Catalytic Activity and Impedance Behavior of Screen‐Printed Nickel Oxide as Efficient Water Oxidation Catalysts
Bibliographic record
Abstract
We report that films screen printed from nickel oxide (NiO) nanoparticles and microballs are efficient electrocatalysts for water oxidation under near-neutral and alkaline conditions. Investigations of the composition and structure of the screen-printed films by X-ray diffraction, X-ray absorption spectroscopy, and scanning electron microscopy confirmed that the material was present as the cubic NiO phase. Comparison of the catalytic activity of the microball films to that of films fabricated by using NiO nanoparticles, under similar experimental conditions, revealed that the microball films outperform nanoparticle films of similar thickness owing to a more porous structure and higher surface area. A thinner, less-resistive NiO nanoparticle film, however, was found to have higher activity per Ni atom. Anodization in borate buffer significantly improved the activity of all three films. X-ray photoelectron spectroscopy showed that during anodization, a mixed nickel oxyhydroxide phase formed on the surface of all films, which could account for the improved activity. Impedance spectroscopy revealed that surface traps contribute significantly to the resistance of the NiO films. On anodization, the trap state resistance of all films was reduced, which led to significant improvements in activity. In 1.00 m NaOH, both the microball and nanoparticle films exhibit high long-term stability and produce a stable current density of approximately 30 mA cm(-2) at 600 mV overpotential.
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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 | 0.001 | 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".