1
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Suvarna M, Zou T, Chong SH, Ge Y, Martín AJ, Pérez-Ramírez J. Active learning streamlines development of high performance catalysts for higher alcohol synthesis. Nat Commun 2024; 15:5844. [PMID: 38992019 PMCID: PMC11239856 DOI: 10.1038/s41467-024-50215-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 07/01/2024] [Indexed: 07/13/2024] Open
Abstract
Developing efficient catalysts for syngas-based higher alcohol synthesis (HAS) remains a formidable research challenge. The chain growth and CO insertion requirements demand multicomponent materials, whose complex reaction dynamics and extensive chemical space defy catalyst design norms. We present an alternative strategy by integrating active learning into experimental workflows, exemplified via the FeCoCuZr catalyst family. Our data-aided framework streamlines navigation of the extensive composition and reaction condition space in 86 experiments, offering >90% reduction in environmental footprint and costs over traditional programs. It identifies the Fe65Co19Cu5Zr11 catalyst with optimized reaction conditions to attain higher alcohol productivities of 1.1 gHA h-1 gcat-1 under stable operation for 150 h on stream, a 5-fold improvement over typically reported yields. Characterization reveals catalytic properties linked to superior activities despite moderate higher alcohol selectivities. To better reflect catalyst demands, we devise multi-objective optimization to maximize higher alcohol productivity while minimizing undesired CO2 and CH4 selectivities. An intrinsic trade-off between these metrics is uncovered, identifying Pareto-optimal catalysts not readily discernible by human experts. Finally, based on feature-importance analysis, we formulate data-informed guidelines to develop performance-specific FeCoCuZr systems. This approach goes beyond existing HAS catalyst design strategies, is adaptable to broader catalytic transformations, and fosters laboratory sustainability.
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Affiliation(s)
- Manu Suvarna
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland
| | - Tangsheng Zou
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland
| | - Sok Ho Chong
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland
| | - Yuzhen Ge
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland
| | - Antonio J Martín
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland
| | - Javier Pérez-Ramírez
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland.
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2
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Ge X, Yin J, Ren Z, Yan K, Jing Y, Cao Y, Fei N, Liu X, Wang X, Zhou X, Chen L, Yuan W, Duan X. Atomic Design of Alkyne Semihydrogenation Catalysts via Active Learning. J Am Chem Soc 2024; 146:4993-5004. [PMID: 38333965 DOI: 10.1021/jacs.3c14495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Alkyne hydrogenation on palladium-based catalysts modified with silver is currently used in industry to eliminate trace amounts of alkynes in alkenes produced from steam cracking and alkane dehydrogenation processes. Intensive efforts have been devoted to designing an alternative catalyst for improvement, especially in terms of selectivity and catalyst cost, which is still far away from that as expected. Here, we describe an atomic design of a high-performance Ni-based intermetallic catalyst aided by active machine learning combined with density functional theory calculations. The engineered NiIn catalyst exhibits >97% selectivity to ethylene and propylene at the full conversion of acetylene and propyne at mild temperature, outperforming the reported Ni-based catalysts and even noble Pd-based ones. Detailed mechanistic studies using theoretical calculations and advanced characterizations elucidate that the atomic-level defined coordination environment of Ni sites and well-designed hybridization of Ni 3d with In 5p orbital determine the semihydrogenation pathway.
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Affiliation(s)
- Xiaohu Ge
- State Key Laboratory of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Jun Yin
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
| | - Zhouhong Ren
- School of Chemistry and Chemical Engineering, In-situ Center for Physical Sciences, Frontiers Science Center for Transformative Molecules, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Kelin Yan
- State Key Laboratory of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yundao Jing
- State Key Laboratory of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yueqiang Cao
- State Key Laboratory of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Nina Fei
- State Key Laboratory of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Xi Liu
- School of Chemistry and Chemical Engineering, In-situ Center for Physical Sciences, Frontiers Science Center for Transformative Molecules, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiaonan Wang
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
| | - Xinggui Zhou
- State Key Laboratory of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Liwei Chen
- School of Chemistry and Chemical Engineering, In-situ Center for Physical Sciences, Frontiers Science Center for Transformative Molecules, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weikang Yuan
- State Key Laboratory of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Xuezhi Duan
- State Key Laboratory of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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3
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Yanagiyama K, Takimoto K, Dinh Le S, Nu Thanh Ton N, Taniike T. High-throughput experimentation for photocatalytic water purification in practical environments. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 342:122974. [PMID: 37981181 DOI: 10.1016/j.envpol.2023.122974] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 10/26/2023] [Accepted: 11/14/2023] [Indexed: 11/21/2023]
Abstract
High-throughput screening instrument was developed for photocatalytic water purification, enabling the simultaneous testing of 132 photocatalytic reactions under uniform visible light irradiation, temperature control, and stirring. The instrument was used to investigate the effects of different catalysts (TiO2, ZnO, α-Fe2O3) and environmental waters (seawater, urban wastewater, and industrial wastewater) on dye degradation. It was observed environmental ions, particularly carbonate and phosphate ions, significantly reduced catalyst activity by inhibiting the adsorption of dye molecules. To develop effective catalysts for dye degradation in industrial wastewater, 15 types of noble metal nanoparticles (NPs) were supported on photocatalysts. The study found that noble metal NPs with high work functions and oxidation resistance, such as Au and Pt, exhibited higher activity even in the industrial wastewater, likely converting environmental ions into active species. These findings, based on 432 test results, demonstrate the effectiveness of the developed high-throughput screening instrument for optimizing photocatalytic water purification.
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Affiliation(s)
- Kyo Yanagiyama
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan
| | - Ken Takimoto
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan
| | - Son Dinh Le
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan
| | - Nhan Nu Thanh Ton
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan
| | - Toshiaki Taniike
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan.
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4
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Taniike T, Fujiwara A, Nakanowatari S, García-Escobar F, Takahashi K. Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis. Commun Chem 2024; 7:11. [PMID: 38216711 PMCID: PMC10786848 DOI: 10.1038/s42004-023-01086-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 12/08/2023] [Indexed: 01/14/2024] Open
Abstract
The empirical aspect of descriptor design in catalyst informatics, particularly when confronted with limited data, necessitates adequate prior knowledge for delving into unknown territories, thus presenting a logical contradiction. This study introduces a technique for automatic feature engineering (AFE) that works on small catalyst datasets, without reliance on specific assumptions or pre-existing knowledge about the target catalysis when designing descriptors and building machine-learning models. This technique generates numerous features through mathematical operations on general physicochemical features of catalytic components and extracts relevant features for the desired catalysis, essentially screening numerous hypotheses on a machine. AFE yields reasonable regression results for three types of heterogeneous catalysis: oxidative coupling of methane (OCM), conversion of ethanol to butadiene, and three-way catalysis, where only the training set is swapped. Moreover, through the application of active learning that combines AFE and high-throughput experimentation for OCM, we successfully visualize the machine's process of acquiring precise recognition of the catalyst design. Thus, AFE is a versatile technique for data-driven catalysis research and a key step towards fully automated catalyst discoveries.
