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Yang B, Liu K, Ma Y, Ma JJ, Chen YY, Huang M, Yang C, Hou Y, Hung SF, Yu JC, Zhang J, Wang X. Incorporation of Pd Single-Atom Sites in Perovskite with an Excellent Selectivity toward Photocatalytic Semihydrogenation of Alkynes. Angew Chem Int Ed Engl 2024; 63:e202410394. [PMID: 39072967 DOI: 10.1002/anie.202410394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2024] [Revised: 07/12/2024] [Accepted: 07/26/2024] [Indexed: 07/30/2024]
Abstract
Semihydrogenation is a crucial industrial process. Noble metals such as Pd have been extensively studied in semihydrogenation reactions, owing to their unique catalytic activity toward hydrogen activation. However, the overhydrogenation of alkenes to alkanes often happens due to the rather strong adsorption of alkenes on Pd active phases. Herein, we demonstrate that the incorporation of Pd active phases as single-atom sites in perovskite lattices such as SrTiO3 can greatly alternate the electronic structure and coordination environment of Pd active phases to facilitate the desorption of alkenes rather than further hydrogenation. Furthermore, the incorporated Pd sites can be well stabilized without sintering by a strong host-guest interaction with SrTiO3 during the activation of H species in hydrogenation reactions. As a result, the Pd incorporated SrTiO3 (Pd-SrTiO3) exhibits an excellent time-independent selectivity (>99.9 %) and robust durability for the photocatalytic semihydrogenation of phenylacetylene to styrene. This strategy based on incorporation of active phases in perovskite lattices will have broad implications in the development of high-performance photocatalysts for selective hydrogenation reactions.
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Affiliation(s)
- Baoying Yang
- State Key Laboratory of Photocatalysis on Energy and Environment College of Chemistry, Fuzhou University, Fuzhou, 350108, P. R. China
| | - Kunlong Liu
- State Key Laboratory of Photocatalysis on Energy and Environment College of Chemistry, Fuzhou University, Fuzhou, 350108, P. R. China
| | - Yuhui Ma
- State Key Laboratory of Photocatalysis on Energy and Environment College of Chemistry, Fuzhou University, Fuzhou, 350108, P. R. China
| | - Jian-Jie Ma
- Department of Applied Chemistry and Center for Emergent Functional Matter Science, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan
| | - Yi-Yu Chen
- Department of Applied Chemistry and Center for Emergent Functional Matter Science, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan
| | - Meirong Huang
- State Key Laboratory of Photocatalysis on Energy and Environment College of Chemistry, Fuzhou University, Fuzhou, 350108, P. R. China
| | - Can Yang
- State Key Laboratory of Photocatalysis on Energy and Environment College of Chemistry, Fuzhou University, Fuzhou, 350108, P. R. China
| | - Yidong Hou
- State Key Laboratory of Photocatalysis on Energy and Environment College of Chemistry, Fuzhou University, Fuzhou, 350108, P. R. China
| | - Sung-Fu Hung
- Department of Applied Chemistry and Center for Emergent Functional Matter Science, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan
| | - Jimmy C Yu
- Department of Chemistry, Chinese University of Hong Kong Shatin, New Territories, Hong Kong, 999077, China
| | - Jinshui Zhang
- State Key Laboratory of Photocatalysis on Energy and Environment College of Chemistry, Fuzhou University, Fuzhou, 350108, P. R. China
| | - Xinchen Wang
- State Key Laboratory of Photocatalysis on Energy and Environment College of Chemistry, Fuzhou University, Fuzhou, 350108, P. R. China
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2
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Zhang KX, Liu ZP. In Situ Surfaced Mn-Mn Dimeric Sites Dictate CO Hydrogenation Activity and C 2 Selectivity over MnRh Binary Catalysts. J Am Chem Soc 2024; 146:27138-27151. [PMID: 39295520 DOI: 10.1021/jacs.4c10052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2024]
Abstract
Massive ethanol production has long been a dream of human society. Despite extensive research in past decades, only a few systems have the potential of industrialization: specifically, Mn-promoted Rh (MnRh) binary heterogeneous catalysts were shown to achieve up to 60% C2 oxygenates selectivity in converting syngas (CO/H2) to ethanol. However, the active site of the binary system has remained poorly characterized. Here, large-scale machine-learning global optimization is utilized to identify the most stable Mn phases on Rh metal surfaces under reaction conditions by exploring millions of likely structures. We demonstrate that Mn prefers the subsurface sites of Rh metal surfaces and is able to emerge onto the surface forming MnRh surface alloy once the oxidative O/OH adsorbates are present. Our machine-learning-based transition state exploration further helps to resolve automatedly the whole reaction network, including 74 elementary reactions on various MnRh surface sites, and reveals that the Mn-Mn dimeric site at the monatomic step edge is the true active site for C2 oxygenate formation. The turnover frequency of the C2 product on the Mn-Mn dimeric site at MnRh steps is at least 107 higher than that on pure Rh steps from our microkinetic simulations, with the selectivity to the C2 product being 52% at 523 K. Our results demonstrate the key catalytic role of Mn-Mn dimeric sites in allowing C-O bond cleavage and facilitating the hydrogenation of O-terminating C2 intermediates, and rule out Rh metal by itself as the active site for CO hydrogenation to C2 oxygenates.
