A Regular Dimpled Surface Morphology for the Oxygen Evolution Reaction
Bibliographic record
Abstract
Optimizing electrochemical reactions is essential to improving the energy efficiency of many renewable energy technologies, such as for increasing their competitive advantage across many sectors of the market. The oxygen evolution reaction (OER) is of particular importance for its applicability to chemical generation and energy storage, and its reliance on low cost materials, such as nickel based catalysts. Nickel electrodes with surface oxides (NiOx) are frequently used as anodes in these systems because they also offer reduced activation energies, exhibit sufficient catalytic activity, and durability under alkaline conditions. Persistent bubble accumulation on highly active electrodes can, however, result in blocked active sites and reduced system performance for these and other OER catalysts. The identification and development of surface morphologies that can effectively evolve and remove oxygen bubbles from the electrode surfaces could be a highly beneficial technology to further enhance the efficiency of the OER. In this work, regular dimpled Ni surfaces were prepared using self-assembled poly(styrene) templates with a distinct diameter (e.g., 1 μm). The electrodeposition of Ni into these assembled templates was tuned to produce four types of dimpled surface textures. The electrochemical activity of these regular features were evaluated for the OER to investigate their influence on the mass transport properties. Enhancements to the OER efficiency were demonstrated for these systems when compared to flat Ni electrodes. The wettability of the dimpled Ni electrodes was characterized by contact angle measurements acquired both before and after electrochemical cycling. Theoretical wetting models were applied to the surface morphologies to correlate the results with adhesion of the oxygen bubbles and partial wetting with the electrolyte. The regular design of these micro- and nanostructured surfaces enables further correlation of structural morphologies in Ni based electrodes to their electrochemical performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".