Browsing by Author "Wang, Peng"

Now showing 1 - 3 of 3
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    Dynamic structural evolution of iron catalysts involving competitive oxidation and carburization during CO2 hydrogenation
    (AAAS, 2022) Zhu, Jie; Wang, Peng; Zhang, Xiaoben; Zhang, Guanghui; Li, Rongtan; Li, Wenhui; Senftle, Thomas P.; Liu, Wei; Wang, Jianyang; Wang, Yanli; Zhang, Anfeng; Fu, Qiang; Song, Chunshan; Guo, Xinwen
    Identifying the dynamic structure of heterogeneous catalysts is crucial for the rational design of new ones. In this contribution, the structural evolution of Fe(0) catalysts during CO2 hydrogenation to hydrocarbons has been investigated by using several (quasi) in situ techniques. Upon initial reduction, Fe species are carburized to Fe3C and then to Fe5C2. The by-product of CO2 hydrogenation, H2O, oxidizes the iron carbide to Fe3O4. The formation of Fe3O4@(Fe5C2+Fe3O4) core-shell structure was observed at steady state, and the surface composition depends on the balance of oxidation and carburization, where water plays a key role in the oxidation. The performance of CO2 hydrogenation was also correlated with the dynamic surface structure. Theoretical calculations and controll experiments reveal the interdependence between the phase transition and reactive environment. We also suggest a practical way to tune the competitive reactions to maintain an Fe5C2-rich surface for a desired C2+ productivity.
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    UBER v1.0: a universal kinetic equation solver for radiation belts
    (European Geosciences Union, 2021) Zheng, Liheng; Chen, Lunjin; Chan, Anthony A.; Wang, Peng; Xia, Zhiyang; Liu, Xu
    Recent proceedings in radiation belt studies have proposed new requirements for numerical methods to solve the kinetic equations involved. In this article, we present a numerical solver that can solve the general form of the radiation belt Fokker–Planck equation and Boltzmann equation in arbitrarily provided coordinate systems and with user-specified boundary geometry, boundary conditions, and equation terms. The solver is based upon the mathematical theory of stochastic differential equations, whose computational accuracy and efficiency are greatly enhanced by specially designed adaptive algorithms and a variance reduction technique. The versatility and robustness of the solver are exhibited in four example problems. The solver applies to a wide spectrum of radiation belt modeling problems, including the ones featuring non-diffusive particle transport such as that arising from nonlinear wave–particle interactions.
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    Understanding and Designing Heterogeneous Catalysts with Computational Modeling and Machine Learning
    (2023-04-21) Wang, Peng; Senftle, Thomas P.
    Alternative catalysts is based on inexpensive and environmental-friendly metals for propane dehydrogenation (PDH) catalysts are needed to overcome the drawbacks of Pt or Cr-based commercial catalysts. A thorough understanding of current catalysts is required to further optimize or design novel catalysts. As such, this dissertation employs Density Functional Theory (DFT) in tandem with ab initio thermodynamics, grand canonical Monte Carlo (GCMC), and Machine Learning (ML) to understand the mechanisms of catalytic performance and phase formation, which are used to design new catalysts. The DFT calculations, in accordance with ab initio thermodynamics, are used to determine surface stability as a function of reaction environment. It is demonstrated that the carbon-rich surfaces of Fe3C exhibit high stability under typical PDH reaction conditions. Further investigation into kinetics shows that these surfaces are responsible for high selectivity by destabilizing propylene adsorption through the ensemble effect. In particular, this dissertation develops a hybrid grand canonical Monte Carlo-Density Functional Theory (GCMC-DFT) method that can effectively sample the structures in complex phase formation without any prior information or parameters about the system. It is shown that the ring formation and ring completion are essential in coke formation on Fe surfaces. Both electronic and geometrical effect can improve the coke resistance of iron-based catalysts. DFT calculated adsorption energies coupled with machine learning are utilized to effectively search through a certain material space and design new catalysts. A Co3Si material is identified to be active and selective for PDH. Silicon promotes cobalt to be selective by downshifting the d-band and destabilizing propylene adsorption. The multi-scale computational methodology developed and applied in this dissertation can provide deep understanding of Fe-based PDH catalysts and assist in designing new catalysts, and can be readily transferred to other catalytic research works.