Prediction of Oxygen Evolution Activity for FeCoMn Oxide Catalysts via Machine Learning
Abstract
:1. Introduction
2. Results and Discussion
2.1. Data Collection
2.2. Feature Construction
2.3. Prediction Model
2.4. Experiment Validation
3. Materials and Methods
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Zhang, X.; Zhang, L.; Zhu, Y.; Li, Z.; Wang, Y.; Wågberg, T.; Hu, G. Increasing Electrocatalytic Oxygen Evolution Efficiency through Cobalt-Induced Intrastructural Enhancement and Electronic Structure Modulation. ChemSusChem 2021, 14, 467–478. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.; Li, Q.; Hu, J.; Zhou, C.; Yang, S.; Huang, X. Coordination confinement pyrolysis to hollow sea urchin shaped composite with embedded ultrasmall Co/Ni alloy for overall water splitting. Int. J. Hydrogen Energy 2022, 47, 3699–3708. [Google Scholar] [CrossRef]
- Dai, Z.; Du, X.; Zhang, X. Controlled synthesis of NiCo2O4@ Ni-MOF on Ni foam as efficient electrocatalyst for urea oxidation reaction and oxygen evolution reaction. Int. J. Hydrogen Energy 2022, 47, 17252–17262. [Google Scholar] [CrossRef]
- Tran-Phu, T.; Daiyan, R.; Leverett, J.; Fusco, Z.; Tadich, A.; Di Bernardo, I.; Kiy, A.; Truong, T.N.; Zhang, Q.; Chen, H. Understanding the activity and stability of flame-made Co3O4 spinels: A route towards the scalable production of highly performing OER electrocatalysts. Chem. Eng. J. 2022, 429, 132180. [Google Scholar] [CrossRef]
- Niu, S.; Jiang, W.J.; Wei, Z.; Tang, T.; Ma, J.; Hu, J.S.; Wan, L.J. Se-doping activates FeOOH for cost-effective and efficient electrochemical water oxidation. J. Am. Chem. Soc. 2019, 141, 7005–7013. [Google Scholar] [CrossRef] [PubMed]
- Song, J.; Wei, C.; Huang, Z.F.; Liu, C.; Zeng, L.; Wang, X.; Xu, Z.J. A review on fundamentals for designing oxygen evolution electrocatalysts. Chem. Soc. Rev. 2020, 49, 2196–2214. [Google Scholar] [CrossRef] [PubMed]
- Kim, M.; Kim, J.; Qin, L.; Mathew, S.; Han, Y.; Li, O.L. Gas-Liquid Interfacial Plasma engineering under dilute nitric acid to improve hydrophilicity and OER performance of nickel foam. Prog. Nat. Sci. Mater. Int. 2022, 32, 608–616. [Google Scholar] [CrossRef]
- Zhao, D.; Zhuang, Z.; Cao, X.; Zhang, C.; Peng, Q.; Chen, C.; Li, Y. Atomic site electrocatalysts for water splitting, oxygen reduction and selective oxidation. Chem. Soc. Rev. 2020, 49, 2215–2264. [Google Scholar] [CrossRef] [PubMed]
- Lee, Y.; Suntivich, J.; May, K.J.; Perry, E.E.; Shao-Horn, Y. Synthesis and activities of rutile IrO2 and RuO2 nanoparticles for oxygen evolution in acid and alkaline solutions. J. Phys. Chem. Lett. 2012, 3, 399–404. [Google Scholar] [CrossRef]
- Wang, C.; Jin, L.; Shang, H.; Xu, H.; Shiraishi, Y.; Du, Y. Advances in engineering RuO2 electrocatalysts towards oxygen evolution reaction. Chin. Chem. Lett. 2021, 32, 2108–2116. [Google Scholar] [CrossRef]
