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Mok DH, Back S, Siahrostami S. Validating ΔΔG Selectivity Descriptor for Electrosynthesis of H 2O 2 from Oxygen Reduction Reaction. Angew Chem Int Ed Engl 2024:e202404677. [PMID: 38513003 DOI: 10.1002/anie.202404677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 03/19/2024] [Accepted: 03/19/2024] [Indexed: 03/23/2024]
Abstract
Understanding selectivity trends is a crucial hurdle in the developing innovative catalysts for generating hydrogen peroxide through the two-electron oxygen reduction reaction (2e-ORR). The identification of selectivity patterns has been made more accessible through the introduction of a newly developed selectivity descriptor derived from thermodynamics, denoted as ΔΔG introduced in Chem Catal. 2023, 3(3), 100568. To validate the suitability of this parameter as a descriptor for 2e-ORR selectivity, we utilize an extensive library of 155 binary alloys. We validate that ΔΔG reliably depicts the selectivity trends in binary alloys reported for their high activity in the 2e-ORR. This analysis also enables the identification of nine selective 2e-ORR catalysts underscoring the efficacy of ΔΔG as 2e-ORR selectivity descriptor. This work highlights the significance of concurrently considering both selectivity and activity trends. This holistic approach is crucial for obtaining a comprehensive understanding in the identification of high-performance catalyst materials for optimal efficiency in various applications.
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Affiliation(s)
- Dong Hyeon Mok
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul, 04107, Republic of Korea
| | - Seoin Back
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul, 04107, Republic of Korea
| | - Samira Siahrostami
- Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, B.C. V5 A 1S6, Canada
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Mok DH, Li H, Zhang G, Lee C, Jiang K, Back S. Data-driven discovery of electrocatalysts for CO 2 reduction using active motifs-based machine learning. Nat Commun 2023; 14:7303. [PMID: 37952012 PMCID: PMC10640609 DOI: 10.1038/s41467-023-43118-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/01/2023] [Indexed: 11/14/2023] Open
Abstract
The electrochemical carbon dioxide reduction reaction (CO2RR) is an attractive approach for mitigating CO2 emissions and generating value-added products. Consequently, discovery of promising CO2RR catalysts has become a crucial task, and machine learning (ML) has been utilized to accelerate catalyst discovery. However, current ML approaches are limited to exploring narrow chemical spaces and provide only fragmentary catalytic activity, even though CO2RR produces various chemicals. Here, by merging pre-developed ML model and a CO2RR selectivity map, we establish high-throughput virtual screening strategy to suggest active and selective catalysts for CO2RR without being limited to a database. Further, this strategy can provide guidance on stoichiometry and morphology of the catalyst to researchers. We predict the activity and selectivity of 465 metallic catalysts toward four expected reaction products. During this process, we discover previously unreported and promising behavior of Cu-Ga and Cu-Pd alloys. These findings are then validated through experimental methods.
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Affiliation(s)
- Dong Hyeon Mok
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul, 04107, Republic of Korea
| | - Hong Li
- Interdisciplinary Research Center, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Guiru Zhang
- Interdisciplinary Research Center, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Chaehyeon Lee
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul, 04107, Republic of Korea
| | - Kun Jiang
- Interdisciplinary Research Center, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Seoin Back
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul, 04107, Republic of Korea.
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Kim J, Mok DH, Kim H, Back S. Accelerating the Search for New Solid Electrolytes: Exploring Vast Chemical Space with Machine Learning-Enabled Computational Calculations. ACS Appl Mater Interfaces 2023. [PMID: 37924286 DOI: 10.1021/acsami.3c10798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2023]
Abstract
Discovering new solid electrolytes (SEs) is essential to achieving higher safety and better energy density for all-solid-state lithium batteries. In this work, we report machine learning (ML)-assisted high-throughput virtual screening (HTVS) results to identify new SE materials. This approach expands the chemical space to explore by substituting elements of prototype structures and accelerates an evaluation of properties by applying various ML models. The screening results in a few candidate materials, which are validated by density functional theory calculations and ab initio molecular dynamics simulations. The shortlisted oxysulfide materials satisfy key properties to be successful SEs. The advanced screening method presented in this work will accelerate the discovery of energy materials for related applications.
