Impact of Changing Mode on the Execution of 100 W Solid Oxide Fuel Cells (SOFCs)
Bibliographic record
Abstract
Solid oxide fuel cells (SOFCs) are electrochemical reactors that can proficiently convert fuel gas, chemical fuel into electrical fuel with insignificant ecological dangers.SOFCs is a developing innovation for clean, solid and adaptable fuel production.The high-temperature SOFCs have a few favorable circumstances contrasted with different sorts of fuel components, for example, the contamination percentage, and higher electrical productivity (~75%) [1,2].Furthermore, SOEC (Solid oxide electrolysis cells) innovation has the preferred standpoint that it can be based on the accessible solid oxide fuel unit (SOFC) innovation [3].It can be operated in a reverse mode (SOECs and Co-Electrolysis) to electrolyze steam and carbon dioxide to produce syngas, which offers an optional approach to convert low-emission electrical fuel into stored chemical fuel.Where the SOFCs mode can be converted to SOECs mode to produce the synthesis gas, generally referred to as syngas, which is a blend of hydrogen and carbon monoxide [4].Besides, joining both SOFC and SOEC in one mode can be a promising innovation to store electrical fuel as chemical fuel and to reconvert it into electricity upon request where it Impact of Changing Mode on the Execution of 100 W Solid Oxide Fuel Cells (SOFCs)
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".