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Sarma BB, Neukum D, Doronkin DE, Lakshmi Nilayam AR, Baumgarten L, Krause B, Grunwaldt JD. Understanding the role of supported Rh atoms and clusters during hydroformylation and CO hydrogenation reactions with in situ/ operando XAS and DRIFT spectroscopy. Chem Sci 2024; 15:12369-12379. [PMID: 39118611 PMCID: PMC11304778 DOI: 10.1039/d4sc02907k] [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: 05/02/2024] [Accepted: 06/29/2024] [Indexed: 08/10/2024] Open
Abstract
Supported Rh single-atoms and clusters on CeO2, MgO, and ZrO2 were investigated as catalysts for hydroformylation of ethylene to propionaldehyde and CO hydrogenation to methanol/ethanol with in situ/operando diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and X-ray absorption spectroscopy (XAS). Under hydroformylation reaction conditions, operando spectroscopic investigations unravel the presence of both single atoms and clusters and detected at first propanal and then methanol. We find that the formation of methanol is associated with CO hydrogenation over Rh clusters which was further confirmed under CO hydrogenation conditions at elevated pressure. The activity of catalysts synthesized via a precipitation (PP) method over various supports towards the hydroformylation reaction follows the order: Rh/ZrO2 > Rh/CeO2 > Rh/MgO. Comparing Rh/CeO2 catalysts synthesized via different methods, catalysts prepared by flame spray pyrolysis (FSP) showed catalytic activity for the hydroformylation reaction at lower temperatures (413 K), whereas catalysts prepared by wet impregnation (WI) showed the highest stability. These results not only provide fundamental insights into the atomistic level of industrially relevant reactions but also pave the way for a rational design of new catalysts in the future.
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Affiliation(s)
- Bidyut Bikash Sarma
- Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology (KIT) Engesserstraße 20 76131 Karlsruhe Germany
- Institute of Catalysis Research and Technology, KIT Hermann-von Helmholtz Platz 1 76344 Eggenstein-Leopoldshafen Germany
- Laboratoire de Chimie de Coordination (LCC), CNRS, Université de Toulouse, INPT, 205 route de Narbonne 31077 Toulouse Cedex 4 France
| | - Dominik Neukum
- Institute of Catalysis Research and Technology, KIT Hermann-von Helmholtz Platz 1 76344 Eggenstein-Leopoldshafen Germany
| | - Dmitry E Doronkin
- Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology (KIT) Engesserstraße 20 76131 Karlsruhe Germany
- Institute of Catalysis Research and Technology, KIT Hermann-von Helmholtz Platz 1 76344 Eggenstein-Leopoldshafen Germany
| | - Ajai Raj Lakshmi Nilayam
- Institute of Nanotechnology, KIT Hermann-von-Helmholtz Platz 1 76344 Eggenstein-Leopoldshafen Germany
| | - Lorena Baumgarten
- Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology (KIT) Engesserstraße 20 76131 Karlsruhe Germany
- Institute of Catalysis Research and Technology, KIT Hermann-von Helmholtz Platz 1 76344 Eggenstein-Leopoldshafen Germany
| | - Bärbel Krause
- Institut für Photonenforschung und Synchrotronstrahlung (IPS), KIT Hermann-von-Helmholtz Platz 1 D-76021 Karlsruhe Germany
| | - Jan-Dierk Grunwaldt
- Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology (KIT) Engesserstraße 20 76131 Karlsruhe Germany
- Institute of Catalysis Research and Technology, KIT Hermann-von Helmholtz Platz 1 76344 Eggenstein-Leopoldshafen Germany
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2
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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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3
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Cao X, Huang J, Du K, Tian Y, Hu Z, Luo Z, Wang J, Guo Y. Machine-Learning-Assisted Descriptors Identification for Indoor Formaldehyde Oxidation Catalysts. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:8372-8379. [PMID: 38691628 DOI: 10.1021/acs.est.4c01691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
The development of highly efficient catalysts for formaldehyde (HCHO) oxidation is of significant interest for the improvement of indoor air quality. Up to 400 works relating to the catalytic oxidation of HCHO have been published to date; however, their analysis for collective inference through conventional literature search is still a challenging task. A machine learning (ML) framework was presented to predict catalyst performance from experimental descriptors based on an HCHO oxidation catalysts database. MnOx, CeO2, Co3O4, TiO2, FeOx, ZrO2, Al2O3, SiO2, and carbon-based catalysts with different promoters were compiled from the literature. Notably, 20 descriptors including reaction catalyst composition, reaction conditions, and catalyst physical properties were collected for data mining (2263 data points). Furthermore, the eXtreme Gradient Boosting algorithm was employed, which successfully predicted the conversion efficiency of HCHO with an R-square value of 0.81. Shapley additive analysis suggested Pt/MnO2 and Ag/Ce-Co3O4 exhibited excellent catalytic performance of HCHO oxidation based on the analysis of the entire database. Validated by experimental tests and theoretical simulations, the key descriptor identified by ML, i.e., the first promoter, was further described as metal-support interactions. This study highlights ML as a useful tool for database establishment and the catalyst rational design strategy based on the importance of analysis between experimental descriptors and the performance of complex catalytic systems.
