High-performance porous 3D Ni skeleton electrodes for the oxygen evolution reaction
Bibliographic record
Abstract
A key component of green hydrogen production technologies is the fabrication of large-scale porous transport layer (PTL) 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 cm 2 Ni-based electrode skeletons. In our experimental conditions, the porosity of coatings prepared using the SWIS deposition system and Ni powders with particle sizes D50 = 32 and 75 μm never exceeded 28%. Higher porosity could only be achieved using the CS deposition system and a spheroidal Ni–Al powder, whose particles consist of an aluminum core encapsulated in a nickel shell. After Al leaching in an alkaline solution, the resulting electrodes showed good mechanical and structural integrity with up to 41% porosity. The electrochemical active surface area of the most porous electrodes is a factor of 2100 larger than a polished Ni plate, and it has superaerophobic properties with a captive air bubble contact angle of 151°. During 1 h of electrolysis, the overpotential at 100 mA cm −2 of the most active leached Ni–Al cold spray deposited electrode was 330 mV, compared to 380 mV for a Ni foam electrode.
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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) | 0.001 | 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".