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Affiliation(s)
- Ke-Xiang Zhang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
- State Key Laboratory of Metal Organic Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
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3
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Wang J, Xu H, Zhang Y, Wu J, Ma H, Zhan X, Zhu J, Cheng D. Discovery of Alloy Catalysts Beyond Pd for Selective Hydrogenation of Reformate via First-Principle Screening with Consideration of H-Coverage. Angew Chem Int Ed Engl 2024; 63:e202317592. [PMID: 38650376 DOI: 10.1002/anie.202317592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 04/06/2024] [Accepted: 04/22/2024] [Indexed: 04/25/2024]
Abstract
The highly selective hydrogenation to remove olefins is a significant refining approach for the reformate. Herein, a library of transition metal for reformate hydrogenation is tested experimentally to validate the predictive level of catalytic activity from our theoretical framework, which combines ab initio calculations and microkinetic modeling, with consideration of surface H-coverage effect on hydrogenation kinetics. The favorable H coverage of specific alloy surface under relevant hydrogenation condition, is found to be determined by its corresponding alloy composition. Besides, olefin hydrogenation rate is determined as a function of two descriptors, i.e. H coverage and binding energies of atomic hydrogen, paving the way to computationally screen on metal component in the periodic table. Evaluation of 172 bimetallic alloys based on the activity volcano map, as well as benzene hydrogenation rate, identifies prospective superior candidates and experimentally confirms that Zn3Ir1 outperforms pure Pd catalysts for the selective hydrogenation refining of reformate. The insights into H-coverage-related microkinetic modelling have enabled us to both theoretically understand experimental findings and identify novel catalysts, thus, bridging the gap between first-principle simulations and industrial applications. This work provides useful guidance for experimental catalyst design, which can be easily extended to other hydrogenation reaction.
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Affiliation(s)
- Jiayi Wang
- State Key Laboratory of Organic-Inorganic Composites, Beijing Key Laboratory of Energy Environmental Catalysis, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
| | - Haoxiang Xu
- State Key Laboratory of Organic-Inorganic Composites, Beijing Key Laboratory of Energy Environmental Catalysis, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
| | - Yihao Zhang
- State Key Laboratory of Organic-Inorganic Composites, Beijing Key Laboratory of Energy Environmental Catalysis, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
| | - Jianguo Wu
- State Key Laboratory of Organic-Inorganic Composites, Beijing Key Laboratory of Energy Environmental Catalysis, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
| | - Haowen Ma
- Lanzhou Petrochemical Research Center, Petrochemical Research Institute, Petrochina, Lanzhou, 730060, People's Republic of China
| | - Xuecheng Zhan
- Lanzhou Petrochemical Research Center, Petrochemical Research Institute, Petrochina, Lanzhou, 730060, People's Republic of China
| | - Jiqin Zhu
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
| | - Daojian Cheng
- State Key Laboratory of Organic-Inorganic Composites, Beijing Key Laboratory of Energy Environmental Catalysis, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
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4
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Wan K, He J, Shi X. Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305758. [PMID: 37640376 DOI: 10.1002/adma.202305758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/24/2023] [Indexed: 08/31/2023]
Abstract
The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.
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Affiliation(s)
- Kaiwei Wan
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Jianxin He
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Xinghua Shi
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
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5
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Xu K, Cui Y, Guan B, Qin L, Feng D, Abuduwayiti A, Wu Y, Li H, Cheng H, Li Z. Nanozymes with biomimetically designed properties for cancer treatment. NANOSCALE 2024; 16:7786-7824. [PMID: 38568434 DOI: 10.1039/d4nr00155a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Nanozymes, as a type of nanomaterials with enzymatic catalytic activity, have demonstrated tremendous potential in cancer treatment owing to their unique biomedical properties. However, the heterogeneity of tumors and the complex tumor microenvironment pose significant challenges to the in vivo catalytic efficacy of traditional nanozymes. Drawing inspiration from natural enzymes, scientists are now using biomimetic design to build nanozymes from the ground up. This approach aims to replicate the key characteristics of natural enzymes, including active structures, catalytic processes, and the ability to adapt to the tumor environment. This achieves selective optimization of nanozyme catalytic performance and therapeutic effects. This review takes a deep dive into the use of these biomimetically designed nanozymes in cancer treatment. It explores a range of biomimetic design strategies, from structural and process mimicry to advanced functional biomimicry. A significant focus is on tweaking the nanozyme structures to boost their catalytic performance, integrating them into complex enzyme networks similar to those in biological systems, and adjusting functions like altering tumor metabolism, reshaping the tumor environment, and enhancing drug delivery. The review also covers the applications of specially designed nanozymes in pan-cancer treatment, from catalytic therapy to improved traditional methods like chemotherapy, radiotherapy, and sonodynamic therapy, specifically analyzing the anti-tumor mechanisms of different therapeutic combination systems. Through rational design, these biomimetically designed nanozymes not only deepen the understanding of the regulatory mechanisms of nanozyme structure and performance but also adapt profoundly to tumor physiology, optimizing therapeutic effects and paving new pathways for innovative cancer treatment.
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Affiliation(s)
- Ke Xu
- School of Medicine, Tongji University, Shanghai 200092, China
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China.
| | - Yujie Cui
- Shanghai Key Laboratory for R&D and Application of Metallic Functional Materials, Institute of New Energy for Vehicles, School of Materials Science and Engineering, Tongji University, Shanghai 201804, China.
| | - Bin Guan
- Center Laboratory, Jinshan Hospital, Fudan University, Shanghai 201508, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Linlin Qin
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China.
- Department of Thoracic Surgery, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai 200081, China
| | - Dihao Feng
- School of Art, Shaoxing University, Shaoxing 312000, Zhejiang, China
| | - Abudumijiti Abuduwayiti
- School of Medicine, Tongji University, Shanghai 200092, China
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China.
| | - Yimu Wu
- School of Medicine, Tongji University, Shanghai 200092, China
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China.
| | - Hao Li
- Department of Organ Transplantation, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361005, Fujian, China
| | - Hongfei Cheng
- Shanghai Key Laboratory for R&D and Application of Metallic Functional Materials, Institute of New Energy for Vehicles, School of Materials Science and Engineering, Tongji University, Shanghai 201804, China.
| | - Zhao Li
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China.