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Affiliation(s)
- Xinyuan Cao
- College of Chemistry, Central China Normal University, Wuhan, Hubei 430079, P. R. China
| | - Jisi Huang
- College of Chemistry, Central China Normal University, Wuhan, Hubei 430079, P. R. China
| | - Kexin Du
- College of Chemistry, Central China Normal University, Wuhan, Hubei 430079, P. R. China
| | - Yawen Tian
- College of Chemistry, Central China Normal University, Wuhan, Hubei 430079, P. R. China
| | - Zhixin Hu
- College of Chemistry, Central China Normal University, Wuhan, Hubei 430079, P. R. China
| | - Zhu Luo
- College of Chemistry, Central China Normal University, Wuhan, Hubei 430079, P. R. China
| | - Jinlong Wang
- College of Chemistry, Central China Normal University, Wuhan, Hubei 430079, P. R. China
- Wuhan Institute of Photochemistry and Technology, Wuhan, Hubei 430083, P. R. China
- Engineering Research Center of Photoenergy Utilization for Pollution Control and Carbon Reduction, Ministry of Education, Wuhan 430079, P. R. China
| | - Yanbing Guo
- College of Chemistry, Central China Normal University, Wuhan, Hubei 430079, P. R. China
- Wuhan Institute of Photochemistry and Technology, Wuhan, Hubei 430083, P. R. China
- Engineering Research Center of Photoenergy Utilization for Pollution Control and Carbon Reduction, Ministry of Education, Wuhan 430079, P. R. China
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4
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Yuan X, Suvarna M, Lim JY, Pérez-Ramírez J, Wang X, Ok YS. Active Learning-Based Guided Synthesis of Engineered Biochar for CO 2 Capture. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:6628-6636. [PMID: 38497595 PMCID: PMC11025117 DOI: 10.1021/acs.est.3c10922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/19/2024]
Abstract
Biomass waste-derived engineered biochar for CO2 capture presents a viable route for climate change mitigation and sustainable waste management. However, optimally synthesizing them for enhanced performance is time- and labor-intensive. To address these issues, we devise an active learning strategy to guide and expedite their synthesis with improved CO2 adsorption capacities. Our framework learns from experimental data and recommends optimal synthesis parameters, aiming to maximize the narrow micropore volume of engineered biochar, which exhibits a linear correlation with its CO2 adsorption capacity. We experimentally validate the active learning predictions, and these data are iteratively leveraged for subsequent model training and revalidation, thereby establishing a closed loop. Over three active learning cycles, we synthesized 16 property-specific engineered biochar samples such that the CO2 uptake nearly doubled by the final round. We demonstrate a data-driven workflow to accelerate the development of high-performance engineered biochar with enhanced CO2 uptake and broader applications as a functional material.