- Yoon, H.; Ju, B.; Kim, D.W. Perspectives on the development of highly active, stable, and cost-effective OER electrocatalysts in acid. Battery Energy 2023, 2, 20230017. [Google Scholar] [CrossRef]
- Osgood, H.; Devaguptapu, S.V.; Xu, H.; Cho, J.; Wu, G. Transition metal (Fe, Co, Ni, and Mn) oxides for oxygen reduction and evolution bifunctional catalysts in alkaline media. Nano Today 2016, 11, 601–625. [Google Scholar] [CrossRef]
- You, N.; Cao, S.; Huang, M.; Fan, X.; Shi, K.; Huang, H.; Chen, Z.; Yang, Z.; Zhang, W. Constructing P-CoMoO4@ NiCoP heterostructure nanoarrays on Ni foam as efficient bifunctional electrocatalysts for overall water splitting. Nano Mater. Sci. 2023, 5, 278–286. [Google Scholar] [CrossRef]
- Mane, R.S.; Mane, S.; Somkuwar, V.; Thombre, N.V.; Patwardhan, A.V.; Jha, N. A novel hierarchically hybrid structure of MXene and bi-ligand ZIF-67 based trifunctional electrocatalyst for zinc-air battery and water splitting. Battery Energy 2023, 2, 20230019. [Google Scholar] [CrossRef]
- Zhao, L.; Jiang, J.; Xiao, S.; Li, Z.; Wang, J.; Wei, X.; Kong, Q.; Chen, J.S.; Wu, R. PtZn nanoparticles supported on porous nitrogen-doped carbon nanofibers as highly stable electrocatalysts for oxygen reduction reaction. Nano Mater. Sci. 2023, 5, 329–334. [Google Scholar] [CrossRef]
- Zhang, Y.; Teng, X.; Ma, Z.; Wang, R.; Lau, W.M.; Shan, A. Cu2O-templated fabrication of Ni (OH)2·0.75 H2O hollow tubes for electrocatalytic oxygen evolution reaction. Prog. Nat. Sci. Mater. Int. 2022, 32, 554–560. [Google Scholar] [CrossRef]
- Ciambriello, L.; Cavaliere, E.; Vassalini, I.; Alessandri, I.; Ferroni, M.; Leoncino, L.; Brescia, R.; Gavioli, L. Role of Electrode Thickness in NiFe Nanogranular Films for Oxygen Evolution Reaction. J. Phys. Chem. C 2022, 126, 21759–21770. [Google Scholar] [CrossRef]
- Ciambriello, L.; Alessandri, I.; Ferroni, M.; Gavioli, L.; Vassalini, I. Unexpected Resilience of NiFe Catalysts for the Alkaline Oxygen Evolution Reaction. ACS Appl. Energy Mater. 2024, 7, 3462–3472. [Google Scholar] [CrossRef]
- Ciambriello, L.; Alessandri, I.; Gavioli, L.; Vassalini, I. NiFe Catalysts for Oxygen Evolution Reaction: Is There an Optimal Thickness for Generating a Dynamically Stable Active Interface? ChemCatChem 2024, e202400286. [Google Scholar] [CrossRef]
- Fan, M.; Cui, J.; Wu, J.; Vajtai, R.; Sun, D.; Ajayan, P.M. Improving the catalytic activity of carbon-supported single atom catalysts by polynary metal or heteroatom doping. Small 2020, 16, 1906782. [Google Scholar] [CrossRef]
- Cheng, R.; Min, Y.; Li, H.; Fu, C. Electronic structure regulation in the design of low-cost efficient electrocatalysts: From theory to applications. Nano Energy 2023, 115, 108718. [Google Scholar] [CrossRef]
- Yan, Y.; Wang, P.; Lin, J.; Cao, J.; Qi, J. Modification strategies on transition metal-based electrocatalysts for efficient water splitting. J. Energy Chem. 2021, 58, 446–462. [Google Scholar] [CrossRef]