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6
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Lan X, Wang Y, Liu B, Kang Z, Wang T. Thermally induced intermetallic Rh 1Zn 1 nanoparticles with high phase-purity for highly selective hydrogenation of acetylene. Chem Sci 2024; 15:1758-1768. [PMID: 38303947 PMCID: PMC10829007 DOI: 10.1039/d3sc05460h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 12/19/2023] [Indexed: 02/03/2024] Open
Abstract
Ordered M1Zn1 intermetallic phases with structurally isolated atom sites offer unique electronic and geometric structures for catalytic applications, but lack reliable industrial synthesis methods that avoid forming a disordered alloy with ill-defined composition. We developed a facile strategy for preparing well-defined M1Zn1 intermetallic nanoparticle (i-NP) catalysts from physical mixtures of monometallic M/SiO2 (M = Rh, Pd, Pt) and ZnO. The Rh1Zn1 i-NPs with structurally isolated Rh atom sites had a high intrinsic selectivity to ethylene (91%) with extremely low C4 and oligomer formation, outperforming the reported intermetallic and alloy catalysts in acetylene semihydrogenation. Further studies revealed that the M1Zn1 phases were formed in situ in a reducing atmosphere at 400 °C by a Zn atom emitting-trapping-ordering (Zn-ETO) mechanism, which ensures the high phase-purity of i-NPs. This study provides a scalable and practical solution for further exploration of Zn-based intermetallic phases and a new strategy for designing Zn-containing catalysts.
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Affiliation(s)
- Xiaocheng Lan
- Beijing Key Laboratory of Green Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University Beijing 100084 China
| | - Yu Wang
- Beijing Key Laboratory of Green Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University Beijing 100084 China
| | - Boyang Liu
- Beijing Key Laboratory of Green Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University Beijing 100084 China
| | - Zhenyu Kang
- Beijing Key Laboratory of Green Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University Beijing 100084 China
| | - Tiefeng Wang
- Beijing Key Laboratory of Green Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University Beijing 100084 China
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7
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Liu QY, Chen D, Shang C, Liu ZP. An optimal Fe-C coordination ensemble for hydrocarbon chain growth: a full Fischer-Tropsch synthesis mechanism from machine learning. Chem Sci 2023; 14:9461-9475. [PMID: 37712046 PMCID: PMC10498498 DOI: 10.1039/d3sc02054a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/11/2023] [Indexed: 09/16/2023] Open
Abstract
Fischer-Tropsch synthesis (FTS, CO + H2 → long-chain hydrocarbons) because of its great significance in industry has attracted huge attention since its discovery. For Fe-based catalysts, after decades of efforts, even the product distribution remains poorly understood due to the lack of information on the active site and the chain growth mechanism. Herein powered by a newly developed machine-learning-based transition state (ML-TS) exploration method to treat properly reaction-induced surface reconstruction, we are able to resolve where and how long-chain hydrocarbons grow on complex in situ-formed Fe-carbide (FeCx) surfaces from thousands of pathway candidates. Microkinetics simulations based on first-principles kinetics data further determine the rate-determining and the selectivity-controlling steps, and reveal the fine details of the product distribution in obeying and deviating from the Anderson-Schulz-Flory law. By showing that all FeCx phases can grow coherently upon each other, we demonstrate that the FTS active site, namely the A-P5 site present on reconstructed Fe3C(031), Fe5C2(510), Fe5C2(021), and Fe7C3(071) terrace surfaces, is not necessarily connected to any particular FeCx phase, rationalizing long-standing structure-activity puzzles. The optimal Fe-C coordination ensemble of the A-P5 site exhibits both Fe-carbide (Fe4C square) and metal Fe (Fe3 trimer) features.
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Affiliation(s)
- Qian-Yu Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University Shanghai 200433 China
| | - Dongxiao Chen
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University Shanghai 200433 China
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University Shanghai 200433 China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University Shanghai 200433 China
- Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences Shanghai 200032 China
- Shanghai Qi Zhi Institution Shanghai 200030 China
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8
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Li Y, Zhang R, Yan X, Fan K. Machine learning facilitating the rational design of nanozymes. J Mater Chem B 2023. [PMID: 37325942 DOI: 10.1039/d3tb00842h] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
As a component substitute for natural enzymes, nanozymes have the advantages of easy synthesis, convenient modification, low cost, and high stability, and are widely used in many fields. However, their application is seriously restricted by the difficulty of rapidly creating high-performance nanozymes. The use of machine learning techniques to guide the rational design of nanozymes holds great promise to overcome this difficulty. In this review, we introduce the recent progress of machine learning in assisting the design of nanozymes. Particular attention is given to the successful strategies of machine learning in predicting the activity, selectivity, catalytic mechanisms, optimal structures and other features of nanozymes. The typical procedures and approaches for conducting machine learning in the study of nanozymes are also highlighted. Moreover, we discuss in detail the difficulties of machine learning methods in dealing with the redundant and chaotic nanozyme data and provide an outlook on the future application of machine learning in the nanozyme field. We hope that this review will serve as a useful handbook for researchers in related fields and promote the utilization of machine learning in nanozyme rational design and related topics.
