Role of Ir Decoration in Activating a Multiscale Fractal Surface in Porous Ni for the Oxygen Evolution Reaction
Bibliographic record
Abstract
Interfacial engineering of electrocatalysts is a pivotal approach for promoting the energy conversion efficiency of water electrolysis systems. Anchoring clusters or even single atoms of a foreign element on the surface of a support, usually 3D structured, can alter its local electronic structure leading to superior electrocatalytic activity. Herein, we report a method to decorate a 3D fractal Ni electrode with Ir atoms via a galvanic displacement reaction. The resulting electrode has a very low Ir loading of 1.6 μmole cm geo –2, with iridium atoms present as 4–5 nm diameter nanoclusters on the electrode surface. The activity for the electrochemical oxygen evolution reaction (OER) of the 3D fractal Ni electrode was improved by decoration with Ir, resulting in an overpotential of 195 mV at 10 mA cm –2 and a Tafel slope of 44 mV dec –1 . Apart from increasing the electrocatalytic activity for the OER due to the very presence of more active iridium atoms, the galvanic displacement reaction resulted in a factor 8–10 increase of the electrochemically active surface area due to the creation/activation of a secondary pore structure that contributes also to the better electrocatalytic performance of the resulting electrode. Using operando acoustic emission, it is demonstrated that the galvanic displacement reaction and the presence of Ir nanoclusters have the additional effect of reducing the average O 2 bubble size formed during the OER. As a result, the blocking effect of O 2 bubbles at high current density is less drastic than on the 3D fractal Ni electrode, resulting in a less severe decrease of the electrochemically active area at large current density. All three effects contribute toward improving the OER performance of the Ir-decorated 3D fractal Ni electrode.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".