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Affiliation(s)
- Xiangzhou Yuan
- Ministry
of Education of Key Laboratory of Energy Thermal Conversion and Control,
School of Energy and Environment, Southeast University, Nanjing 210096, China
- Korea
Biochar Research Center, APRU Sustainable Waste Management Program
& Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Manu Suvarna
- Institute
for Chemical and Bioengineering, Department of Chemistry and Applied
Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland
| | - Juin Yau Lim
- Korea
Biochar Research Center, APRU Sustainable Waste Management Program
& Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Javier Pérez-Ramírez
- Institute
for Chemical and Bioengineering, Department of Chemistry and Applied
Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland
| | - Xiaonan Wang
- Department
of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Yong Sik Ok
- Korea
Biochar Research Center, APRU Sustainable Waste Management Program
& Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Republic of Korea
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5
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Zhou J, Jacobsson TJ, Wang Z, Huang Q, Zhang X, Zhao Y, Hou G. Data-Driven Tunnel Oxide Passivated Contact Solar Cell Performance Analysis Using Machine Learning. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2309351. [PMID: 38175915 DOI: 10.1002/adma.202309351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/21/2023] [Indexed: 01/06/2024]
Abstract
Tunnel oxide passivated contacts (TOPCon) have gained interest as a way to increase the energy conversion efficiency of silicon solar cells, and the International Technology Roadmap of Photovoltaics forecasts TOPCon to become an important technology despite a few remaining challenges. To review the recent development of TOPCon cells, this work has compiled a dataset of all device data found in current literature, which sums up to 405 devices from 131 papers. This may seem like a surprisingly small number of cells given the recent interest in the TOPCon architecture, but it illustrates a problem of data dissemination in the field. Notwithstanding the limited number of cells, there is a great diversity in cell manufacturing procedures, and this work observes a gradual increase in performance indicating that the field has not yet converged on a set of best practices. By analyzing the data using statistical methods and machine learning (ML) algorithms, this work is able to reinforces some commonly held hypotheses related to the performance differences between different device architectures. This work also identifies a few more unintuitive feature combinations that would be of interest for further experimentally studies. This work also aims to inspire improvements in data management and dissemination within the TOPCon community.
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Affiliation(s)
- Jiakai Zhou
- Institute of Photoelectronic Thin Film Devices and Technology of Nankai University, Nankai University, Tianjin, 300350, China
- Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, Tianjin, 300350, China
- Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, Tianjin, 300350, China
- State Key Laboratory of Photovoltaic Materials and Solar Cells, Tianjin, 300350, China
| | - T Jesper Jacobsson
- Institute of Photoelectronic Thin Film Devices and Technology of Nankai University, Nankai University, Tianjin, 300350, China
- Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, Tianjin, 300350, China
- Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, Tianjin, 300350, China
- State Key Laboratory of Photovoltaic Materials and Solar Cells, Tianjin, 300350, China
| | - Zhi Wang
- School of Microelectronics, Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, Tianjin University, Tianjin, 300072, China
| | - Qian Huang
- Institute of Photoelectronic Thin Film Devices and Technology of Nankai University, Nankai University, Tianjin, 300350, China
- Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, Tianjin, 300350, China
- Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, Tianjin, 300350, China
- State Key Laboratory of Photovoltaic Materials and Solar Cells, Tianjin, 300350, China
| | - Xiaodan Zhang
- Institute of Photoelectronic Thin Film Devices and Technology of Nankai University, Nankai University, Tianjin, 300350, China
- Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, Tianjin, 300350, China
- Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, Tianjin, 300350, China
- State Key Laboratory of Photovoltaic Materials and Solar Cells, Tianjin, 300350, China
| | - Ying Zhao
- Institute of Photoelectronic Thin Film Devices and Technology of Nankai University, Nankai University, Tianjin, 300350, China
- Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, Tianjin, 300350, China
- Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, Tianjin, 300350, China
- State Key Laboratory of Photovoltaic Materials and Solar Cells, Tianjin, 300350, China
| | - Guofu Hou
- Institute of Photoelectronic Thin Film Devices and Technology of Nankai University, Nankai University, Tianjin, 300350, China
- Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, Tianjin, 300350, China
- Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, Tianjin, 300350, China
- State Key Laboratory of Photovoltaic Materials and Solar Cells, Tianjin, 300350, China
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6
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Jyothirmai MV, Dantuluri R, Sinha P, Abraham BM, Singh JK. Machine-Learning-Driven High-Throughput Screening of Transition-Metal Atom Intercalated g-C 3N 4/MX 2 (M = Mo, W; X = S, Se, Te) Heterostructures for the Hydrogen Evolution Reaction. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 38436945 DOI: 10.1021/acsami.3c17389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
Rising global energy demand, accompanied by environmental concerns linked to conventional fossil fuels, necessitates a shift toward cleaner and sustainable alternatives. This study focuses on the machine-learning (ML)-driven high-throughput screening of transition-metal (TM) atom intercalated g-C3N4/MX2 (M = Mo, W; X = S, Se, Te) heterostructures to unravel the rich landscape of possibilities for enhancing the hydrogen evolution reaction (HER) activity. The stability of the heterostructures and the intercalation within the substrates are verified through adhesion and binding energies, showcasing the significant impact of chalcogenide selection on the interaction properties. Based on hydrogen adsorption Gibbs free energy (ΔGH) computed via density functional theory (DFT) calculations, several ML models were evaluated, particularly random forest regression (RFR) emerges as a robust tool in predicting HER activity with a low mean absolute error (MAE) of 0.118 eV, thereby paving the way for accelerated catalyst screening. The Shapley Additive exPlanation (SHAP) analysis elucidates pivotal descriptors that influence the HER activity, including hydrogen adsorption on the C site (HC), MX layer (HMX), S site (HS), and intercalation of TM atoms at the N site (IN). Overall, our integrated approach utilizing DFT and ML effectively identifies hydrogen adsorption on the N site (site-3) of g-C3N4 as a pivotal active site, showcasing exceptional HER activity in heterostructures intercalated with Sc and Ti, underscoring their potential for advancing catalytic performance.
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Affiliation(s)
- M V Jyothirmai
- Department of Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - Roshini Dantuluri
- Department of Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - Priyanka Sinha
- Department of Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - B Moses Abraham
- Departament de Ciència de Materials i Química Física, Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, c/Martí i Franquès 1-11, Barcelona 08028, Spain
| | - Jayant K Singh
- Department of Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
- Prescience Insilico Private Limited, Bangalore 560049, India
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7
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Roy D, Charan Mandal S, Das A, Pathak B. Unravelling CO 2 Reduction Reaction Intermediates on High Entropy Alloy Catalysts: An Interpretable Machine Learning Approach to Establish Scaling Relations. Chemistry 2024; 30:e202302679. [PMID: 37966848 DOI: 10.1002/chem.202302679] [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: 08/16/2023] [Revised: 10/30/2023] [Accepted: 11/15/2023] [Indexed: 11/16/2023]
Abstract
Establishment of a scaling relation among the reaction intermediates is highly important but very much challenging on complex surfaces, such as surfaces of high entropy alloys (HEAs). Herein, we designed an interpretable machine learning (ML) approach to establish a scaling relation among CO2 reduction reaction (CO2 RR) intermediates adsorbed at the same adsorption site. Local Interpretable Model-Agnostic Explanations (LIME), Accumulated Local Effects (ALE), and Permutation Feature Importance (PFI) are used for the global and local interpretation of the utilized black box models. These methods were successfully applied through an iterative way and validated on CuCoNiZnMg and CuCoNiZnSnbased HEAs data. Finally, we successfully predicted adsorption energies of *H2 CO (MAE: 0.24 eV) and *H3 CO (MAE: 0.23 eV) by using the *HCO training data. Similarly, adsorption energy of *O (MAE: 0.32 eV) is also predicted from *H training data. We believe that our proposed method can shift the paradigm of state-of-the-art ML in catalysis towards better interpretability.