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Affiliation(s)
- Yucong Li
- CAS Engineering Laboratory for Nanozyme, Key Laboratory of Protein and Peptide Pharmaceutical, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
- University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100408, China
| | - Ruofei Zhang
- CAS Engineering Laboratory for Nanozyme, Key Laboratory of Protein and Peptide Pharmaceutical, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
| | - Xiyun Yan
- CAS Engineering Laboratory for Nanozyme, Key Laboratory of Protein and Peptide Pharmaceutical, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
- University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100408, China
- Nanozyme Medical Center, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou 450052, China
| | - Kelong Fan
- CAS Engineering Laboratory for Nanozyme, Key Laboratory of Protein and Peptide Pharmaceutical, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
- University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100408, China
- Nanozyme Medical Center, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou 450052, China
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9
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Liu J, Zhao X, Zhao K, Goncharov VG, Delhommelle J, Lin J, Guo X. A modulated fingerprint assisted machine learning method for retrieving elastic moduli from resonant ultrasound spectroscopy. Sci Rep 2023; 13:5919. [PMID: 37041266 PMCID: PMC10090122 DOI: 10.1038/s41598-023-33046-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 04/06/2023] [Indexed: 04/13/2023] Open
Abstract
We used deep-learning-based models to automatically obtain elastic moduli from resonant ultrasound spectroscopy (RUS) spectra, which conventionally require user intervention of published analysis codes. By strategically converting theoretical RUS spectra into their modulated fingerprints and using them as a dataset to train neural network models, we obtained models that successfully predicted both elastic moduli from theoretical test spectra of an isotropic material and from a measured steel RUS spectrum with up to 9.6% missing resonances. We further trained modulated fingerprint-based models to resolve RUS spectra from yttrium-aluminum-garnet (YAG) ceramic samples with three elastic moduli. The resulting models were capable of retrieving all three elastic moduli from spectra with a maximum of 26% missing frequencies. In summary, our modulated fingerprint method is an efficient tool to transform raw spectroscopy data and train neural network models with high accuracy and resistance to spectra distortion.
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Affiliation(s)
- Juejing Liu
- Department of Chemistry, Washington State University, Pullman, WA, 99164, USA
- Alexandra Navrotsky Institute for Experimental Thermodynamics, Washington State University, Pullman, WA, 99164, USA
- School of Mechanical and Materials Engineering, Washington State University, Pullman, WA, 99164, USA
| | - Xiaodong Zhao
- Department of Chemistry, Washington State University, Pullman, WA, 99164, USA
- Alexandra Navrotsky Institute for Experimental Thermodynamics, Washington State University, Pullman, WA, 99164, USA
| | - Ke Zhao
- Alexandra Navrotsky Institute for Experimental Thermodynamics, Washington State University, Pullman, WA, 99164, USA
| | - Vitaliy G Goncharov
- Department of Chemistry, Washington State University, Pullman, WA, 99164, USA
- Alexandra Navrotsky Institute for Experimental Thermodynamics, Washington State University, Pullman, WA, 99164, USA
- School of Mechanical and Materials Engineering, Washington State University, Pullman, WA, 99164, USA
| | - Jerome Delhommelle
- Department of Chemistry, University of Massachusetts, Lowell, MA, 01854, USA
| | - Jian Lin
- School of Nuclear Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Xiaofeng Guo
- Department of Chemistry, Washington State University, Pullman, WA, 99164, USA.
- Alexandra Navrotsky Institute for Experimental Thermodynamics, Washington State University, Pullman, WA, 99164, USA.
- School of Mechanical and Materials Engineering, Washington State University, Pullman, WA, 99164, USA.
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10
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Bridging the complexity gap in computational heterogeneous catalysis with machine learning. Nat Catal 2023. [DOI: 10.1038/s41929-023-00911-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
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11
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Li H, Jiao Y, Davey K, Qiao SZ. Data-Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts. Angew Chem Int Ed Engl 2023; 62:e202216383. [PMID: 36509704 DOI: 10.1002/anie.202216383] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022]
Abstract
The design of heterogeneous catalysts is necessarily surface-focused, generally achieved via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure the adsorption energy is physically meaningful is the stable existence of the conceived active-site structure on the surface. The development of improved understanding of the catalyst surface, however, is challenging practically because of the complex nature of dynamic surface formation and evolution under in-situ reactions. We propose therefore data-driven machine-learning (ML) approaches as a solution. In this Minireview we summarize recent progress in using machine-learning to search and predict (meta)stable structures, assist operando simulation under reaction conditions and micro-environments, and critically analyze experimental characterization data. We conclude that ML will become the new norm to lower costs associated with discovery and design of optimal heterogeneous catalysts.
