Browsing by Author "Senftle, Thomas P."

Now showing 1 - 7 of 7
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    Challenges in photocatalysis using covalent organic frameworks
    (IOP Publishing, 2024) Jiang, Shu-Yan; Senftle, Thomas P.; Verduzco, Rafael; NanoEnabled Water Treatment Center
    Photocatalysis is an attractive, energy-efficient technology for organic transformations, polymer synthesis, and degradation of environmental pollutants. There is a need for new photocatalysts stable in different media and that can be tailored for specific applications. Covalent organic frameworks (COF) are crystalline, nanoporous materials with π-conjugated backbone monomers, representing versatile platforms as heterogeneous, metal-free photocatalysts. The backbone structure can be tailored to achieve desired photocatalytic properties, side-chains can mediate adsorption, and the nanoporous structure provides large surface area for molecular adsorption. While these properties make COFs attractive as photocatalysts, several fundamental questions remain regarding mechanisms for different photocatalytic transformations, reactant transport into porous COF structures, and both structural and chemical stability in various environments. In this perspective, we provide a brief overview of COF photocatalysts and identify challenges that should be addressed in future research seeking to employ COFs as photocatalysts. We close with an outlook and perspective on future research directions in the area of COF photocatalysts.
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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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    Porphyrin-based donor–acceptor COFs as efficient and reusable photocatalysts for PET-RAFT polymerization under broad spectrum excitation
    (Royal Society of Chemistry, 2021) Zhu, Yifan; Zhu, Dongyang; Chen, Yu; Yan, Qianqian; Liu, Chun-Yen; Ling, Kexin; Liu, Yifeng; Lee, Dongjoo; Wu, Xiaowei; Senftle, Thomas P.; Verduzco, Rafael
    Covalent organic frameworks (COFs) are crystalline and porous organic materials attractive for photocatalysis applications due to their structural versatility and tunable optical and electronic properties. The use of photocatalysts (PCs) for polymerizations enables the preparation of well-defined polymeric materials under mild reaction conditions. Herein, we report two porphyrin-based donor–acceptor COFs that are effective heterogeneous PCs for photoinduced electron transfer-reversible addition–fragmentation chain transfer (PET-RAFT). Using density functional theory (DFT) calculations, we designed porphyrin COFs with strong donor–acceptor characteristics and delocalized conduction bands. The COFs were effective PCs for PET-RAFT, successfully polymerizing a variety of monomers in both organic and aqueous media using visible light (λmax from 460 to 635 nm) to produce polymers with tunable molecular weights (MWs), low molecular weight dispersity, and good chain-end fidelity. The heterogeneous COF PCs could also be reused for PET-RAFT polymerization at least 5 times without losing photocatalytic performance. This work demonstrates porphyrin-based COFs that are effective catalysts for photo-RDRP and establishes design principles for the development of highly active COF PCs for a variety of applications.
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    Three-dimensional covalent organic frameworks with pto and mhq-z topologies based on Tri- and tetratopic linkers
    (Springer Nature, 2023) Zhu, Dongyang; Zhu, Yifan; Chen, Yu; Yan, Qianqian; Wu, Han; Liu, Chun-Yen; Wang, Xu; Alemany, Lawrence B.; Gao, Guanhui; Senftle, Thomas P.; Peng, Yongwu; Wu, Xiaowei; Verduzco, Rafael
    Three-dimensional (3D) covalent organic frameworks (COFs) possess higher surface areas, more abundant pore channels, and lower density compared to their two-dimensional counterparts which makes the development of 3D COFs interesting from a fundamental and practical point of view. However, the construction of highly crystalline 3D COF remains challenging. At the same time, the choice of topologies in 3D COFs is limited by the crystallization problem, the lack of availability of suitable building blocks with appropriate reactivity and symmetries, and the difficulties in crystalline structure determination. Herein, we report two highly crystalline 3D COFs with pto and mhq-z topologies designed by rationally selecting rectangular-planar and trigonal-planar building blocks with appropriate conformational strains. The pto 3D COFs show a large pore size of 46 Å with an extremely low calculated density. The mhq-z net topology is solely constructed from totally face-enclosed organic polyhedra displaying a precise uniform micropore size of 1.0 nm. The 3D COFs show a high CO2 adsorption capacity at room temperature and can potentially serve as promising carbon capture adsorbents. This work expands the choice of accessible 3D COF topologies, enriching the structural versatility of COFs.
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    Titanium oxide improves boron nitride photocatalytic degradation of perfluorooctanoic acid
    (Elsevier, 2022) Duan, Lijie; Wang, Bo; Heck, Kimberly N.; Clark, Chelsea A.; Wei, Jinshan; Wang, Minghao; Metz, Jordin; Wu, Gang; Tsai, Ah-Lim; Guo, Sujin; Arredondo, Jacob; Mohite, Aditya D.; Senftle, Thomas P.; Westerhoff, Paul; Alvarez, Pedro; Wen, Xianghua; Song, Yonghui; Wong, Michael S.; Center for Nanotechnology Enabled Water Treatment
    Boron nitride (BN) has the newly-found property of degrading recalcitrant polyfluoroalkyl substances (PFAS) under ultraviolet C (UV-C, 254 nm) irradiation. It is ineffective at longer wavelengths, though. In this study, we report the simple calcination of BN and UV-A active titanium oxide (TiO2) creates a BN/TiO2 composite that is more photocatalytically active than BN or TiO2 under UV-A for perfluorooctanoic acid (PFOA). Under UV-A, BN/TiO2 degraded PFOA ∼ 15 × faster than TiO2, while BN was inactive. Band diagram analysis and photocurrent response measurements indicated that BN/TiO2 is a type-II heterojunction semiconductor, facilitating charge carrier separation. Additional experiments confirmed the importance of photogenerated holes for degrading PFOA. Outdoor experimentation under natural sunlight found BN/TiO2 to degrade PFOA in deionized water and salt-containing water with a half-life of 1.7 h and 4.5 h, respectively. These identified photocatalytic properties of BN/TiO2 highlight the potential for the light-driven destruction of other PFAS.
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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.
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    Using statistical learning to predict interactions between single metal atoms and modified MgO(100) supports
    (Springer Nature, 2020) Liu, Chun-Yen; Zhang, Shijia; Martinez, Daniel; Li, Meng; Senftle, Thomas P.
    Metal/oxide interactions mediated by charge transfer influence reactivity and stability in numerous heterogeneous catalysts. In this work, we use density functional theory (DFT) and statistical learning (SL) to derive models for predicting how the adsorption strength of metal atoms on MgO(100) surfaces can be enhanced by modifications of the support. MgO(100) in its pristine form is relatively unreactive, and thus is ideal for examining ways in which its electronic interactions with metals can be enhanced, tuned, and controlled. We find that the charge transfer characteristics of MgO are readily modified either by adsorbates on the surface (e.g., H, OH, F, and NO2) or dopants in the oxide lattice (e.g., Li, Na, B, and Al). We use SL methods (i.e., LASSO, Horseshoe prior, and Dirichlet–Laplace prior) that are trained against DFT data to identify physical descriptors for predicting how the adsorption energy of metal atoms will change in response to support modification. These SL-derived feature selection tools are used to screen through more than one million candidate descriptors that are generated from simple chemical properties of the adsorbed metals, MgO, dopants, and adsorbates. Among the tested SL tools, we demonstrate that Dirichlet–Laplace prior predicts metal adsorption energies on MgO most accurately, while also identifying descriptors that are most transferable to chemically similar oxides, such as CaO, BaO, and ZnO.