Optimizing the Ultra-High Dispersion of Ir on NiO Nanosponge for Enhanced Oxygen Evolution Reaction
Bibliographic record
Abstract
Nickel oxide is known for its excellent performance in various catalytic processes, owing to its wide availability and cost-effectiveness compared to noble metal catalysts in alkaline environments. While iridium (Ir) oxide demonstrates superior catalytic performance for the oxygen evolution reaction (OER), its scarcity and high cost limit its widespread use. Achieving ultra-high dispersion of Ir addresses these challenges by reducing its required quantity while simultaneously enhancing OER efficiency. Catalysts with ultra-high dispersion exhibit superior performance and selectivity due to their increased reactivity and efficient use of active sites. However, anchoring dispersed Ir within host materials and preventing agglomeration remains a significant challenge [1,2]. In this study, we employ a two-step electrochemical anodization process to fabricate highly porous nickel oxide nanosponges [3]. We then introduce an innovative sonochemical technique that creates defects and achieves ultra-high dispersion of Ir on these porous NiO nanostructures by varying the Ir solution concentration. This single-step sonication process generates a high density of defects within the NiO matrix, stabilizing dispersed Ir sites across the nanostructure [4]. By combining electrochemical anodization with defect engineering via sonication, we achieve efficient entrapment and uniform distribution of Ir at the atomic level. Our results show that defects and the ultra-high dispersion of Ir atoms significantly enhance OER electrocatalytic efficiency. Incorporating Ir into the NiO nanosponge support maximizes the active sites, improving H 2 O molecule adsorption and its conversion to OH, resulting in more efficient catalysts with improved reaction kinetics for OER. The electrodes are characterized using advanced microscopy techniques, including FESEM, TEM, and HAADF-STEM, alongside spectroscopy methods such as XPS and ToF-SIMS [5]. Linear sweep voltammetry is used to assess the O 2 evolution activity of NiO electrodes. These results highlight a significant improvement in the electrocatalytic efficiency of nanostructured NiO electrodes modified with durable and optimized Ir co-catalysts via the sonochemical technique. [1] Q. Wang, X. Huang, Z.L. Zhao, M. Wang, B. Xiang, J. Li, Z. Feng, H. Xu, M. Gu, J. Am. Chem. Soc. 142 (2020) 7425–7433. https://doi.org/10.1021/jacs.9b12642. [2] M.-Q. Yang, K.-L. Zhou, C. Wang, M.-C. Zhang, C.-H. Wang, X. Ke, G. Chen, H. Wang, R.-Z. Wang, J. Mater. Chem. A. 10 (2022) 25692–25700. https://doi.org/10.1039/D2TA07292K. [3] U. Sultan, F. Ahmadloo, G. Cha, B. Gökcan, S. Hejazi, J.E. Yoo, N.T. Nguyen, M. Altomare, P. Schmuki, M.S. Killian, ACS Appl. Energy Mater. 3 (2020) 7865–7872. https://doi.org/10.1021/acsaem.0c01249. [4] S. Hejazi, S. Pour-Ali, A. Kosari, N. Farahbakhsh, M.S. Killian, S. Mohajernia, Sustain. Energy Fuels. (2024). https://doi.org/10.1039/D4SE01214C. [5] M. Shahsanaei, N. Farahbakhsh, S. Pour-Ali, S. Mohajernia, S. Orangpour, A. Schardt, C. Engelhard, M. Killian, S. Hejazi, J. Mater. Chem. A. (2024). https://doi.org/10.1039/D4TA01809E. Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".