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Affiliation(s)
- Toshiaki Taniike
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan.
| | - Aya Fujiwara
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan
| | - Sunao Nakanowatari
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan
| | | | - Keisuke Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo, 060-0810, Japan
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5
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Chammingkwan P, Khoshsefat M, Terano M, Taniike T. Parallel Catalyst Synthesis Protocol for Accelerating Heterogeneous Olefin Polymerization Research. Polymers (Basel) 2023; 15:4729. [PMID: 38139980 PMCID: PMC10747057 DOI: 10.3390/polym15244729] [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: 11/14/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023] Open
Abstract
The data scientific approach has become an indispensable tool for capturing structure-performance relationships in complex systems, where the quantity and quality of data play a crucial role. In heterogeneous olefin polymerization research, the exhaustive and multi-step nature of Ziegler-Natta catalyst synthesis has long posed a bottleneck in synthetic throughput and data generation. In this contribution, a custom-designed 12-parallel reactor system and a catalyst synthesis protocol were developed to achieve the parallel synthesis of a magnesium ethoxide-based Ziegler-Natta catalyst. The established system, featuring a miniature reaction vessel with magnetically suspended stirring, allows for over a tenfold reduction in synthetic scale while ensuring the consistency and reliability of the synthesis. We demonstrate that the established protocol is highly efficient for the generation of a catalyst library with diverse compositions and physical features, holding promise as a foundation for the data-driven establishment of the structure-performance relationship in heterogeneous olefin polymerization catalysis.
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Affiliation(s)
- Patchanee Chammingkwan
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1292, Japan; (M.K.); (M.T.)
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6
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Deng S, Chen C, Li K, Chen X, Xia K, Li S. Structure-Based Multilevel Descriptors for High-throughput Screening of Elastomers. J Phys Chem B 2023; 127:10077-10087. [PMID: 37942925 DOI: 10.1021/acs.jpcb.3c06025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
To discover new materials, high-throughput screening (HTS) with machine learning (ML) requires universally available descriptors that can accurately predict the desired properties. For elastomers, experimental and simulation data in current descriptors may not be available for all candidates of interest, hindering elastomer discovery through HTS. To address this challenge, we introduce structure-based multilevel (SM) descriptors of elastomers derived solely from molecular structure that is universally available. Our SM descriptors are hierarchically organized to capture both local soft and hard segment structures as well as the global structures of elastomers. With the SM-Morgan Fingerprint (SM-MF) descriptor, one of our SM descriptors, a machine learning model accurately predicts elastomer toughness with a remarkable accuracy of 0.91. Furthermore, an HTS pipeline is established to swiftly screen elastomers with targeted toughness. We also demonstrate the generality and applicability of SM descriptors by using them to construct HTS pipelines for screening elastomers with a targeted critical strain or Young's modulus. The user-friendliness and low computational cost of SM descriptors make them a promising tool to significantly enhance HTS in the search for novel materials.
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Affiliation(s)
- Siyan Deng
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Chao Chen
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Ke Li
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Xi Chen
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Kelin Xia
- School of Physical and Mathematical Sciences, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Shuzhou Li
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
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7
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Zhang SBXY, Pessemesse Q, Lätsch L, Engel KM, Stark WJ, van Bavel AP, Horton AD, Payard PA, Copéret C. Role and dynamics of transition metal carbides in methane coupling. Chem Sci 2023; 14:5899-5905. [PMID: 37293639 PMCID: PMC10246698 DOI: 10.1039/d3sc01054f] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 05/02/2023] [Indexed: 06/10/2023] Open
Abstract
Transition metal carbides have numerous applications and are known to excel in terms of hardness, thermal stability and conductivity. In particular, the Pt-like behavior of Mo and W carbides has led to the popularization of metal carbides in catalysis, ranging from electrochemically-driven reactions to thermal methane coupling. Herein, we show the active participation of carbidic carbon in the formation of C2 products during methane coupling at high temperature that is associated with the dynamics of Mo and W carbides. A detailed mechanistic study reveals that the catalyst performance of these metal carbides can be traced back to its carbon diffusivity and exchange capability upon interaction with methane (gas phase carbon). A stable C2 selectivity over time on stream for Mo carbide (Mo2C) can be rationalized by fast carbon diffusion dynamics, while W carbide (WC) shows loss of selectivity due to slow diffusion leading to surface carbon depletion. This finding showcases that the bulk carbidic carbon of the catalyst plays a crucial role and that the metal carbide is not only responsible for methyl radical formation. Overall, this study evidences the presence of a carbon equivalent to the Mars-Van Krevelen type mechanism for non-oxidative coupling of methane.
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Affiliation(s)
- Seraphine B X Y Zhang
- Department of Chemistry and Applied Biosciences, ETH Zurich Vladimir-Prelog-Weg 1-5 8093 Zürich Switzerland
| | - Quentin Pessemesse
- Université de Lyon, Université Claude Bernard Lyon I, CNRS, INSA, CPE, UMR 5246, ICBMS 1 rue Victor Grignard 69622 Lyon France
| | - Lukas Lätsch
- Department of Chemistry and Applied Biosciences, ETH Zurich Vladimir-Prelog-Weg 1-5 8093 Zürich Switzerland
| | - Konstantin M Engel
- Department of Chemistry and Applied Biosciences, ETH Zurich Vladimir-Prelog-Weg 1-5 8093 Zürich Switzerland
| | - Wendelin J Stark
- Department of Chemistry and Applied Biosciences, ETH Zurich Vladimir-Prelog-Weg 1-5 8093 Zürich Switzerland
| | | | - Andrew D Horton
- Shell Global Solutions International B. V. Grasweg 31 1031 HW Amsterdam Netherlands
| | - Pierre-Adrien Payard
- Université de Lyon, Université Claude Bernard Lyon I, CNRS, INSA, CPE, UMR 5246, ICBMS 1 rue Victor Grignard 69622 Lyon France
| | - Christophe Copéret
- Department of Chemistry and Applied Biosciences, ETH Zurich Vladimir-Prelog-Weg 1-5 8093 Zürich Switzerland
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8
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Mou LH, Han T, Smith PES, Sharman E, Jiang J. Machine Learning Descriptors for Data-Driven Catalysis Study. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2301020. [PMID: 37191279 PMCID: PMC10401178 DOI: 10.1002/advs.202301020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/07/2023] [Indexed: 05/17/2023]
Abstract
Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML-assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented.
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Affiliation(s)
- Li-Hui Mou
- Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - TianTian Han
- Hefei JiShu Quantum Technology Co. Ltd., Hefei, 230026, China
| | - Pieter E S Smith
- YDS Pharmatech, ETEC, 1220 Washington Ave., Albany, NY, 12203, USA
| | - Edward Sharman
- Department of Neurology, University of California, Irvine, CA, 92697, USA
| | - Jun Jiang
- Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, 230026, China
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9
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Takahashi K, Takahashi L. Toward the Golden Age of Materials Informatics: Perspective and Opportunities. J Phys Chem Lett 2023; 14:4726-4733. [PMID: 37172318 DOI: 10.1021/acs.jpclett.3c00648] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Materials informatics is reaching the transition point and is evolving from its early stages of adoption and development and moving toward its golden age. Here, the transformation of the early stage of materials informatics toward the next level of materials informatics is explored. In particular, it has become crucial to be able to manipulate materials synthesis data, materials properties data, and materials characterization data. Through the use of ontology, material design and understanding can be carried out simultaneously in a whitebox manner. Here, a perspective on the ultimate goal of materials informatics along with potential key components is discussed.