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Affiliation(s)
- Diptendu Roy
- Department of Chemistry, Indian Institute of Technology Indore, Indore, 453552, India
| | - Shyama Charan Mandal
- Department of Chemistry, Indian Institute of Technology Indore, Indore, 453552, India
- Present address: SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University, 443 Via Ortega, Stanford, CA 94305, USA
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025, USA
| | - Amitabha Das
- Department of Chemistry, Indian Institute of Technology Indore, Indore, 453552, India
| | - Biswarup Pathak
- Department of Chemistry, Indian Institute of Technology Indore, Indore, 453552, India
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8
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Wang G, Mine S, Chen D, Jing Y, Ting KW, Yamaguchi T, Takao M, Maeno Z, Takigawa I, Matsushita K, Shimizu KI, Toyao T. Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach. Nat Commun 2023; 14:5861. [PMID: 37735169 PMCID: PMC10514199 DOI: 10.1038/s41467-023-41341-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 08/28/2023] [Indexed: 09/23/2023] Open
Abstract
Designing novel catalysts is key to solving many energy and environmental challenges. Despite the promise that data science approaches, including machine learning (ML), can accelerate the development of catalysts, truly novel catalysts have rarely been discovered through ML approaches because of one of its most common limitations and criticisms-the assumed inability to extrapolate and identify extraordinary materials. Herein, we demonstrate an extrapolative ML approach to develop new multi-elemental reverse water-gas shift catalysts. Using 45 catalysts as the initial data points and performing 44 cycles of the closed loop discovery system (ML prediction + experiment), we experimentally tested a total of 300 catalysts and identified more than 100 catalysts with superior activity compared to those of the previously reported high-performance catalysts. The composition of the optimal catalyst discovered was Pt(3)/Rb(1)-Ba(1)-Mo(0.6)-Nb(0.2)/TiO2. Notably, niobium (Nb) was not included in the original dataset, and the catalyst composition identified was not predictable even by human experts.
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Affiliation(s)
- Gang Wang
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo, 001-0021, Japan
| | - Shinya Mine
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo, 001-0021, Japan
| | - Duotian Chen
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo, 001-0021, Japan
| | - Yuan Jing
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo, 001-0021, Japan
| | - Kah Wei Ting
- 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
| | - Motoshi Takao
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo, 001-0021, Japan
| | - Zen Maeno
- School of Advanced Engineering, Kogakuin University, 2665-1, Nakano-cho, Hachioji, 192-0015, 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.
- Institute for Liberal Arts and Sciences, Kyoto University, 69-302, Yoshida-Konoe-cho, Sakyo-ku, Kyoto, 606-8315, Japan.
| | - Koichi Matsushita
- Central Technical Research Laboratory, ENEOS Corporation, 8, Chidori-cho, Naka-ku, Yokohama, 231-0815, Japan
| | - Ken-Ichi Shimizu
- 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.
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9
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Zhou W, Docherty SR, Ehinger C, Zhou X, Copéret C. The promotional role of Mn in CO 2 hydrogenation over Rh-based catalysts from a surface organometallic chemistry approach. Chem Sci 2023; 14:5379-5385. [PMID: 37234901 PMCID: PMC10207883 DOI: 10.1039/d3sc01163a] [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: 03/03/2023] [Accepted: 04/14/2023] [Indexed: 05/28/2023] Open
Abstract
Rh-based catalysts modified by transition metals have been intensively studied for CO2 hydrogenation due to their high activity. However, understanding the role of promoters at the molecular level remains challenging due to the ill-defined structure of heterogeneous catalysts. Here, we constructed well-defined RhMn@SiO2 and Rh@SiO2 model catalysts via surface organometallic chemistry combined with thermolytic molecular precursor (SOMC/TMP) approach to rationalize the promotional effect of Mn in CO2 hydrogenation. We show that the addition of Mn shifts the products from almost pure CH4 to a mixture of methane and oxygenates (CO, CH3OH, and CH3CH2OH) upon going from Rh@SiO2 to RhMn@SiO2. In situ X-ray absorption spectroscopy (XAS) confirms that the MnII is atomically dispersed in the vicinity of metallic Rh nanoparticles and enables to induce the oxidation of Rh to form the Mn-O-Rh interface under reaction conditions. The formed interface is proposed to be key to maintaining Rh+ sites, which is related to suppressing the methanation reaction and stabilizing the formate species as evidenced by in situ DRIFTS to promote the formation of CO and alcohols.