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Affiliation(s)
- Haobo Li
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Yan Jiao
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Kenneth Davey
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Shi-Zhang Qiao
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
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12
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Li J, Yao Z, Zhao J, Deng S, Wang S, Wang J. Microkinetic simulations of acetylene(acetylene-d2) hydrogenation(deuteration) on Ag nanoparticles. MOLECULAR CATALYSIS 2023. [DOI: 10.1016/j.mcat.2022.112845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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13
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Wang J, Xu H, Che C, Zhu J, Cheng D. Rational Design of PdAg Catalysts for Acetylene Selective Hydrogenation via Structural Descriptor-based Screening Strategy. ACS Catal 2022. [DOI: 10.1021/acscatal.2c05498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Jiayi Wang
- State Key Laboratory of Organic-Inorganic Composites, Beijing Key Laboratory of Energy Environmental Catalysis, Beijing University of Chemical Technology, Beijing100029, People’s Republic of China
| | - Haoxiang Xu
- State Key Laboratory of Organic-Inorganic Composites, Beijing Key Laboratory of Energy Environmental Catalysis, Beijing University of Chemical Technology, Beijing100029, People’s Republic of China
| | - Chunxia Che
- Lanzhou Petrochemical Research Center, Petrochemical Research Institute, Petrochina, Lanzhou730060, P. R. China
| | - Jiqin Zhu
- State Key Laboratory of Organic-Inorganic Composites, Beijing Key Laboratory of Energy Environmental Catalysis, Beijing University of Chemical Technology, Beijing100029, People’s Republic of China
| | - Daojian Cheng
- State Key Laboratory of Organic-Inorganic Composites, Beijing Key Laboratory of Energy Environmental Catalysis, Beijing University of Chemical Technology, Beijing100029, People’s Republic of China
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14
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Chen L, Li XT, Ma S, Hu YF, Shang C, Liu ZP. Highly Selective Low-Temperature Acetylene Semihydrogenation Guided by Multiscale Machine Learning. ACS Catal 2022. [DOI: 10.1021/acscatal.2c04379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Lin Chen
- Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai200433, People’s Republic of China
| | - Xiao-Tian Li
- Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai200433, People’s Republic of China
| | - Sicong Ma
- Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai200032, People’s Republic of China
| | - Yi-Fan Hu
- Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai200433, People’s Republic of China
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai200433, People’s Republic of China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai200433, People’s Republic of China
- Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai200032, People’s Republic of China
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15
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Che L, Guo J, He Z, Zhang H. Evidence of rate-determining step variation along reactivity in acetylene hydrogenation: a systematic kinetic study on elementary steps, kinetically relevant(s) and active species. J Catal 2022. [DOI: 10.1016/j.jcat.2022.08.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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16
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Selectivity control in alkyne semihydrogenation: Recent experimental and theoretical progress. CHINESE JOURNAL OF CATALYSIS 2022. [DOI: 10.1016/s1872-2067(21)64036-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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17
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Xie W, Xu J, Chen J, Wang H, Hu P. Achieving Theory-Experiment Parity for Activity and Selectivity in Heterogeneous Catalysis Using Microkinetic Modeling. Acc Chem Res 2022; 55:1237-1248. [PMID: 35442027 PMCID: PMC9069691 DOI: 10.1021/acs.accounts.2c00058] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Microkinetic modeling based on density functional
theory (DFT)
energies plays an essential role in heterogeneous catalysis because
it reveals the fundamental chemistry for catalytic reactions and bridges
the microscopic understanding from theoretical calculations and experimental
observations. Microkinetic modeling requires building a set of ordinary
differential equations (ODEs) based on the calculation results of
thermodynamic properties of adsorbates and kinetic parameters for
the reaction elementary steps. Solving a microkinetic model can extract
information on catalytic chemistry, including critical reaction intermediates,
reaction pathways, the surface species distribution, activity, and
selectivity, thus providing vital guidelines for altering catalysts. However, the quantitative reliability of traditional microkinetic
models is often insufficient to conclusively extrapolate the mechanistic
details of complex reaction systems. This can be attributed to several
factors, the most important of which is the limitation of obtaining
an accurate estimation of the energy inputs via traditional calculation
methods. These limitations include the difficulty of using static
DFT methods to calculate reaction energies of adsorption/desorption
processes, often rate-controlling or selectivity-determining steps,
and the inadequate consideration of surface coverage effects. In addition,
the robust microkinetic software is rare, which also complicates the
resolution of complex catalytic systems. In this Account, we
review our recent works toward refining the
predictions of microkinetic modeling in heterogeneous catalysis and
achieving theory–experiment parity for activity and selectivity.
First, we introduce CATKINAS, a microkinetic software developed in
our group, and show how it disentangles the problem that traditional
microkinetic software has and how it can now be applied to obtain
kinetic results for more sophisticated reaction systems. Second, we
describe a molecular dynamics method developed recently to obtain
the free-energy changes for the adsorption/desorption process to fill
in the missing energy inputs. Third, we show that a rigorous consideration
of surface coverage effects is pivotal for building more realistic
models and obtaining accurate kinetic results. Following a series
of studies on acetylene hydrogenation reactions on Pd catalysts, we
demonstrate how this new approach can provide an improved quantitative
understanding of the mechanism, active site, and intrinsic structural
sensitivity. Finally, we conclude with a brief outlook and the remaining
challenges in this field.
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Affiliation(s)
- Wenbo Xie
- School of Chemistry and Chemical Engineering, The Queen’s University of Belfast, Belfast BT9 5AG, U.K
| | - Jiayan Xu
- School of Chemistry and Chemical Engineering, The Queen’s University of Belfast, Belfast BT9 5AG, U.K
| | - Jianfu Chen
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Haifeng Wang
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - P. Hu
- School of Chemistry and Chemical Engineering, The Queen’s University of Belfast, Belfast BT9 5AG, U.K
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, East China University of Science and Technology, Shanghai 200237, P. R. China
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18
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Wu R, Wang L. Insight and Activation Energy Surface of the Dehydrogenation of C2HxO Species in Ethanol Oxidation Reaction on Ir(100). Chemphyschem 2022; 23:e202200132. [PMID: 35446461 DOI: 10.1002/cphc.202200132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/20/2022] [Indexed: 11/10/2022]
Abstract
Dehydrogenation of an organic compound is the first and the most fundamental elementary reaction in many organic reactions. In ethanol oxidation reaction (EOR) to form CO 2 , there are a total of 46 pathways in C 2 H x O (x=1-6) species leading to the removal of all six hydrogen atoms in five C-H bonds and one O-H bond. To investigate the degree of dehydrogenation in EOR under operando conditions, we performed density function theory (DFT) calculations to study 28 dehydrogenation steps of C 2 H x O on Ir(100). An activation energy surface was then constructed and compared with that of the C-C bond cleavages to understand the importance of the degree of dehydrogenation in EOR. The results show that there are likely 28 dehydrogenations in EOR under fuel cell temperatures and the last two hydrogens in C 2 H 2 O are less likely cleaved. On the other hand, deep dehydrogenation including 45 dehydrogenations can occur under ethanol steam reforming conditions.