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Affiliation(s)
- Keisuke Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan
| | - Lauren Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan
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10
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The value of negative results in data-driven catalysis research. Nat Catal 2023. [DOI: 10.1038/s41929-023-00920-9] [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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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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12
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Takahashi K, Ohyama J, Nishimura S, Fujima J, Takahashi L, Uno T, Taniike T. Catalysts informatics: paradigm shift towards data-driven catalyst design. Chem Commun (Camb) 2023; 59:2222-2238. [PMID: 36723221 DOI: 10.1039/d2cc05938j] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Designing catalysts is a challenging matter as catalysts are involved with various factors that impact synthesis, catalysts, reactor and reaction. In order to overcome these difficulties, catalysts informatics is proposed as an alternative way to design and understand catalysts. The underlying concept of catalysts informatics is to design the catalysts from trends and patterns found in catalysts data. Here, three key concepts are introduced: experimental catalysts database, knowledge extraction from catalyst data via data science, and a catalysts informatics platform. Methane oxidation is chosen as a prototype reaction for demonstrating various aspects of catalysts informatics. This work summarizes how catalysts informatics plays a role in catalyst design. The work covers big data generation via high throughput experiments, machine learning, catalysts network method, catalyst design from small data, catalysts informatics platform, and the future of catalysts informatics via ontology. Thus, the proposed catalysts informatics would help innovate how catalysts can be designed and understood.
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Affiliation(s)
- Keisuke Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan.
| | - Junya Ohyama
- Faculty of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, 860-8555, Japan
| | - Shun Nishimura
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
| | - Jun Fujima
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan.
| | - Lauren Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan.
| | - Takeaki Uno
- National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, 101-8430, Japan
| | - Toshiaki Taniike
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
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13
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Burgun U, Zonouz HR, Okutan H, Atakül H, Senkan S, Sarioglan A, Gumuslu Gur G. Effects of Rare Earth Metal Promotion over Zeolite-Supported Fe-Cu-Based Catalysts on the Light Olefin Production Performance in Fischer-Tropsch Synthesis. ACS OMEGA 2023; 8:648-662. [PMID: 36643472 PMCID: PMC9835664 DOI: 10.1021/acsomega.2c05795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
Fischer-Tropsch synthesis (FTS), a significant reaction for effective H2 utilization, is a promising approach for direct production of light olefins from syngas (H2 + CO). For the FT-Olefin process, an efficient catalyst restricting the product distribution of FTS to light olefins is required. Aligned with this goal, we synthesized 24 catalysts comprising Fe and Cu in combination with rare earth metals (La, Ce, Nd, Ho, Er) and zeolite supports (ultrastable Y and mordenite). FT-Olefin performances of these catalysts were screened using a high-throughput test system at atmospheric pressure, and then promising catalysts were tested under high pressure in a conventional test system. Results show that Nd increases selectivity to light olefins and Ho suppresses C5+ and coke formation. It is also demonstrated that zeolite-metal interaction, leading to a mixture of both acidic and basic sites, is significant in increasing light olefin production. The mordenite-supported 20 wt % Fe, 0.5 wt % Cu, and 0.5 wt % Ho catalyst provides the highest light olefin yield with the lowest coke and heavier hydrocarbon selectivity.
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Affiliation(s)
- Utku Burgun
- Chemical
Engineering Department, Istanbul Technical
University, 34469Istanbul, Turkey
- ITU
Synthetic Fuels and Chemicals Technology Center, ITU-SENTEK, 34469Istanbul, Turkey
| | - Hadi R. Zonouz
- Chemical
Engineering Department, Istanbul Technical
University, 34469Istanbul, Turkey
- ITU
Synthetic Fuels and Chemicals Technology Center, ITU-SENTEK, 34469Istanbul, Turkey
| | - Hasancan Okutan
- Chemical
Engineering Department, Istanbul Technical
University, 34469Istanbul, Turkey
- ITU
Synthetic Fuels and Chemicals Technology Center, ITU-SENTEK, 34469Istanbul, Turkey
| | - Hüsnü Atakül
- Chemical
Engineering Department, Istanbul Technical
University, 34469Istanbul, Turkey
- ITU
Synthetic Fuels and Chemicals Technology Center, ITU-SENTEK, 34469Istanbul, Turkey
| | - Selim Senkan
- ITU
Synthetic Fuels and Chemicals Technology Center, ITU-SENTEK, 34469Istanbul, Turkey
- Chemical
and Biomolecular Engineering Department, University of California, Los Angeles, Los Angeles, California90095, United States
| | - Alper Sarioglan
- Chemical
Engineering Department, Istanbul Technical
University, 34469Istanbul, Turkey
- ITU
Synthetic Fuels and Chemicals Technology Center, ITU-SENTEK, 34469Istanbul, Turkey
| | - Gamze Gumuslu Gur
- Chemical
Engineering Department, Istanbul Technical
University, 34469Istanbul, Turkey
- ITU
Synthetic Fuels and Chemicals Technology Center, ITU-SENTEK, 34469Istanbul, Turkey
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14
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Jing W, Shen H, Qin R, Wu Q, Liu K, Zheng N. Surface and Interface Coordination Chemistry Learned from Model Heterogeneous Metal Nanocatalysts: From Atomically Dispersed Catalysts to Atomically Precise Clusters. Chem Rev 2022; 123:5948-6002. [PMID: 36574336 DOI: 10.1021/acs.chemrev.2c00569] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The surface and interface coordination structures of heterogeneous metal catalysts are crucial to their catalytic performance. However, the complicated surface and interface structures of heterogeneous catalysts make it challenging to identify the molecular-level structure of their active sites and thus precisely control their performance. To address this challenge, atomically dispersed metal catalysts (ADMCs) and ligand-protected atomically precise metal clusters (APMCs) have been emerging as two important classes of model heterogeneous catalysts in recent years, helping to build bridge between homogeneous and heterogeneous catalysis. This review illustrates how the surface and interface coordination chemistry of these two types of model catalysts determines the catalytic performance from multiple dimensions. The section of ADMCs starts with the local coordination structure of metal sites at the metal-support interface, and then focuses on the effects of coordinating atoms, including their basicity and hardness/softness. Studies are also summarized to discuss the cooperativity achieved by dual metal sites and remote effects. In the section of APMCs, the roles of surface ligands and supports in determining the catalytic activity, selectivity, and stability of APMCs are illustrated. Finally, some personal perspectives on the further development of surface coordination and interface chemistry for model heterogeneous metal catalysts are presented.