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Affiliation(s)
- Wei Zhou
- Department of Chemistry and Applied Bioscience, ETH Zürich Vladimir Prelog Weg2 CH-8093 Zürich Switzerland
| | - Scott R Docherty
- Department of Chemistry and Applied Bioscience, ETH Zürich Vladimir Prelog Weg2 CH-8093 Zürich Switzerland
| | - Christian Ehinger
- Department of Chemistry and Applied Bioscience, ETH Zürich Vladimir Prelog Weg2 CH-8093 Zürich Switzerland
| | - Xiaoyu Zhou
- Department of Chemistry and Applied Bioscience, ETH Zürich Vladimir Prelog Weg2 CH-8093 Zürich Switzerland
| | - Christophe Copéret
- Department of Chemistry and Applied Bioscience, ETH Zürich Vladimir Prelog Weg2 CH-8093 Zürich Switzerland
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10
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Li J, Pan L, Li Z, Wang Y. Unveiling the migration of Cr and Cd to biochar from pyrolysis of manure and sludge using machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 885:163895. [PMID: 37146809 DOI: 10.1016/j.scitotenv.2023.163895] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/21/2023] [Accepted: 04/28/2023] [Indexed: 05/07/2023]
Abstract
Heavy metal (HM) in biochar derived from pyrolysis of sludge or manure is the main issue for its large-scale application in soils for carbon sequestration. However, there is a paucity of efficient approaches to predict and comprehend the HM migration during pyrolysis for preparing low HM-contained biochar. Herein, the data on the feedstock information (FI), additive, total concentration of feedstock (FTC) of HM Cr and Cd, and pyrolysis condition, were extracted from the literature, to predict total concentration (TC) and retention rate (RR) of Cr and Cd in sludge/manure biochar using ML for mapping their migration during pyrolysis. Two datasets for Cr and Cd were compiled with 388 and 292 data points from 48 and 37 peer-review papers. The results indicated that the TC and RR of Cr and Cd could be predicted by the Random Forest model with test R2 of 0.74-0.98. Their TC and RR in biochar were dominated by the FTC and FI, respectively; while pyrolysis temperature was the most important to Cd RR. Moreover, potassium-based inorganic additives decreased the TC and RR of Cr while increased those of Cd. The predictive models and insights provided by this work could aid the understanding of HM migration during manure and sludge pyrolysis and guide the preparation of low HM-contained biochar.
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Affiliation(s)
- Jie Li
- CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; CAS Engineering Laboratory for Recycling Technology of Municipal Solid Wastes, Xiamen 361021, China.
| | - Lanjia Pan
- CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; CAS Engineering Laboratory for Recycling Technology of Municipal Solid Wastes, Xiamen 361021, China
| | - Zhiwei Li
- CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; CAS Engineering Laboratory for Recycling Technology of Municipal Solid Wastes, Xiamen 361021, China
| | - Yin Wang
- CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; CAS Engineering Laboratory for Recycling Technology of Municipal Solid Wastes, Xiamen 361021, China; Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, China.
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Rossi K. What do we talk about, when we talk about single-crystal termination-dependent selectivity of Cu electrocatalysts for CO 2 reduction? A data-driven retrospective. Phys Chem Chem Phys 2023; 25:6867-6876. [PMID: 36799456 DOI: 10.1039/d2cp04576a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
We mine from the literature experimental data on the CO2 electrochemical reduction selectivity of Cu single crystal surfaces. We then probe the accuracy of a machine learning model trained to predict faradaic efficiencies for 11 CO2 reduction reaction products, as a function of the applied voltage at which the reaction takes place, and the relative amounts of non equivalent surface sites, distinguished according to their nominal coordination. A satisfactory model accuracy is found only when discriminating data according to their provenance. On one hand, this result points at a qualitative agreement across reported experimental CO2 reduction reactions trends for single-crystal surfaces with well-defined terminations. On the other, this finding hints at the presence of differences in nominally identical catalysts and/or CO2 reduction reaction measurements, which result in quantitative disagreement between experiments.
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Affiliation(s)
- Kevin Rossi
- Institut des sciences et ingénierie chimiques, École Polytechnique Fédérale de Lausanne, 1950 Sion, Switzerland.
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