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Li W, Bu Y, Ge X, Li F, Han GF, Baek JB. Recent Advances in Iridium-based Electrocatalysts for Acidic Electrolyte Oxidation. CHEMSUSCHEM 2024; 17:e202400295. [PMID: 38362788 DOI: 10.1002/cssc.202400295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 02/14/2024] [Accepted: 02/16/2024] [Indexed: 02/17/2024]
Abstract
Ongoing research to develop advanced electrocatalysts for the oxygen evolution reaction (OER) is needed to address demand for efficient energy conversion and carbon-free energy sources. In the OER process, acidic electrolytes have higher proton concentration and faster response than alkaline ones, but their harsh strongly acidic environment requires catalysts with greater corrosion and oxidation resistance. At present, iridium oxide (IrO2) with its strong stability and excellent catalytic performance is the catalyst of choice for the anode side of commercial PEM electrolysis cells. However, the scarcity and high cost of iridium (Ir) and the unsatisfactory activity of IrO2 hinder industrial scale application and the sustainable development of acidic OER catalytic technology. This highlights the importance of further research on acidic Ir-based OER catalysts. In this review, recent advances in Ir-based acidic OER electrocatalysts are summarized, including fundamental understanding of the acidic OER mechanism, recent insights into the stability of acidic OER catalysts, highly efficient Ir-based electrocatalysts, and common strategies for optimizing Ir-based catalysts. The future challenges and prospects of developing highly effective Ir-based catalysts are also discussed.
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Affiliation(s)
- Wanqing Li
- UNIST-NUIST Environment and Energy Jointed Lab, UNNU), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Technology, Nanjing University of Information Science and Technology (NUIST), Nanjing, 210044, P. R. China
| | - Yunfei Bu
- UNIST-NUIST Environment and Energy Jointed Lab, UNNU), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Technology, Nanjing University of Information Science and Technology (NUIST), Nanjing, 210044, P. R. China
| | - Xinlei Ge
- UNIST-NUIST Environment and Energy Jointed Lab, UNNU), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Technology, Nanjing University of Information Science and Technology (NUIST), Nanjing, 210044, P. R. China
| | - Feng Li
- Laboratory of Advanced Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, 220 Handan, Shanghai, 200433, P. R. China
| | - Gao-Feng Han
- Key Laboratory of Automobile Materials, Ministry of Education, and School of Materials Science and Engineering, Jilin University, Changchun, 130022, P. R. China
| | - Jong-Beom Baek
- School of Energy and Chemical Engineering/Center for Dimension Controllable Organic Frameworks, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST, Ulsan, 44919, South Korea
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Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks. Nat Commun 2023; 14:792. [PMID: 36774355 PMCID: PMC9922329 DOI: 10.1038/s41467-023-36322-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 01/24/2023] [Indexed: 02/13/2023] Open
Abstract
The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt3Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt3Ir, and Pt3Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond the d-band center metric of binding sites.
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Jiang X, Wang Y, Jia B, Qu X, Qin M. Using Machine Learning to Predict Oxygen Evolution Activity for Transition Metal Hydroxide Electrocatalysts. ACS APPLIED MATERIALS & INTERFACES 2022; 14:41141-41148. [PMID: 36044226 DOI: 10.1021/acsami.2c13435] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Electrocatalytic water splitting is an attractive way to generate hydrogen and oxygen for obtaining clean energy. Oxygen evolution reaction (OER), as one of the half reactions of oxygen evolution, is kinetically unfavorable involving the transfer of four electrons. Hydroxides are promising candidates for efficient OER electrocatalysts toward water splitting because of their high intrinsic activity and active surface area. However, quantitative prediction of hydroxide electrocatalytic performances from high-dimensional component spaces remains a challenge, severely hindering the performance-oriented precise composition and process design. Herein, we introduce a machine learning-based OER activity prediction method for hydroxide catalysts under extensive doping space for the first time. The relationship among composition, morphology, phase, pH value of the electrolyte, type of the working electrode, and overpotential was successfully fitted by the random forest algorithm. The model shows a good precision on the forecast of new experiments with a mean relative error of 4.74%. Furthermore, a new high-activity hydroxide catalyst Ni0.77Fe0.13La0.1 was rationally designed and experimentally prepared, showing an ultra-low OP of 226 mV for a current density of 10 mA cm-2. This work provides an effective and novel way for hydroxide electrocatalyst prediction, which can further enhance the electrocatalyst design toward high catalytic performance.
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Affiliation(s)
- Xue Jiang
- Beijing Advanced Innovation Center for Materials Genome Engineering, Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Yong Wang
- Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Baorui Jia
- Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Xuanhui Qu
- Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Mingli Qin
- Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China
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Zhang Y, Peck TC, Reddy GK, Banerjee D, Jia H, Roberts CA, Ling C. Descriptor-Free Design of Multicomponent Catalysts. ACS Catal 2022. [DOI: 10.1021/acscatal.2c02807] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Ying Zhang
- Toyota Research Institute of North America, Ann Arbor, Michigan 48105, United States
| | - Torin C. Peck
- Toyota Research Institute of North America, Ann Arbor, Michigan 48105, United States
| | - Gunugunuri K. Reddy
- Toyota Research Institute of North America, Ann Arbor, Michigan 48105, United States
| | - Debasish Banerjee
- Toyota Research Institute of North America, Ann Arbor, Michigan 48105, United States
| | - Hongfei Jia
- Toyota Research Institute of North America, Ann Arbor, Michigan 48105, United States
| | - Charles A. Roberts
- Toyota Research Institute of North America, Ann Arbor, Michigan 48105, United States
| | - Chen Ling
- Toyota Research Institute of North America, Ann Arbor, Michigan 48105, United States
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