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Affiliation(s)
- Wentong Jing
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, and National & Local Joint Engineering Research Center for Preparation Technology of Nanomaterials, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Hui Shen
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, and National & Local Joint Engineering Research Center for Preparation Technology of Nanomaterials, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Ruixuan Qin
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, and National & Local Joint Engineering Research Center for Preparation Technology of Nanomaterials, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Qingyuan Wu
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, and National & Local Joint Engineering Research Center for Preparation Technology of Nanomaterials, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361102, China
| | - Kunlong Liu
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, and National & Local Joint Engineering Research Center for Preparation Technology of Nanomaterials, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Nanfeng Zheng
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, and National & Local Joint Engineering Research Center for Preparation Technology of Nanomaterials, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361102, China
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15
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Suvarna M, Preikschas P, Pérez-Ramírez J. Identifying Descriptors for Promoted Rhodium-Based Catalysts for Higher Alcohol Synthesis via Machine Learning. ACS Catal 2022; 12:15373-15385. [PMID: 36570082 PMCID: PMC9765739 DOI: 10.1021/acscatal.2c04349] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 10/28/2022] [Indexed: 12/05/2022]
Abstract
Rhodium-based catalysts offer remarkable selectivities toward higher alcohols, specifically ethanol, via syngas conversion. However, the addition of metal promoters is required to increase reactivity, augmenting the complexity of the system. Herein, we present an interpretable machine learning (ML) approach to predict and rationalize the performance of Rh-Mn-P/SiO2 catalysts (P = 19 promoters) using the open-source dataset on Rh-catalyzed higher alcohol synthesis (HAS) from Pacific Northwest National Laboratory (PNNL). A random forest model trained on this dataset comprising 19 alkali, transition, post-transition metals, and metalloid promoters, using catalytic descriptors and reaction conditions, predicts the higher alcohols space-time yield (STYHA) with an accuracy of R 2 = 0.76. The promoter's cohesive energy and alloy formation energy with Rh are revealed as significant descriptors during posterior feature-importance analysis. Their interplay is captured as a dimensionless property, coined promoter affinity index (PAI), which exhibits volcano correlations for space-time yield. Based on this descriptor, we develop guidelines for the rational selection of promoters in designing improved Rh-Mn-P/SiO2 catalysts. This study highlights ML as a tool for computational screening and performance prediction of unseen catalysts and simultaneously draws insights into the property-performance relations of complex catalytic systems.
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Affiliation(s)
- Manu Suvarna
- Institute for Chemical and
Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093Zurich, Switzerland
| | - Phil Preikschas
- Institute for Chemical and
Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093Zurich, Switzerland
| | - Javier Pérez-Ramírez
- Institute for Chemical and
Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093Zurich, Switzerland
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16
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New paradigms for exploiting parallel experiments in Bayesian optimization. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.108110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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17
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Rangarajan S, Tian H. Improving the predictive power of microkinetic models via machine learning. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2022.100858] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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18
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Ishioka S, Fujiwara A, Nakanowatari S, Takahashi L, Taniike T, Takahashi K. Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane. ACS Catal 2022. [DOI: 10.1021/acscatal.2c03142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Sora Ishioka
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan
| | - Aya Fujiwara
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
| | - Sunao Nakanowatari
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
| | - Lauren Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan
| | - Toshiaki Taniike
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
| | - Keisuke Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan
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19
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Chen YY, Ross Kunz M, He X, Fushimi R. Recent progress toward catalyst properties, performance, and prediction with data-driven methods. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2022.100843] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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20
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Takahashi K, Takahashi L, Le SD, Kinoshita T, Nishimura S, Ohyama J. Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation. J Am Chem Soc 2022; 144:15735-15744. [PMID: 35984913 DOI: 10.1021/jacs.2c06143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The coupling of high-throughput calculations with catalyst informatics is proposed as an alternative way to design heterogeneous catalysts. High-throughput first-principles calculations for the oxidative coupling of methane (OCM) reaction are designed and performed where 1972 catalyst surface planes for the CH4 to CH3 reaction are calculated. Several catalysts for the OCM reaction are designed based on key elements that are unveiled via data visualization and network analysis. Among the designed catalysts, several active catalysts such as CoAg/TiO2, Mg/BaO, and Ti/BaO are found to result in high C2 yield. Results illustrate that designing catalysts using high-throughput calculations is achievable in principle if appropriate trends and patterns within the data generated via high-throughput calculations are identified. Thus, high-throughput calculations in combination with catalyst informatics offer a potential alternative method for catalyst design.
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Affiliation(s)
- Keisuke Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-8510, Japan
| | - Lauren Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-8510, Japan
| | - Son Dinh Le
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1292, Japan
| | - Takaaki Kinoshita
- Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto 860-8555, Japan
| | - Shun Nishimura
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1292, Japan
| | - Junya Ohyama
- Faculty of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto 860-8555, Japan
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21
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Metal Ions (Li, Mg, Zn, Ce) Doped into La2O3 Nanorod for Boosting Catalytic Oxidative Coupling of Methane. Catalysts 2022. [DOI: 10.3390/catal12070713] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
A series of La2O3 nanorod catalysts with doping of active metal ions (Li, Mg, Zn and Ce) were synthesized successfully by the hydrothermal method. The La2O3 nanorods show a uniform size with the length of 50–200 nm and the width of 5–20 nm, and the {110} crystal facet is a preferentially exposed surface. The active metal ions (Li, Mg, Zn and Ce) doped into the lattice of La2O3 nanorods enhance the selectivity of the desired products during oxidative coupling of methane (OCM) and decrease the reaction temperature. Among these catalysts, the Mg-La2O3 catalyst exhibits the best catalytic performance during the OCM reaction, i.e., its selectivity and yield of C2 products at 780 °C is 73% and 21%, respectively. The effect of doped metal ions on catalytic activity for OCM was systematically investigated. Insight into the fabrication strategy and promoting factors of the OCM reaction indicates the potential to further design a high-efficient catalyst in the future.
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22
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Nishimura S, Ohyama J, Li X, Miyazato I, Taniike T, Takahashi K. Machine Learning-Aided Catalyst Modification in Oxidative Coupling of Methane via Manganese Promoter. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c05079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Shun Nishimura
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1292, Japan
| | - Junya Ohyama
- Faculty of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto 860-8555, Japan
| | - Xinyue Li
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1292, Japan
| | - Itsuki Miyazato
- Department of Chemistry, Hokkaido University, N-10 W-8, Sapporo 060-0810, Japan
| | - Toshiaki Taniike
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1292, Japan
| | - Keisuke Takahashi
- Department of Chemistry, Hokkaido University, N-10 W-8, Sapporo 060-0810, Japan
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23
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Yang RX, Jan K, Chen CT, Chen WT, Wu KCW. Thermochemical Conversion of Plastic Waste into Fuels, Chemicals, and Value-Added Materials: A Critical Review and Outlooks. CHEMSUSCHEM 2022; 15:e202200171. [PMID: 35349769 DOI: 10.1002/cssc.202200171] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/27/2022] [Indexed: 06/14/2023]
Abstract
Plastic waste is an emerging environmental issue for our society. Critical action to tackle this problem is to upcycle plastic waste as valuable feedstock. Thermochemical conversion of plastic waste has received growing attention. Although thermochemical conversion is promising for handling mixed plastic waste, it typically occurs at high temperatures (300-800 °C). Catalysts can play a critical role in improving the energy efficiency of thermochemical conversion, promoting targeted reactions, and improving product selectivity. This Review aims to summarize the state-of-the-art of catalytic thermochemical conversions of various types of plastic waste. First, general trends and recent development of catalytic thermochemical conversions including pyrolysis, gasification, hydrothermal processes, and chemolysis of plastic waste into fuels, chemicals, and value-added materials were reviewed. Second, the status quo for the commercial implementation of thermochemical conversion of plastic waste was summarized. Finally, the current challenges and future perspectives of catalytic thermochemical conversion of plastic waste including the design of sustainable and robust catalysts were discussed.
