Glancing Angle Deposition for Enhanced Oxygen Evolution Reaction
Bibliographic record
Abstract
The oxygen evolution reaction (OER) presents a major kinetic challenge in alkaline water electrolysis. Nickel oxide (NiO) is generally accepted as a favorable material due to its abundance and stability, yet it also exhibits limited intrinsic catalytic activity. In this study, nanocolumnar NiO electrodes were fabricated using glancing angle deposition (GLAD), and key deposition parameters, film thickness, angle, and deposition rate, were systematically tuned to optimize OER performance. An overpotential of 311 mV at 10 mA/cm 2 was achieved for a 512 nm thick film deposited at 78° with an increasing‐rate profile. Interestingly, this performance peak coincided with a morphological transition zone within the nanocolumns, where growth dynamics likely promote a favorable defect landscape. In contrast, thicker films showed reduced activity, likely due to diminished defect density associated with further morphological evolution. Electrochemical cycling further enhanced performance via a self‐reconstruction process, forming branch‐like NiOOH/Ni(OH) 2 features and reducing the overpotential to 269 mV. These results highlight the impact of growth‐induced structural transitions and defect formation on catalytic performance, positioning GLAD as an effective platform for rational OER catalyst design.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".