Amorphous Ni-Based Nanoparticles for Alkaline Oxygen Evolution
Bibliographic record
Abstract
Amorphous Ni 79.2– x Co x Nb 12.5 Y 8.3 ( x = 0 and 5 at %) nanoparticles were produced through a two-stage ball milling process for use as electrocatalysts in the alkaline oxygen evolution reaction (OER). Cyclic voltammetry demonstrated that these amorphous alloys have excellent long-term cyclic durability when compared to crystalline Ni and Ni 95 Co 5 . Potentiostatic polarization measurements showed that the catalytic performance of amorphous Ni 74.2 Co 5 Nb 12.5 Y 8.3 was maintained by displaying a low overpotential of 346 mV at 10 mA cm –2 even after 10,000 cycles, while deactivation could be observed for the other catalysts. XPS analysis revealed that the retention of catalytic activity was attributed to the stabilization of β-NiOOH. Through transmission electron microscopy analysis, it was found that the surface of amorphous Ni 74.2 Co 5 Nb 12.5 Y 8.3 remained amorphous, although definitive signs of electrochemically induced Nb leaching could be observed. The leaching of Nb aided the overall performance since Nb is not electrochemically active toward the OER and was solely added to facilitate the formation of an amorphous structure. These findings not only support the excellent long-term stability and activity of amorphous Ni 74.2 Co 5 Nb 12.5 Y 8.3 nanoparticles toward the alkaline OER but also demonstrate how anodic cycling can be used to condition the surface of the catalyst.
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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 | 0.000 |
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".