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Affiliation(s)
- Ren-Xuan Yang
- Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA 01851, USA
- Department of Chemical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10607, Taiwan
- Institute of Environmental Engineering and Management, National Taipei University of Technology, No.1 Sec. 3, Chung-Hsiao E. Rd., Taipei, 106344, Taiwan
| | - Kalsoom Jan
- Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA 01851, USA
| | - Ching-Tien Chen
- Department of Chemical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10607, Taiwan
| | - Wan-Ting Chen
- Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA 01851, USA
| | - Kevin C-W Wu
- Department of Chemical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10607, Taiwan
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24
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Bennett JA, Abolhasani M. Autonomous chemical science and engineering enabled by self-driving laboratories. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2022.100831] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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25
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Sulphur Oxidative Coupling of Methane process development and its modelling via Machine Learning. AIChE J 2022. [DOI: 10.1002/aic.17793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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26
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Si J, Zhao G, Sun W, Liu J, Guan C, Yang Y, Shi XR, Lu Y. Oxidative Coupling of Methane: Examining the Inactivity of the MnO x -Na 2 WO 4 /SiO 2 Catalyst at Low Temperature. Angew Chem Int Ed Engl 2022; 61:e202117201. [PMID: 35181983 DOI: 10.1002/anie.202117201] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Indexed: 11/07/2022]
Abstract
Oxidative coupling of methane (OCM) catalyzed by MnOx -Na2 WO4 /SiO2 has great industrial promise to convert methane directly to C2-3 products, but its high light-off temperature is the most challenging obstacle to commercialization and its working mechanism is still a mystery. We report the discovery of a low-temperature active and selective MnOx -Na2 WO4 /SiO2 catalyst enriched with Q2 units in the SiO2 carrier, being capable of converting 23 % CH4 with 72 % C2-3 selectivity at 660 °C. From experiments and theoretical calculations, a large number of Q2 units in the MnOx -Na2 WO4 /SiO2 catalyst is a trigger for markedly lowering the light-off temperature of the Mn3+ ↔Mn2+ redox cycle involved in the OCM reaction because of the easy formation of MnSiO3 . Notably, the MnSiO3 formation proceeds merely through the SiO2 -involved reaction in the presence of Na2 WO4 : Mn7 SiO12 +6 SiO2 ↔7 MnSiO3 +1.5 O2 . The Na2 WO4 not only drives the light-off of this cycle but also gets it working with substantial selectivity toward C2-3 products. Our findings shine a light on the rational design of more advanced MnOx -Na2 WO4 based OCM catalysts through establishing new Mn3+ ↔Mn2+ redox cycles with lowered light-off temperature.
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Affiliation(s)
- Jiaqi Si
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, East China Normal University, Shanghai, 200062, China
| | - Guofeng Zhao
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, East China Normal University, Shanghai, 200062, China
| | - Weidong Sun
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, East China Normal University, Shanghai, 200062, China
| | - Jincun Liu
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, East China Normal University, Shanghai, 200062, China
| | - Cairu Guan
- School of Physical Science and Technology, ShanghaiTech University, 100 Haike Road, Shanghai, 201210, China
| | - Yong Yang
- School of Physical Science and Technology, ShanghaiTech University, 100 Haike Road, Shanghai, 201210, China
| | - Xue-Rong Shi
- Department of Materials Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China
| | - Yong Lu
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, East China Normal University, Shanghai, 200062, China.,Institute of Eco-Chongming, Shanghai, 202162, China
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27
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Si J, Zhao G, Sun W, Liu J, Guan C, Yang Y, Shi XR, Lu Y. Oxidative Coupling of Methane: Examining the Inactivity of the MnOx‐Na2WO4/SiO2 Catalyst at Low Temperature. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202117201] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jiaqi Si
- East China Normal University School of chemistry and molecular engineering CHINA
| | - Guofeng Zhao
- East China Normal University School of chemistry and molecular engineering CHINA
| | - Weidong Sun
- East China Normal University School of chemistry and molecular engineering CHINA
| | - Jincun Liu
- East China Normal University Scool of chemistry and molecular engineering CHINA
| | - Cairu Guan
- ShanghaiTech University - Zhangjiang Campus: ShanghaiTech University School of physical Science and Technology CHINA
| | - Yong Yang
- ShanghaiTech University - Zhangjiang Campus: ShanghaiTech University School of physical science and technology CHINA
| | - Xue-Rong Shi
- Shanghai University of Engineering Science - Songjiang Campus: Shanghai University of Engineering Science Department of Materials Engineering CHINA
| | - Yong Lu
- East China Normal University School of Chemistry and Molecular Engineering 3663 North Zhongshan Road 200062 Shanghai CHINA
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28
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Thum L, Riedel W, Milojevic N, Guan C, Trunschke A, Dinse KP, Risse T, Schomäcker R, Schlögl R. Transition-Metal-Doping of CaO as Catalyst for the OCM Reaction, a Reality Check. Front Chem 2022; 10:768426. [PMID: 35223767 PMCID: PMC8876934 DOI: 10.3389/fchem.2022.768426] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/17/2022] [Indexed: 11/13/2022] Open
Abstract
In this study, first-row transition metal-doped calcium oxide materials (Mn, Ni, Cr, Co., and Zn) were synthesized, characterized, and tested for the OCM reaction. Doped carbonate precursors were prepared by a co-precipitation method. The synthesis parameters were optimized to yield materials with a pure calcite phase, which was verified by XRD. EPR measurements on the doped CaO materials indicate a successful substitution of Ca2+ with transition metal ions in the CaO lattice. The materials were tested for their performance in the OCM reaction, where a beneficial effect towards selectivity and activity effect could be observed for Mn, Ni, and Zn-doped samples, where the selectivity of Co- and Cr-doped CaO was strongly reduced. The optimum doping concentration could be identified in the range of 0.04-0.10 atom%, showing the strongest decrease in the apparent activation energy, as well as the maximum increase in selectivity.
