High-performance porous 3D Ni skeleton electrodes for the oxygen evolution reaction in AEMWEs
Bibliographic record
Abstract
A key component of green hydrogen production technologies is the fabrication of large-scale electrodes for use in anion electrolyte membrane water electrolysers (AEMWEs). One strategy to achieve that goal is to manufacture Ni-based 3D electrode skeletons that can be further catalyzed to achieve high current densities at low overpotentials. In the present work, shock-wave induced spray (SWIS) and cold spray (CS) deposition techniques were used to prepare 20 cm2 Ni-based electrode skeletons. As-deposited SWIS-sprayed Ni skeleton electrodes had 28% porosity. A further increase in porosity up to 43% was achieved by CS deposition of Ni-Al spheroidal powder made of an aluminum core encapsulated in a nickel shell, with subsequent leaching of Al. The resulting electrode showed good structural and mechanical integrity. For the more porous CS skeleton electrodes, the electrochemically active surface area was increased by a factor of 2100 compared to the bulk Ni plate. The overpotential at 10 mA cm-2 of the more active leached CS-deposited skeleton electrode was 250 mV, compared to 296 for a commercially Ni foam with 90% porosity and 365 mV for a Ni plate electrode. These coatings are an effective methodology for the preparation of 3D Ni skeleton electrodes.
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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 | 0.001 |
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".