Browsing by Author "Liu, Chun-Yen"

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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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    Unraveling Metal-Support Interactions in Catalysis with Density Functional Theory and Statistical Learning
    (2022-04-21) Liu, Chun-Yen; Senftle, Thomas Patrick
    Oxide supported transition metals are common heterogeneous catalysts in chemical industry. Single atom catalysts (SACs) are the most efficient way to utilize all the metal atoms. To synthesize the SACs, the interaction strength between transition metals and the oxide supports is critical since weak interaction cannot resist sintering and thus form metal nanoparticles on the oxide substrates. Beyond the impact on metal particle size distribution, electronic metal-support interactions (EMSI) offer a path to tune the oxidation state of the transition metals and the catalytic reactivity or selectivity. Here, we use density functional theory (DFT) together with statistical learning (SL) to construct physical descriptors that can predict the metal binding energy on the oxide supports. We showed that the derived descriptors can capture the extent in the change of metal binding energy on modified MgO(100) in response to substituent dopants or surface adsorbates. Along with developing the understanding of EMSI, a novel SL algorithm, named iterative Bayesian additive regression trees (iBART), was proposed to construct the physical descriptors more efficiently than state-of-the-art methods. In summary, this work yields a systematic understanding in EMSI and an original SL method to build up physical descriptors.
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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.