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Affiliation(s)
- Lukas Thum
- Technische Universität Berlin, Fakultät II, Institut für Chemie, Berlin, Germany
- Department of Inorganic Chemistry, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany
| | - Wiebke Riedel
- Freie Universität Berlin, Institut für Chemie und Biochemie, Berlin, Germany
| | - Natasa Milojevic
- Technische Universität Berlin, Fakultät II, Institut für Chemie, Berlin, Germany
| | - Chengyue Guan
- BasCat—UniCat BASF JointLab, Technische Universität Berlin, Berlin, Germany
| | - Annette Trunschke
- Department of Inorganic Chemistry, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany
| | - Klaus-Peter Dinse
- Freie Universität Berlin, Institut für Experimentalphysik, Berlin, Germany
| | - Thomas Risse
- Freie Universität Berlin, Institut für Chemie und Biochemie, Berlin, Germany
| | - Reinhard Schomäcker
- Technische Universität Berlin, Fakultät II, Institut für Chemie, Berlin, Germany
- *Correspondence: Reinhard Schomäcker,
| | - Robert Schlögl
- Department of Inorganic Chemistry, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany
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29
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Oh KH, Lee HK, Kang SW, Yang JI, Nam G, Lim T, Lee SH, Hong CS, Park JC. Automated synthesis and data accumulation for fast production of high-performance Ni nanocatalysts. J IND ENG CHEM 2022. [DOI: 10.1016/j.jiec.2021.11.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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30
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Li Q, Ouyang Y, Li H, Wang L, Zeng J. Photocatalytic Conversion of Methane: Recent Advancements and Prospects. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202108069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Qi Li
- State Key Laboratory for Powder Metallurgy School of Materials Science and Engineering Central South University Changsha Hunan 410083 P. R. China
| | - Yuxing Ouyang
- State Key Laboratory for Powder Metallurgy School of Materials Science and Engineering Central South University Changsha Hunan 410083 P. R. China
| | - Hongliang Li
- Hefei National Laboratory for Physical Sciences at the Microscale Key Laboratory of Strongly-Coupled Quantum Matter Physics of Chinese Academy of Sciences Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes Department of Chemical Physics University of Science and Technology of China Hefei Anhui 230026 P. R. China
| | - Liangbing Wang
- State Key Laboratory for Powder Metallurgy School of Materials Science and Engineering Central South University Changsha Hunan 410083 P. R. China
| | - Jie Zeng
- Hefei National Laboratory for Physical Sciences at the Microscale Key Laboratory of Strongly-Coupled Quantum Matter Physics of Chinese Academy of Sciences Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes Department of Chemical Physics University of Science and Technology of China Hefei Anhui 230026 P. R. China
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31
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Chen K, Tian H, Li B, Rangarajan S. A chemistry‐inspired neural network kinetic model for oxidative coupling of methane from high‐throughput data. AIChE J 2022. [DOI: 10.1002/aic.17584] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Kexin Chen
- Department of Chemical and Biomolecular Engineering Lehigh University Bethlehem Pennsylvania USA
| | - Huijie Tian
- Department of Chemical and Biomolecular Engineering Lehigh University Bethlehem Pennsylvania USA
| | - Bowen Li
- Department of Chemical and Biomolecular Engineering Lehigh University Bethlehem Pennsylvania USA
| | - Srinivas Rangarajan
- Department of Chemical and Biomolecular Engineering Lehigh University Bethlehem Pennsylvania USA
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32
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Nishimura S, Le SD, Miyazato I, Fujima J, Taniike T, Ohyama J, Takahashi K. High-Throughput Screening and Literature Data Driven Machine Learning Assisting Investigation of Multi-component La2O3-based Catalysts for Oxidative Coupling of Methane. Catal Sci Technol 2022. [DOI: 10.1039/d1cy02206g] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Multi-component La2O3-based catalysts for oxidative coupling of methane (OCM) were designed based on high-throughput screening (HTS) and literature datasets with multi-output machine learning (ML) approaches including random forest regression (RFR),...
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33
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Trunschke A. Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective. Catal Sci Technol 2022. [DOI: 10.1039/d2cy00275b] [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
Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures.
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Affiliation(s)
- Annette Trunschke
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Department of Inorganic Chemistry, Faradayweg 4-6, 14195 Berlin, Germany
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34
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Oxidative Coupling of Methane for Ethylene Production: Reviewing Kinetic Modelling Approaches, Thermodynamics and Catalysts. Processes (Basel) 2021. [DOI: 10.3390/pr9122196] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Ethylene production via oxidative coupling of methane (OCM) represents an interesting route for natural gas upscaling, being the focus of intensive research worldwide. Here, OCM developments are analysed in terms of kinetic mechanisms and respective applications in chemical reactor models, discussing current challenges and directions for further developments. Furthermore, some thermodynamic aspects of the OCM reactions are also revised, providing achievable olefins yields in a wide range of operational reaction conditions. Finally, OCM catalysts are reviewed in terms of respective catalytic performances and thermal stability, providing an executive summary for future studies on OCM economic feasibility.
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35
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Lezcano G, Velisoju VK, Kulkarni SR, Ramirez A, Castaño P. Engineering Thermally Resistant Catalytic Particles for Oxidative Coupling of Methane Using Spray-Drying and Incorporating SiC. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c02802] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Gontzal Lezcano
- KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Vijay K. Velisoju
- KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Shekhar R. Kulkarni
- KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Adrian Ramirez
- KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Pedro Castaño
- KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
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36
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Kiani D, Sourav S, Wachs IE, Baltrusaitis J. A combined computational and experimental study of methane activation during oxidative coupling of methane (OCM) by surface metal oxide catalysts. Chem Sci 2021; 12:14143-14158. [PMID: 34760199 PMCID: PMC8565385 DOI: 10.1039/d1sc02174e] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Accepted: 10/04/2021] [Indexed: 11/21/2022] Open
Abstract
The experimentally validated computational models developed herein, for the first time, show that Mn-promotion does not enhance the activity of the surface Na2WO4 catalytic active sites for CH4 heterolytic dissociation during OCM. Contrary to previous understanding, it is demonstrated that Mn-promotion poisons the surface WO4 catalytic active sites resulting in surface WO5 sites with retarded kinetics for C-H scission. On the other hand, dimeric Mn2O5 surface sites, identified and studied via ab initio molecular dynamics and thermodynamics, were found to be more efficient in activating CH4 than the poisoned surface WO5 sites or the original WO4 sites. However, the surface reaction intermediates formed from CH4 activation over the Mn2O5 surface sites are more stable than those formed over the Na2WO4 surface sites. The higher stability of the surface intermediates makes their desorption unfavorable, increasing the likelihood of over-oxidation to CO x , in agreement with the experimental findings in the literature on Mn-promoted catalysts. Consequently, the Mn-promoter does not appear to have an essential positive role in synergistically tuning the structure of the Na2WO4 surface sites towards CH4 activation but can yield MnO x surface sites that activate CH4 faster than Na2WO4 surface sites, but unselectively.
