Stabilizing Hydrous β-NiOOH for Efficient Electrocatalytic Water Oxidation by Integrating Y and Co into Amorphous Ni-Based Nanoparticles
Bibliographic record
Abstract
A two-stage ball milling process was used to synthesize amorphous Ni 79.2 Nb 12.5 Y 8.3 and Ni 74.2 Co 5 Nb 12.5 Y 8.3 nanoparticles from elemental powders. The two-stage ball milling process provides a scalable and industrially applicable method for producing non-metalloid amorphous nanoparticles. The amorphous nanoparticles displayed excellent catalytic performance toward the oxygen evolution reaction (OER) in 1 M KOH, displaying lower overpotentials than IrO 2 at 10 mA cm –2 . The addition of Co in the amorphous alloy reduced the overpotential to 288 mV at 10 mA cm –2 . The pairing of X-ray photoelectron spectroscopy and in situ X-ray absorption spectroscopy revealed that the improved OER activity of amorphous Ni 74.2 Co 5 Nb 12.5 Y 8.3 was attributed to the catalytic synergy between Y and Co. The integration of Y supported proton-coupled electron-transfer processes that assisted with the electrostatic adsorption of OH – and formation of oxyhydroxide species, while Co sites enabled metal-oxo bonding to prevent Ni overcharging and the stabilization of β-NiOOH. The catalytic synergy between Y and Co reduces the amount of Co needed to enhance the OER activity of Ni-based alloys and lessens the dependence on Co, which is in high demand in many renewable energy and storage applications.
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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) | 0.000 | 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".