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Affiliation(s)
- Ruitao Wu
- Southern Illinois University Carbondale, Chemistry and Biochemistry, UNITED STATES
| | - Lichang Wang
- Southern Illinois University Carbondale, Department of Chemistry and Biochemistry, 224 Neckers Hall, 62901, Carbondale, UNITED STATES
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19
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Wang C, Wang Z, Mao S, Chen Z, Wang Y. Coordination environment of active sites and their effect on catalytic performance of heterogeneous catalysts. CHINESE JOURNAL OF CATALYSIS 2022. [DOI: 10.1016/s1872-2067(21)63924-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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20
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Li F, Cheng X, Lu LL, Yin YC, Luo JD, Lu G, Meng YF, Mo H, Tian T, Yang JT, Wen W, Liu ZP, Zhang G, Shang C, Yao HB. Stable All-Solid-State Lithium Metal Batteries Enabled by Machine Learning Simulation Designed Halide Electrolytes. NANO LETTERS 2022; 22:2461-2469. [PMID: 35244400 DOI: 10.1021/acs.nanolett.2c00187] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Solid electrolytes (SEs) with superionic conductivity and interfacial stability are highly desirable for stable all-solid-state Li-metal batteries (ASSLMBs). Here, we employ neural network potential to simulate materials composed of Li, Zr/Hf, and Cl using stochastic surface walking method and identify two potential unique layered halide SEs, named Li2ZrCl6 and Li2HfCl6, for stable ASSLMBs. The predicted halide SEs possess high Li+ conductivity and outstanding compatibility with Li metal anodes. We synthesize these SEs and demonstrate their superior stability against Li metal anodes with a record performance of 4000 h of steady lithium plating/stripping. We further fabricate the prototype stable ASSLMBs using these halide SEs without any interfacial modifications, showing small internal cathode/SE resistance (19.48 Ω cm2), high average Coulombic efficiency (∼99.48%), good rate capability (63 mAh g-1 at 1.5 C), and unprecedented cycling stability (87% capacity retention for 70 cycles at 0.5 C).
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Affiliation(s)
- Feng Li
- Division of Nanomaterials and Chemistry, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Xiaobin Cheng
- Department of Chemical Physics, Hefei Science Center of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Lei-Lei Lu
- Division of Nanomaterials and Chemistry, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Yi-Chen Yin
- Department of Applied Chemistry, Hefei Science Center of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jin-Da Luo
- Department of Applied Chemistry, Hefei Science Center of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Gongxun Lu
- College of Materials Science and Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Yu-Feng Meng
- Division of Nanomaterials and Chemistry, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Hongsheng Mo
- Department of Applied Chemistry, Hefei Science Center of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Te Tian
- Division of Nanomaterials and Chemistry, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jing-Tian Yang
- Department of Applied Chemistry, Hefei Science Center of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Wen Wen
- Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200030, China
| | - Guozhen Zhang
- Division of Nanomaterials and Chemistry, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200030, China
| | - Hong-Bin Yao
- Division of Nanomaterials and Chemistry, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, China
- Department of Applied Chemistry, Hefei Science Center of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
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21
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An S, Liu Z, Bu J, Lin J, Yao Y, Yan C, Tian W, Zhang J. Functional Aqueous Zinc–Acetylene Batteries for Electricity Generation and Electrochemical Acetylene Reduction to Ethylene. Angew Chem Int Ed Engl 2022; 61:e202116370. [DOI: 10.1002/anie.202116370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Indexed: 11/11/2022]
Affiliation(s)
- Siying An
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology Department of Advanced Chemical Engineering School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xi'an Shaanxi, 710000 P. R. China
| | - Zhenpeng Liu
- State Key Laboratory of Solidification Processing and School of Materials Science and Engineering Northwestern Polytechnical University Xi'an Shaanxi, 710072 P. R. China
| | - Jun Bu
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology Department of Advanced Chemical Engineering School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xi'an Shaanxi, 710000 P. R. China
| | - Jin Lin
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology Department of Advanced Chemical Engineering School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xi'an Shaanxi, 710000 P. R. China
| | - Yuan Yao
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology Department of Advanced Chemical Engineering School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xi'an Shaanxi, 710000 P. R. China
| | - Chen Yan
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology Department of Advanced Chemical Engineering School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xi'an Shaanxi, 710000 P. R. China
| | - Wei Tian
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology Department of Advanced Chemical Engineering School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xi'an Shaanxi, 710000 P. R. China
| | - Jian Zhang
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology Department of Advanced Chemical Engineering School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xi'an Shaanxi, 710000 P. R. China
- State Key Laboratory of Solidification Processing and School of Materials Science and Engineering Northwestern Polytechnical University Xi'an Shaanxi, 710072 P. R. China
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22
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Gou G, Che C, Wen H, Qin J, Cao X, Han W, Zhang F, Long Y, Ma J. θ-Al2O3/FeO1.25 possessing a special ring complex of FeII---HO===FeIII for the efficient catalytic semi-hydrogenation of acetylene under front–end conditions. J Catal 2022. [DOI: 10.1016/j.jcat.2022.02.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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23
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An S, Liu Z, Bu J, Lin J, Yao Y, Yan C, Tian W, Zhang J. Functional Aqueous Zinc‐Acetylene Batteries for Electricity Generation and Electrochemical Acetylene Reduction to Ethylene. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202116370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Siying An
- Northwestern Polytechnical University School of chemistry and chemical engineering Xi'an CHINA
| | - Zhenpeng Liu
- Northwestern Polytechnical University School of materials science and engineering Xi'an CHINA
| | - Jun Bu
- Northwestern Polytechnical University School of chemistry and chemical engineering Xi'an CHINA
| | - Jin Lin
- Northwestern Polytechnical University School of chemistry and chemical engineering Xi'an CHINA
| | - Yuan Yao
- Northwestern Polytechnical University School of chemistry and chemical engineering Xi'an CHINA
| | - Chen Yan
- Northwestern Polytechnical University School of chemistry and chemical engineering Xi'an CHINA
| | - Wei Tian
- Northwestern Polytechnical University School of chemistry and chemical engineering Xi'an CHINA
| | - Jian Zhang
- Northwestern Polytechnical University School of Chemistry and Chemical Engineering Youyi West Road 710129 Xi’an CHINA
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24
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Zhan X, Zhu H, Ma H, Hu X, Xie Y, Guo D, Chen M, Ma P, Sun L, Wang WD, Dong Z. Ultrafine PdCo bimetallic nanoclusters confined in N-doped porous carbon for the efficient semi-hydrogenation of alkynes. Dalton Trans 2022; 51:16361-16370. [DOI: 10.1039/d2dt02765h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Ultrafine PdCo bimetallic nanoclusters with Co atom-modified Pd active sites were highly dispersed and confined in an m-NC material for selective semi-hydrogenation of alkynes to alkenes.