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Affiliation(s)
- Daniyal Kiani
- Department of Chemical and Biomolecular Engineering, Lehigh University B336 Iacocca Hall, 111 Research Drive Bethlehem PA 18015 USA
| | - Sagar Sourav
- Department of Chemical and Biomolecular Engineering, Lehigh University B336 Iacocca Hall, 111 Research Drive Bethlehem PA 18015 USA
| | - Israel E Wachs
- Department of Chemical and Biomolecular Engineering, Lehigh University B336 Iacocca Hall, 111 Research Drive Bethlehem PA 18015 USA
| | - Jonas Baltrusaitis
- Department of Chemical and Biomolecular Engineering, Lehigh University B336 Iacocca Hall, 111 Research Drive Bethlehem PA 18015 USA
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37
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Kwon D, Yang I, Cho J, Jung JC. Waste-derived calcium oxide catalysts for oxidative coupling of methane. MOLECULAR CATALYSIS 2021. [DOI: 10.1016/j.mcat.2021.111982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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38
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Nguyen TN, Seenivasan K, Nakanowatari S, Mohan P, Tran TPN, Nishimura S, Takahashi K, Taniike T. Factors to influence low-temperature performance of supported Mn–Na2WO4 in oxidative coupling of methane. MOLECULAR CATALYSIS 2021. [DOI: 10.1016/j.mcat.2021.111976] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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39
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Oaki Y, Igarashi Y. Materials Informatics for 2D Materials Combined with Sparse Modeling and Chemical Perspective: Toward Small-Data-Driven Chemistry and Materials Science. BULLETIN OF THE CHEMICAL SOCIETY OF JAPAN 2021. [DOI: 10.1246/bcsj.20210253] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Yuya Oaki
- Department of Applied Chemistry, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan
- JST, PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
| | - Yasuhiko Igarashi
- Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
- JST, PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
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40
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Takahashi L, Nguyen TN, Nakanowatari S, Fujiwara A, Taniike T, Takahashi K. Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts. Chem Sci 2021; 12:12546-12555. [PMID: 34703540 PMCID: PMC8494033 DOI: 10.1039/d1sc04390k] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 08/27/2021] [Indexed: 12/01/2022] Open
Abstract
Designing high performance catalysts for the oxidative coupling of methane (OCM) reaction is often hindered by inconsistent catalyst data, which often leads to difficulties in extracting information such as combinatorial effects of elements upon catalyst performance as well as difficulties in reaching yields beyond a particular threshold. In order to investigate C2 yields more systematically, high throughput experiments are conducted in an effort to mass-produce catalyst-related data in a way that provides more consistency and structure. Graph theory is applied in order to visualize underlying trends in the transformation of high-throughput data into networks, which are then used to design new catalysts that potentially result in high C2 yields during the OCM reaction. Transforming high-throughput data in this manner has resulted in a representation of catalyst data that is more intuitive to use and also has resulted in the successful design of a myriad of catalysts that elicit high C2 yields, several of which resulted in yields greater than those originally reported in the high-throughput data. Thus, transforming high-throughput catalytic data into catalyst design-friendly maps provides a new method of catalyst design that is more efficient and has a higher likelihood of resulting in high performance catalysts. Catalyst data created through high-throughput experimentation is transformed into catalyst knowledge networks, leading to a new method of catalyst design where successfully designed catalysts result in high C2 yields during the OCM reaction.![]()
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Affiliation(s)
- Lauren Takahashi
- Department of Chemistry, Hokkaido University North 10, West 8 Sapporo 060-8510 Japan
| | - Thanh Nhat Nguyen
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology 1-1 Asahidai Nomi Ishikawa 923-1292 Japan
| | - Sunao Nakanowatari
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology 1-1 Asahidai Nomi Ishikawa 923-1292 Japan
| | - Aya Fujiwara
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology 1-1 Asahidai Nomi Ishikawa 923-1292 Japan
| | - Toshiaki Taniike
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology 1-1 Asahidai Nomi Ishikawa 923-1292 Japan
| | - Keisuke Takahashi
- Department of Chemistry, Hokkaido University North 10, West 8 Sapporo 060-8510 Japan
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41
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Joo C, Park H, Lim J, Cho H, Kim J. Development of physical property prediction models for polypropylene composites with optimizing random forest hyperparameters. INT J INTELL SYST 2021. [DOI: 10.1002/int.22700] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Chonghyo Joo
- Green Materials and Processes R&D Group Korea Institute of Industrial Technology Jung‐gu, Ulsan Republic of Korea
- Department of Chemical Engineering Konkuk University Gwangjin‐gu, Seoul Republic of Korea
| | - Hyundo Park
- Green Materials and Processes R&D Group Korea Institute of Industrial Technology Jung‐gu, Ulsan Republic of Korea
- Department of Chemical and Biomolecular Engineering Yonsei University Seodaemun‐gu, Seoul Republic of Korea
| | - Jongkoo Lim
- Research & Development Center GS Caltex Corporation Yuseon‐gu, Daejeon Republic of Korea
| | - Hyungtae Cho
- Green Materials and Processes R&D Group Korea Institute of Industrial Technology Jung‐gu, Ulsan Republic of Korea
| | - Junghwan Kim
- Green Materials and Processes R&D Group Korea Institute of Industrial Technology Jung‐gu, Ulsan Republic of Korea
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42
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Tsuji Y, Yoshioka Y, Hori M, Yoshizawa K. Exploring Metal Cluster Catalysts Using Swarm Intelligence: Start with Hydrogen Adsorption. Top Catal 2021. [DOI: 10.1007/s11244-021-01512-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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43
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Haraguchi Y, Igarashi Y, Imai H, Oaki Y. Size‐Distribution Control of Exfoliated Nanosheets Assisted by Machine Learning: Small‐Data‐Driven Materials Science Using Sparse Modeling. ADVANCED THEORY AND SIMULATIONS 2021. [DOI: 10.1002/adts.202100158] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Yuri Haraguchi
- Department of Applied Chemistry Faculty of Science and Technology Keio University 3‐14‐1 Hiyoshi Kohoku‐ku Yokohama 223–8522 Japan
| | - Yasuhiko Igarashi
- Faculty of Engineering Information and Systems University of Tsukuba 1‐1‐1 Tennodai Tsukuba 305–8573 Japan
- JST PRESTO 4‐1‐8 Honcho Kawaguchi Saitama 332‐0012 Japan
| | - Hiroaki Imai
- Department of Applied Chemistry Faculty of Science and Technology Keio University 3‐14‐1 Hiyoshi Kohoku‐ku Yokohama 223–8522 Japan
| | - Yuya Oaki
- Department of Applied Chemistry Faculty of Science and Technology Keio University 3‐14‐1 Hiyoshi Kohoku‐ku Yokohama 223–8522 Japan
- JST PRESTO 4‐1‐8 Honcho Kawaguchi Saitama 332‐0012 Japan
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44
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Takahashi K, Fujima J, Miyazato I, Nakanowatari S, Fujiwara A, Nguyen TN, Taniike T, Takahashi L. Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts. J Phys Chem Lett 2021; 12:7335-7341. [PMID: 34327995 DOI: 10.1021/acs.jpclett.1c02111] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Identification of catalysts is a difficult matter as catalytic activities involve a vast number of complex features that each catalyst possesses. Here, catalysis gene expression profiling is proposed from unique features discovered in catalyst data collected by high-throughput experiments as an alternative way of representing the catalysts. Combining constructed catalyst gene sequences with hierarchical clustering results in catalyst gene expression profiling where natural language processing is used to identify similar catalysts based on edit distance. In addition, catalysts with similar properties are designed by modifying catalyst genes where the designed catalysts are experimentally confirmed to have catalytic activities that are associated with their catalyst gene sequences. Thus, the proposed method of catalyst gene expressions allows for a novel way of describing catalysts that allows for similarities in catalysts and catalytic activity to be easily recognized while enabling the ability to design new catalysts based on manipulating chemical elements of catalysts with similar catalyst gene sequences.
