Understanding the Effect of Ni-Substitution on the Oxygen Evolution Reaction of (100) IrO<sub>2</sub> Surfaces
Bibliographic record
Abstract
The electrolysis process of water is impeded by the slow kinetics of the oxygen evolution reaction (OER). While iridium oxide (IrO 2 ) is considered one of the most efficient metal catalysts for the OER, the adsorption of the OER intermediates at the IrO 2 surface is not optimal, the *OH intermediate being too strongly adsorbed. The substitution of iridium by another metal cation is then a common strategy to improve the catalytic activity. A combined computational and experimental approach was followed to elucidate the fundamental effect of nickel on the OER activity of (100)-oriented IrO 2 . To achieve this, (100)-oriented Ir 1– x Ni x O 2 model surfaces were synthesized by pulsed laser deposition. Detailed structural and chemical characterizations were performed by X-ray diffraction, transmission electron microscopy, atomic force microscopy, and X-ray absorption spectroscopy. The composition of the film was varied between 0 and 15 at %, which is the solubility limit for the substitution of iridium by nickel atoms in (100)-oriented Ir 1– x Ni x O 2 . All electrochemical characterizations were performed in an alkaline electrolyte, in which nickel dissolution is not observed by X-ray photoelectron spectroscopy. The current density recorded at +1.6 V ( vs RHE) was increased from 35 to 348 μA cm ox –2 between IrO 2 to Ir 0.85 Ni 0.15 O 2 . Density functional theory calculations showed that the energy diagram of the OER intermediates was modified by the substitution of iridium with nickel atoms through a ligand effect. Also, it was found that iridium is the active site for low nickel content, whereas nickel is the most active site when its concentration reaches 15 at %.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".