Nickel Structures as a Template Strategy to Create Shaped Iridium Electrocatalysts for Electrochemical Water Splitting
Bibliographic record
Abstract
Low-cost, highly active, and highly stable catalysts are desired for the generation of hydrogen and oxygen using water electrolyzers. To enhance the kinetics of the oxygen evolution reaction in an acidic medium, it is of paramount importance to redesign iridium electrocatalysts into novel structures with organized morphology and high surface area. Here, we report on the designing of a well-defined and highly active hollow nanoframe based on iridium. The synthesis strategy was to control the shape of nickel nanostructures on which iridium nanoparticles will grow. After the growth of iridium on the surface, the next step was to etch the nickel core to form the NiIr hollow nanoframe. The etching procedure was found to be significant in controlling the hydroxide species on the iridium surface and by that affecting the performance. The catalytic performance of the NiIr hollow nanoframe was studied for oxygen evolution reaction and shows 29 times increased iridium mass activity compared to commercially available iridium-based catalysts. Our study provides novel insights to control the fabrication of iridium-shaped catalysts using 3d transition metal as a template and via a facile etching step to steer the formation of hydroxide species on the surface. These findings shall aid the community to finally create stable iridium alloys for polymer electrolyte membrane water electrolyzers, and the strategy is also useful for many other electrochemical devices such as batteries, fuel cells, sensors, and solar organic cells.
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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 | 0.001 |
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".