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Affiliation(s)
- Xuecheng Zhan
- Lanzhou Petrochemical Research Center, Petrochemical Research Institute, PetroChina Company Limited, Lanzhou, 730060, PR China
| | - Hanghang Zhu
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, 730000, PR China
| | - Haowen Ma
- Lanzhou Petrochemical Research Center, Petrochemical Research Institute, PetroChina Company Limited, Lanzhou, 730060, PR China
| | - Xiaoli Hu
- Lanzhou Petrochemical Research Center, Petrochemical Research Institute, PetroChina Company Limited, Lanzhou, 730060, PR China
| | - Yuan Xie
- Lanzhou Petrochemical Research Center, Petrochemical Research Institute, PetroChina Company Limited, Lanzhou, 730060, PR China
| | - Dajiang Guo
- Lanzhou Petrochemical Research Center, Petrochemical Research Institute, PetroChina Company Limited, Lanzhou, 730060, PR China
| | - Minglin Chen
- Lanzhou Petrochemical Research Center, Petrochemical Research Institute, PetroChina Company Limited, Lanzhou, 730060, PR China
| | - Ping Ma
- Lanzhou Petrochemical Research Center, Petrochemical Research Institute, PetroChina Company Limited, Lanzhou, 730060, PR China
| | - Liming Sun
- Lanzhou Petrochemical Research Center, Petrochemical Research Institute, PetroChina Company Limited, Lanzhou, 730060, PR China
| | - Wei David Wang
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, 730000, PR China
| | - Zhengping Dong
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, 730000, PR China
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25
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Wei P, Zheng J, Li Q, Qin Y, Guan H, Tan D, Song L. The modulation mechanism of geometric and electronic structures of bimetallic catalysts: Pd 13−mAg m ( m=0–13) clusters for acetylene semi-hydrogenation. Inorg Chem Front 2022. [DOI: 10.1039/d2qi01222g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The modulation mechanism of the second metal in bimetallic catalysts is examined by taking acetylene semi-hydrogenation over Pd13−mAgm clusters, in which a metastable Pd6Ag7 structure exhibits excellent activity/selectivity to ethylene.
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Affiliation(s)
- Panpeng Wei
- College of Chemistry and Chemical Engineering, China University of Petroleum, Qingdao, Shandong 266580, China
| | - Jian Zheng
- College of Chemistry and Chemical Engineering, China University of Petroleum, Qingdao, Shandong 266580, China
| | - Qiang Li
- Key Laboratory of Petrochemical Catalytic Science and Technology, Liaoning Province, Liaoning Petrochemical University, Fushun, Liaoning 113001, China
| | - Yucai Qin
- Key Laboratory of Petrochemical Catalytic Science and Technology, Liaoning Province, Liaoning Petrochemical University, Fushun, Liaoning 113001, China
| | - Huimin Guan
- Key Laboratory of Petrochemical Catalytic Science and Technology, Liaoning Province, Liaoning Petrochemical University, Fushun, Liaoning 113001, China
| | - Duping Tan
- Lanzhou Petrochemical Research Center, Petrochemical Research Institute, Petrochina, Lanzhou 730060, China
| | - Lijuan Song
- College of Chemistry and Chemical Engineering, China University of Petroleum, Qingdao, Shandong 266580, China
- Key Laboratory of Petrochemical Catalytic Science and Technology, Liaoning Province, Liaoning Petrochemical University, Fushun, Liaoning 113001, China
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26
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Xu X, Ma J, Wu F, Zhu K, Zhou H, Zhang Y, Li X, Zhou Y, Jia G, Liu D, Gao P, Ye W. Regulating Interfacial Water Structure by Tensile Strain to Boost Electrochemical Semi-hydrogenation of Alkynes. Inorg Chem Front 2022. [DOI: 10.1039/d2qi00767c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Electrochemical semi-hydrogenation (ECSH) of alkynes to produce alkenes is an ideal alternative to traditional thermal semi-hydrogenation (TSH), and yet is limited by low conversion yield and product selectivity. Here, we...
