Influence of Gas Flow Rate on Performance of H[sub 2]S/Air Solid Oxide Fuel Cells with MoS[sub 2]-NiS-Ag Anode
Bibliographic record
Abstract
Performance of a solid oxide fuel cell with the configuration of H 2 S , ( MoS 2 + NiS + Ag ) /yttria-stabilized zirconia/Pt, air is dependent on anode and cathode compartment gas flow rates. The cell open-circuit voltage (OCV) was independent of air flow rate but increased with increasing H 2 S flow rate. A linear relationship existed between OCV and the logarithm of H 2 S flow rate. Increasing the flow rate increased the exchange rate of reaction products and H 2 S feed at the anode catalyst, thereby decreasing the local concentration of reaction products and increasing the local concentration of H 2 S ; hence, OCV increased from the Nernst effect. The magnitude of the change in OCV with temperature was consistent with calculated values based on reaction equilibria. It was found that increasing either or both air flow rate and H 2 S flow rate improved current-voltage and power density performance. The results were consistent with improved gas diffusion in the cathode with increasing air flow rate, and with both improved gas diffusion in anode and increased concentration of anodic electroactive species with increasing H 2 S flow rate. © 2003 The Electrochemical Society. All rights reserved.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".