that leverages shared structures across regression components, thereby enhancing the efficiency of identifying relevant predictors and improving the predictive ability. We further expand this concept to mixtures of functional regressions, employing a group lasso penalty for variable selection in heterogeneous functional data. Lastly, we recognize the limitations of existing methods in testing the equality of multiple functional graphs and develop a novel, permutation-based testing procedure. This method provides a robust, distribution-free approach to comparing network structures across different functional variables, as illustrated through simulation studies and functional magnetic resonance imaging (fMRI) analysis for ASD. Hence, these research works provide a comprehensive framework for functional data analysis, significantly advancing the estimation of network structures, functional variable selection, and testing of functional graph equality. This methodology holds great promise for enhancing our understanding of heterogeneous functional data and its practical applications.
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    The incidence of environmental status signaling on three hospitality and tourism green products: A scenario-based quasi-experimental analysis
    Rahman, Imran; Chen, Han; Bernard, Shaniel (Elsevier, 2023-03-01)
    This study examined whether environmental status signaling (ESS) applied to purchase situations involving three environmental products in hospitality and tourism: an environment-friendly car, an organically-produced wine, and a green hotel. Findings from three scenario-based quasi-experimental studies suggested that ESS differed across the type of green product and the consumption setting. When status motive was high, consumers would purchase the environment-friendly car over its more-luxurious conventional counterpart across all consumption settings. Higher purchase intention was also found for the more luxurious hotel over the environment-friendly hotel and for the organically-produced wine over the better-rated conventional wine in private settings. These effects disappeared in the public setting. Moreover, ESS was independent of whether green products were priced equal or more. Recommendations on how to promote the personal benefits of green products and improve performance, design, and packaging of the green products were provided to practitioners in the hospitality and tourism industry.