Optimisation of Manufacturing Parameters for an Ni–Ag Fuel Cell Electrode
Bibliographic record
Abstract
Abstract The aim of this research is to optimise manufacturing parameters for a fuel cell electrode. The combination of nickel oxide, silver oxide and ammonium bicarbonate powders is used to produce the electrode. The main role of silver element is to increase the activity in the electrode. Ni–Ag electrode can be used in fuel cells as positive and negative electrodes. All powders are mixed in the benzene solution by a magnetic mixer and then compressed to form green electrode. The range of pressure in this step is between 40 and 160 MPa. The green electrode is sintered in hydrogen atmosphere through a tube furnace and then cooled to 200 °C under argon atmosphere. The range of sintering temperature and time is 500–800 °C and 10–60 min, respectively. Also, silver oxide and ammonium bicarbonate percentages are varied from 20 to 65 and 15 to 35%, respectively. All parameters including composition, pressure, sintering temperature and time are changed during electrode fabrication to achieve optimised properties in the electrode. So, it is necessary to perform several tests measuring porosity, surface area, density, weight loss, mechanical strength, shrinkage, exchange current density and metallographic photos. The optimum conditions of the electrode production resulting from this investigation include compacting pressure 60 MPa, sintering temperature 560 °C, sintering time 15 min, silver oxide percentage 50% and ammonium bicarbonate percentage 27%.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".