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Affiliation(s)
- Keisuke Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-8510, Japan
| | - Jun Fujima
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-8510, Japan
| | - Itsuki Miyazato
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-8510, Japan
| | - Sunao Nakanowatari
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
| | - Aya Fujiwara
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
| | - Thanh Nhat Nguyen
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
| | - Toshiaki Taniike
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
| | - Lauren Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-8510, Japan
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45
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Ishioka S, Miyazato I, Takahashi L, Nguyen TN, Taniike T, Takahashi K. Unveiling gas-phase oxidative coupling of methane via data analysis. J Comput Chem 2021; 42:1447-1451. [PMID: 34018210 DOI: 10.1002/jcc.26554] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/20/2021] [Accepted: 04/26/2021] [Indexed: 01/04/2023]
Abstract
Unveiling the details of the mechanisms of a chemical reaction is a difficult task as reaction mechanisms are strongly coupled with reaction conditions. Here, catalysts informatics combined with high-throughput experimental data is implemented to understand the oxidative coupling of methane (OCM) reaction. In particular, pairwise correlation and data visualization are performed to reveal the relation between reaction conditions and selectivity/conversion. In addition, machine learning is used to fill the gap between experimental data points; thus, a more detailed understanding of the OCM reaction against reaction conditions can be achieved. Therefore, catalysts informatics is proposed for understanding the details of the reaction mechanism, thereby aiding reaction design.
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Affiliation(s)
- Sora Ishioka
- Department of Chemistry, Hokkaido University, Sapporo, Japan
| | - Itsuki Miyazato
- Department of Chemistry, Hokkaido University, Sapporo, Japan
| | | | - Thanh Nhat Nguyen
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
| | - Toshiaki Taniike
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
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46
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Li Q, Ouyang Y, Li H, Wang L, Zeng J. Photocatalytic Conversion of Methane: Recent Advancements and Prospects. Angew Chem Int Ed Engl 2021; 61:e202108069. [PMID: 34309996 DOI: 10.1002/anie.202108069] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Indexed: 11/07/2022]
Abstract
Abundant and affordable methane is not only a high-quality fossil fuel, it is also a raw material for the synthesis of value-added chemicals. Solar-energy-driven conversion of methane offers a promising approach to directly transform methane to valuable energy sources under mild conditions, but remains a great challenge at present. In this Review, recent advances in the photocatalytic conversion of methane are systematically summarized. Insights into the construction of effective semiconductor-based photocatalysts from the perspective of light-absorption units and active centers are highlighted and discussed in detail. The performance of various photocatalysts in the conversion of methane is presented, with the photooxidation classified according to the oxidant systems. Lastly, challenges and future perspectives in the photocatalytic oxidation of methane are described.
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Affiliation(s)
- Qi Li
- State Key Laboratory for Powder Metallurgy, School of Materials Science and Engineering, Central South University, Changsha, Hunan, 410083, P. R. China
| | - Yuxing Ouyang
- State Key Laboratory for Powder Metallurgy, School of Materials Science and Engineering, Central South University, Changsha, Hunan, 410083, P. R. China
| | - Hongliang Li
- Hefei National Laboratory for Physical Sciences at the Microscale, Key Laboratory of Strongly-Coupled Quantum Matter Physics of Chinese Academy of Sciences, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China
| | - Liangbing Wang
- State Key Laboratory for Powder Metallurgy, School of Materials Science and Engineering, Central South University, Changsha, Hunan, 410083, P. R. China
| | - Jie Zeng
- Hefei National Laboratory for Physical Sciences at the Microscale, Key Laboratory of Strongly-Coupled Quantum Matter Physics of Chinese Academy of Sciences, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China
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47
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Yabushita M, Yoshida M, Osuga R, Muto F, Iguchi S, Yasuda S, Neya A, Horie M, Maki S, Kanie K, Yamanaka I, Yokoi T, Muramatsu A. Mechanochemical Route for Preparation of MFI-Type Zeolites Containing Highly Dispersed and Small Ce Species and Catalytic Application to Low-Temperature Oxidative Coupling of Methane. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c01664] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Mizuho Yabushita
- Department of Applied Chemistry, School of Engineering, Tohoku University, 6-6-07 Aoba, Aramaki, Aoba-ku, Sendai, Miyagi 980-8579, Japan
| | - Motohiro Yoshida
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
| | - Ryota Osuga
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
| | - Fumiya Muto
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
| | - Shoji Iguchi
- Department of Chemical Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan
| | - Shuhei Yasuda
- Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
| | - Atsushi Neya
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
| | - Mami Horie
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
| | - Sachiko Maki
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
- International Center for Synchrotron Radiation Innovation Smart, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
| | - Kiyoshi Kanie
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
- International Center for Synchrotron Radiation Innovation Smart, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
| | - Ichiro Yamanaka
- Department of Chemical Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan
- Core Research for Evolutional Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
| | - Toshiyuki Yokoi
- Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
| | - Atsushi Muramatsu
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
- International Center for Synchrotron Radiation Innovation Smart, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
- Core Research for Evolutional Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
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Mine S, Takao M, Yamaguchi T, Toyao T, Maeno Z, Hakim Siddiki SMA, Takakusagi S, Shimizu K, Takigawa I. Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts. ChemCatChem 2021. [DOI: 10.1002/cctc.202100495] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Shinya Mine
- Institute for Catalysis Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
| | - Motoshi Takao
- Institute for Catalysis Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
| | - Taichi Yamaguchi
- Institute for Catalysis Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
| | - Takashi Toyao
- Institute for Catalysis Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
- Elements Strategy Initiative for Catalysis and Batteries Kyoto University, Katsura Kyoto 615-8520 Japan
| | - Zen Maeno
- Institute for Catalysis Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
| | | | - Satoru Takakusagi
- Institute for Catalysis Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
| | - Ken‐ichi Shimizu
- Institute for Catalysis Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
- Elements Strategy Initiative for Catalysis and Batteries Kyoto University, Katsura Kyoto 615-8520 Japan
| | - Ichigaku Takigawa
- RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi Chuo-ku Tokyo 103-0027 Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD) Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
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50
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Nakanowatari S, Nguyen TN, Chikuma H, Fujiwara A, Seenivasan K, Thakur A, Takahashi L, Takahashi K, Taniike T. Extraction of Catalyst Design Heuristics from Random Catalyst Dataset and their Utilization in Catalyst Development for Oxidative Coupling of Methane. ChemCatChem 2021. [DOI: 10.1002/cctc.202100460] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Affiliation(s)
- Sunao Nakanowatari
- Graduate School of Advanced Science and Technology Japan Advanced Institute of Science and Technology 923-1292 Nomi Ishikawa Japan
| | - Thanh Nhat Nguyen
- Graduate School of Advanced Science and Technology Japan Advanced Institute of Science and Technology 923-1292 Nomi Ishikawa Japan
| | - Hiroki Chikuma
- Graduate School of Advanced Science and Technology Japan Advanced Institute of Science and Technology 923-1292 Nomi Ishikawa Japan
| | - Aya Fujiwara
- Graduate School of Advanced Science and Technology Japan Advanced Institute of Science and Technology 923-1292 Nomi Ishikawa Japan
| | - Kalaivani Seenivasan
- Graduate School of Advanced Science and Technology Japan Advanced Institute of Science and Technology 923-1292 Nomi Ishikawa Japan
| | - Ashutosh Thakur
- Graduate School of Advanced Science and Technology Japan Advanced Institute of Science and Technology 923-1292 Nomi Ishikawa Japan
- CSIR-North East Institute of Science and Technology 785006 Jorhat Assam India
| | - Lauren Takahashi
- Department of Chemistry Hokkaido University 060-0815 Sapporo Japan
| | | | - Toshiaki Taniike
- Graduate School of Advanced Science and Technology Japan Advanced Institute of Science and Technology 923-1292 Nomi Ishikawa Japan
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