Optimisation of Manufacturing Parameters for an Ni–Ag Fuel Cell Electrode
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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%.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it