- Zhang, A.; Liang, Y.; Zhang, H.; Geng, Z.; Zeng, J. Doping regulation in transition metal compounds for electrocatalysis. Chem. Soc. Rev. 2021, 50, 9817–9844. [Google Scholar] [CrossRef] [PubMed]
- Huang, J.; Jiang, Y.; An, T.; Cao, M. Increasing the active sites and intrinsic activity of transition metal chalcogenide electrocatalysts for enhanced water splitting. J. Mater. Chem. A 2020, 8, 25465–25498. [Google Scholar] [CrossRef]
- Strmcnik, D.; Uchimura, M.; Wang, C.; Subbaraman, R.; Danilovic, N.; Van Der Vliet, D.; Paulikas, A.P.; Stamenkovic, V.R.; Markovic, N.M. Improving the hydrogen oxidation reaction rate by promotion of hydroxyl adsorption. Nat. Chem. 2013, 5, 300–306. [Google Scholar] [CrossRef] [PubMed]
- Du, H.; Pu, W.; Yang, C. Morphology control of Co3O4 with nickel incorporation for highly efficient oxygen evolution reaction. Appl. Surf. Sci. 2021, 541, 148221. [Google Scholar] [CrossRef]
- Yan, K.L.; Shang, X.; Li, Z.; Dong, B.; Li, X.; Gao, W.K.; Chi, J.Q.; Chai, Y.M.; Liu, C.G. Ternary mixed metal Fe-doped NiCo2O4 nanowires as efficient electrocatalysts for oxygen evolution reaction. Appl. Surf. Sci. 2017, 416, 371–378. [Google Scholar] [CrossRef]
- Huang, Y.; Han, J.; Wang, H.; Liu, L.; Liang, H. Multiple metallic dopants in nickel nanoparticles for electrocatalytic oxygen evolution. Prog. Nat. Sci. Mater. Int. 2023, 33, 67–73. [Google Scholar] [CrossRef]
- Kou, C.; Han, J.; Wang, H.; Han, M.; Liang, H. Unveiling the role of Zn dopants in NiFe phosphide nanosheet for oxygen evolution reaction. Prog. Nat. Sci. Mater. Int. 2023, 33, 74–82. [Google Scholar] [CrossRef]
- Xiao, Z.; Zhou, W.; Yang, B.; Liao, C.; Kang, Q.; Chen, G.; Liu, M.; Liu, X.; Ma, R.; Zhang, N. Tuned d-band states over lanthanum doped nickel oxide for efficient oxygen evolution reaction. Nano Mater. Sci. 2023, 5, 228–236. [Google Scholar] [CrossRef]
- Raccuglia, P.; Elbert, K.C.; Adler, P.D.; Falk, C.; Wenny, M.B.; Mollo, A.; Zeller, M.; Friedler, S.A.; Schrier, J.; Norquist, A.J. Machine-learning-assisted materials discovery using failed experiments. Nature 2016, 533, 73–76. [Google Scholar] [CrossRef] [PubMed]
- Xue, D.; Balachandran, P.V.; Hogden, J.; Theiler, J.; Xue, D.; Lookman, T. Accelerated search for materials with targeted properties by adaptive design. Nat. Commun. 2016, 7, 11241. [Google Scholar] [CrossRef] [PubMed]
- Xue, D.; Xue, D.; Yuan, R.; Zhou, Y.; Balachandran, P.V.; Ding, X.; Sun, J.; Lookman, T. An informatics approach to transformation temperatures of NiTi-based shape memory alloys. Acta Mater. 2017, 125, 532–541. [Google Scholar] [CrossRef]
- Zhang, Y.; Wen, C.; Wang, C.; Antonov, S.; Xue, D.; Bai, Y.; Su, Y. Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models. Acta Mater. 2020, 185, 528–539. [Google Scholar] [CrossRef]
- Wu, L.; Guo, T.; Li, T. Rational design of transition metal single-atom electrocatalysts: A simulation-based, machine learning-accelerated study. J. Mater. Chem. A 2020, 8, 19290–19299. [Google Scholar] [CrossRef]
