Hydrogen Bubble Templating of Fractal Ni Foams for Water Oxidation in Alkaline Media
Bibliographic record
Abstract
Dynamic Hydrogen Bubble Templating of Ni electrodes (NiDHBT) is applied to form fractal macroporous structures and their properties towards oxygen evolution reaction (OER) is assessed. Upon varying Ni electrodeposition conditions, morphological features with percolating porosity were finely tuned and electrochemical activity towards O2 evolution reaction (OER) in 1M KOH were enhanced. As demonstrated by cyclic voltammetry, NiDHBT electrochemical features displayed a tremendous increase (x 250) of the electrochemically active Ni surface atoms, with strong mechanical adherence to the underlying substrate. Under constant electrolysis (0.25 A cm-2), OER potentials remained stable and occurred at lower potentials (shifted by ca. 300 mV) as compared to a bare Ni plate. Contact angle measurements confirm the super-aerophobic nature of the fractal Ni films, which leads to fast and sustainable release of O2 bubbles during potentiostatic measurements. Due to the versatility of the DHBT method, fractal Ni-based, e.g. NixFe1-x, electrodes for use in advanced electrolysis cell configurations can be envisioned, to further examine transport enhancement in relevant industrial systems and its tolerance to commercial alkaline solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".