The Electrochemistry of Metallic Nickel: Oxides, Hydroxides, Hydrides and Alkaline Hydrogen Evolution
Bibliographic record
Abstract
Ni-based catalysts in aqueous alkaline media are low-cost electrode materials for electrolytic hydrogen generation, a renewable method of producing fuel and industrial feedstock. However, Ni cathodes show a significant decrease in their hydrogen evolution reaction (HER) activity after several hours of electrolysis. Further, industrial electrolysers are often subjected to transient anodic currents, the effects of which on Ni-based catalysts are not well-known. We consider the source of electrode deactivation and the effects of temporary anodic currents on smooth metallic Ni electrodes in alkaline solutions by cyclic voltammetry (CV), galvanostatic and potentiostatic polarization, X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). Polished surfaces are covered by a bilayer composed of α-Ni(OH) 2 underlaid by non-stoichiometric NiO x . Below the reversible hydrogen electrode (RHE) potential, the air-formed layer mostly reduces to Ni metal and H atoms incorporate deep into the electrode material. Under industrial conditions, i.e., concentrated NaOH/KOH solutions and large cathodic current densities, α-NiH x and β-NiH x can form at the electrode surface. Above the RHE potential, NiO x , α-Ni(OH) 2 , β-Ni(OH) 2 and β-NiOOH form reversibly and mostly reduce back to Ni on subsequent cathodic polarization. However, repeated oxidation and reduction will introduce strain on a catalyst material, which may lead to its mechanical failure.
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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".