- Wen, C.; Wang, C.; Zhang, Y.; Antonov, S.; Xue, D.; Lookman, T.; Su, Y. Modeling solid solution strengthening in high entropy alloys using machine learning. Acta Mater. 2021, 212, 116917. [Google Scholar] [CrossRef]
- Li, S.; Zhang, Y.; Hu, Y.; Wang, B.; Sun, S.; Yang, X.; He, H. Predicting metal-organic frameworks as catalysts to fix carbon dioxide to cyclic carbonate by machine learning. J. Mater. 2021, 7, 1029–1038. [Google Scholar] [CrossRef]
- Wang, X.; Xiao, B.; Li, Y.; Tang, Y.; Liu, F.; Chen, J.; Liu, Y. First-principles based machine learning study of oxygen evolution reactions of perovskite oxides using a surface center-environment feature model. Appl. Surf. Sci. 2020, 531, 147323. [Google Scholar] [CrossRef]
- Chen, A.; Zhang, X.; Zhou, Z. Machine learning: Accelerating materials development for energy storage and conversion. InfoMat 2020, 2, 553–576. [Google Scholar] [CrossRef]
- Jiang, X.; Wang, Y.; Jia, B.; Qu, X.; Qin, M. Prediction of oxygen evolution activity for NiCoFe oxide catalysts via machine learning. ACS Omega 2022, 7, 14160–14164. [Google Scholar] [CrossRef]
- Ahmed, M.G.; Tay, Y.F.; Chi, X.; Zhang, M.; Tan, J.M.R.; Chiam, S.Y.; Rusydi, A.; Wong, L.H. Efficient Ternary Mn-Based Spinel Oxide with Multiple Active Sites for Oxygen Evolution Reaction Discovered via High-Throughput Screening Methods. Small 2023, 19, 2204520. [Google Scholar] [CrossRef] [PubMed]
- The Royal Society of Chemistry’s Interactive Periodic Table Database. Available online: https://www.rsc.org/periodic-table/ (accessed on 11 January 2024).
Features | Abbreviation | Unit | Formula |
---|---|---|---|
valence electron number | VEN | ||
relative atomic mass | RAM | ||
atomic number | AN | ||
non-boned atomic radius | RA | Å | |
covalent radius | RC | Å | |
first ionization energy | FIE | kJ mol−1 | |
Pauling scale electronegativity | EP | ||
the number of electrons in the outermost d orbital | DE |
Sample | Fe | Co | Mn | Experimental (mV) | Predicted (mV) |
---|---|---|---|---|---|
exp-1 | 0.12 | 0.4 | 0.48 | 344 | 369.3 |
exp-2 | 0.3 | 0.4 | 0.3 | 367 | 389.1 |
exp-3 | 0.3 | 0.2 | 0.5 | 372 | 415.6 |
exp-4 | 0.5 | 0.5 | 0 | 391 | 442.2 |
exp-5 | 0 | 0.5 | 0.5 | 356 | 385.6 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhang, L.; Hou, J.; Ji, H.; Meng, D.; Qi, J.; San, X. Prediction of Oxygen Evolution Activity for FeCoMn Oxide Catalysts via Machine Learning. Catalysts 2024, 14, 513. https://doi.org/10.3390/catal14080513
Zhang L, Hou J, Ji H, Meng D, Qi J, San X. Prediction of Oxygen Evolution Activity for FeCoMn Oxide Catalysts via Machine Learning. Catalysts. 2024; 14(8):513. https://doi.org/10.3390/catal14080513
Chicago/Turabian StyleZhang, Lei, Jinfei Hou, Honglin Ji, Dan Meng, Jian Qi, and Xiaoguang San. 2024. "Prediction of Oxygen Evolution Activity for FeCoMn Oxide Catalysts via Machine Learning" Catalysts 14, no. 8: 513. https://doi.org/10.3390/catal14080513