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27
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Noonikara-Poyil A, Ridlen SG, Fernández I, Dias HVR. Isolable acetylene complexes of copper and silver. Chem Sci 2022; 13:7190-7203. [PMID: 35799825 PMCID: PMC9214850 DOI: 10.1039/d2sc02377f] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 05/19/2022] [Indexed: 12/02/2022] Open
Abstract
Copper and silver play important roles in acetylene transformations but isolable molecules with acetylene bonded to Cu(i) and Ag(i) ions are scarce. This report describes the stabilization of π-acetylene complexes of such metal ions supported by fluorinated and non-fluorinated, pyrazole-based chelators. These Cu(i) and Ag(i) complexes were formed readily in solutions under an atmosphere of excess acetylene and the appropriate ligand supported metal precursor, and could be isolated as crystalline solids, enabling complete characterization using multiple tools including X-ray crystallography. Molecules that display κ2-or κ3-ligand coordination modes and trigonal planar or tetrahedral metal centers have been observed. Different trends in coordination shifts of the acetylenic carbon resonance were revealed by 13C NMR spectroscopy for the Cu(i) and Ag(i) complexes. The reduction in acetylene
Created by potrace 1.16, written by Peter Selinger 2001-2019
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C
Created by potrace 1.16, written by Peter Selinger 2001-2019
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C due to metal ion coordination is relatively large for copper adducts. Computational tools were also used to quantitatively understand in detail the bonding situation in these species. It is found that the interaction between the transition metal fragment and the acetylene ligand is significantly stronger in the copper complexes, which is consistent with the experimental findings. The CC distance of these copper and silver acetylene complexes resulting from routine X-ray models suffers due to incomplete deconvolution of thermal smearing and anisotropy of the electron density in acetylene, and is shorter than expected. A method to estimate the CC distance of these metal complexes based on their experimental CC is also presented. Gaseous acetylene can be trapped on copper(i) and silver(i) sites supported by pyrazole-based scorpionates to produce isolable molecules for detailed investigations and the study of metal-acetylene bonding.![]()
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Affiliation(s)
- Anurag Noonikara-Poyil
- Department of Chemistry and Biochemistry, The University of Texas at Arlington, Arlington, Texas 76019, USA
| | - Shawn G. Ridlen
- Department of Chemistry and Biochemistry, The University of Texas at Arlington, Arlington, Texas 76019, USA
| | - Israel Fernández
- Departamento de Química Orgánica I and Centro de Innovación en Química Avanzada (ORFEO-CINQA), Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040-Madrid, Spain
| | - H. V. Rasika Dias
- Department of Chemistry and Biochemistry, The University of Texas at Arlington, Arlington, Texas 76019, USA
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28
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Kang PL, Shang C, Liu ZP. Recent implementations in LASP 3.0: Global neural network potential with multiple elements and better long-range description. CHINESE J CHEM PHYS 2021. [DOI: 10.1063/1674-0068/cjcp2108145] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Pei-lin Kang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science (Ministry of Education), Department of Chemistry, Fudan University, Shanghai 200433, China Shanghai Qi Zhi Institute, Shanghai 200030, China
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science (Ministry of Education), Department of Chemistry, Fudan University, Shanghai 200433, China Shanghai Qi Zhi Institute, Shanghai 200030, China
| | - Zhi-pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science (Ministry of Education), Department of Chemistry, Fudan University, Shanghai 200433, China Shanghai Qi Zhi Institute, Shanghai 200030, China
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29
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Chen D, Lai Z, Zhang J, Chen J, Hu P, Wang H. Gold Segregation Improves Electrocatalytic Activity of Icosahedron Au@Pt Nanocluster: Insights from Machine Learning
†. CHINESE J CHEM 2021. [DOI: 10.1002/cjoc.202100352] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Dingming Chen
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, School of Chemistry and Molecular Engineering East China University of Science and Technology Shanghai 200237 China
| | - Zhuangzhuang Lai
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, School of Chemistry and Molecular Engineering East China University of Science and Technology Shanghai 200237 China
| | - Jiawei Zhang
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, School of Chemistry and Molecular Engineering East China University of Science and Technology Shanghai 200237 China
| | - Jianfu Chen
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, School of Chemistry and Molecular Engineering East China University of Science and Technology Shanghai 200237 China
| | - Peijun Hu
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, School of Chemistry and Molecular Engineering East China University of Science and Technology Shanghai 200237 China
| | - Haifeng Wang
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, School of Chemistry and Molecular Engineering East China University of Science and Technology Shanghai 200237 China
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30
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Liu QY, Shang C, Liu ZP. In Situ Active Site for CO Activation in Fe-Catalyzed Fischer-Tropsch Synthesis from Machine Learning. J Am Chem Soc 2021; 143:11109-11120. [PMID: 34278799 DOI: 10.1021/jacs.1c04624] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
In situ-formed iron carbides (FeCx) are the key components responsible for Fischer-Tropsch synthesis (FTS, CO + H2 → long-chain hydrocarbons) on Fe-based catalysts in industry. The true active site is, however, highly controversial despite more than a century of study, which is largely due to the combined complexity in both FeCx structures and mechanism of CO hydrogenation. Herein powered by machine learning simulation, millions of structure candidates for FeCx bulk and surfaces are explored under FTS conditions, which leads to resolving the active site for CO activation. This is achieved without a priori input from experiment by first constructing the thermodynamics convex hull of bulk phases, followed by identifying the low surface energy surfaces and evaluating the adsorption ability of CO and H, and finally determining the lowest energy reaction pathway of CO activation. Rich information on FeCx structures and CO hydrogenation pathways is gleaned: (i) Fe5C2, Fe7C3, and Fe2C are the three stable bulk phases under FTS in producing olefins, where Fe7C3 and Fe2C have multiple energetically nearly degenerate bulk crystal phases; (ii) only three low surface energy surfaces of these bulk phases, namely, χ-Fe5C2(510), χ-Fe5C2(111), and η-Fe2C(111), expose the Fe sites that can adsorb H atoms exothermically, where the surface Fe:C ratio is 2, 1.75, and 2, respectively; (iii) CO activation via direct dissociation can occur at the surface C vacancies (e.g., with a barrier of 1.1 eV) that are created dynamically via hydrogenation. These atomic-level understandings facilitate the building of the structure-activity correlation and designing better FT catalysts.
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Affiliation(s)
- Qian-Yu Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
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