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Affiliation(s)
- Jongseung Kim
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul 04107, Republic of Korea
| | - Dong Hyeon Mok
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul 04107, Republic of Korea
| | - Heejin Kim
- Division of Analytical Science, Korea Basic Science Institute (KBSI), Yuseong-gu, Daejeon 34133, Republic of Korea
| | - Seoin Back
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul 04107, Republic of Korea
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Jo J, Yoo JM, Mok DH, Jang HY, Kim J, Ko W, Yeom K, Bootharaju MS, Back S, Sung YE, Hyeon T. Facet-Defined Strain-Free Spinel Oxide for Oxygen Reduction. Nano Lett 2022; 22:3636-3644. [PMID: 35357196 DOI: 10.1021/acs.nanolett.2c00238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Exposing facet and surface strain are critical factors affecting catalytic performance but unraveling the composition-dependent activity on specific facets under strain-controlled environment is still challenging due to the synthetic difficulties. Herein, we achieved a (001) facet-defined Co-Mn spinel oxide surface with different surface compositions using epitaxial growth on Co3O4 nanocube template. We adopted composition gradient synthesis to relieve the strain layer by layer, minimizing the surface strain effect on catalytic activity. In this system, experimental and calculational analyses of model oxygen reduction reaction (ORR) activity reveals a volcano-like trend with Mn/Co ratios because of an adequate charge transfer from octahedral-Mn to neighboring Co. Co0.5Mn0.5 as an optimized Mn/Co ratio exhibits both outstanding ORR activity (0.894 V vs RHE in 1 M KOH) and stability (2% activity loss against chronoamperometry). By controlling facet and strain, this study provides a well-defined platform for investigating composition-structure-activity relationships in electrocatalytic processes.
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Affiliation(s)
- Jinwoung Jo
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea
- School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Ji Mun Yoo
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea
- School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Dong Hyeon Mok
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul 04107, Republic of Korea
| | - Ho Yeon Jang
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul 04107, Republic of Korea
| | - Jiheon Kim
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea
- School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Wonjae Ko
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea
- School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Kyungbeen Yeom
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea
- School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Megalamane S Bootharaju
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea
- School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Seoin Back
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul 04107, Republic of Korea
| | - Yung-Eun Sung
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea
- School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Taeghwan Hyeon
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea
- School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
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Yuan Q, Zhao J, Mok DH, Zheng Z, Ye Y, Liang C, Zhou L, Back S, Jiang K. Electrochemical Hydrogen Peroxide Synthesis from Selective Oxygen Reduction over Metal Selenide Catalysts. Nano Lett 2022; 22:1257-1264. [PMID: 34965148 DOI: 10.1021/acs.nanolett.1c04420] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Se-based nanoalloys as an emerging class of metal chalcogenide with tunable crystalline structure, component distribution, and electronic structure have attracted considerable interest in renewable energy conversion and utilization. In this Letter, we report a series of nanosized M-Se catalysts (M = Cu, Ni, Co) as prepared from laser ablation method and screen their electrocatalytic performance for onsite H2O2 generation from selective oxygen reduction reaction (ORR) in alkaline media. A flexible control on 2e-/4e- ORR pathway has been achieved by engineering the alloying component. Moreover, through a feedback loop between theory and experiment an optimized scaling relationship between oxygenated ORR intermediates has been discovered on cubic Cu7.2Se4 nanocrystals, that is, the ensemble effect of isolated Cu component destabilizes O* binding while the ligand effect of Se to Cu fine-tunes the binding strength of OOH*, leading to a superb H2O2 selectivity above 90% over a wide potential window even after 1400 potential cycles.
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Affiliation(s)
- Qinglin Yuan
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jiajun Zhao
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dong Hyeon Mok
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul 04107, Republic of Korea
| | - Zemin Zheng
- School of Chemistry and Chemical Engineering, Frontiers Science Centre for Transformative Molecules, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yixing Ye
- Key Laboratory of Materials Physics and Anhui Key Laboratory of Nanomaterials and Nanotechnology, Institute of Solid-State Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Changhao Liang
- Key Laboratory of Materials Physics and Anhui Key Laboratory of Nanomaterials and Nanotechnology, Institute of Solid-State Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Lin Zhou
- School of Chemistry and Chemical Engineering, Frontiers Science Centre for Transformative Molecules, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Seoin Back
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul 04107, Republic of Korea
| | - Kun Jiang
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Abstract
To discover new catalysts using density functional theory (DFT) calculations, binding energies of reaction intermediates are considered as descriptors to predict catalytic activities. Recently, machine learning methods have been developed to reduce the number of computationally intensive DFT calculations for a high-throughput screening. These methods require several steps such as bulk structure optimization, surface structure modeling, and active site identification, which could be time-consuming as the number of new candidate materials increases. To bypass these processes, in this work, we report an atomic structure-free representation of active motifs to predict binding energies. We identify binding site atoms and their nearest neighboring atoms positioned in the same layer and the sublayer, and their atomic properties are collected to construct fingerprints. Our method enabled a quicker training (200-400 s using CPU) compared to the previous deep-learning models and predicted CO and H binding energies with mean absolute errors (MAEs) of 0.120 and 0.105 eV, respectively. Our method is also capable of creating all possible active motifs without any DFT calculations and predicting their binding energies using the trained model. The predicted binding energy distributions can suggest promising candidates to accelerate catalyst discovery.
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Affiliation(s)
- Dong Hyeon Mok
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul 04107, Republic of Korea
| | - Seoin Back
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul 04107, Republic of Korea
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