Dimensional effect of graphite flow field channels of a direct methanol fuel cell under different operating conditions
Bibliographic record
Abstract
Direct methanol fuel cells are a potential candidate to replace traditional power sources for portable applications. The flow field design, manufacture, and optimization are of great significance to the cell performance. When the scale of flow channels decreases to the level of submillimeter‐scale, it is favourable to the reactant and product management. This paper focuses on the effectiveness of using a multi‐tooth planing technique to create submillimeter‐scale parallel channels in a graphite sheet. Besides the structural parameters of flow channels, a series of operating parameters are experimentally investigated, including methanol concentration, methanol feed rate, oxygen feed rate, cathode backpressure, and environmental temperature. Results indicate that the prepared channels promote a higher cell performance than the traditional design with a larger scale. It is beneficial to both the anode and cathode performances, but it has a more prominent effect at the anode. The methanol concentration of 4 mol/L yields the best performance. Using a relatively lower methanol feed rate below 0.5 mL/min has a more obvious effect on the fuel cell. The cell performance is insensitive to the change of cathode oxygen feed rate and backpressure especially when the oxygen can be sufficiently supplied. In this case we can use lower levels of oxygen feed rate and cathode backpressure. The cell temperatures and influence of environmental temperature are also discussed.
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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 | 0.001 |
| 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 | 0.000 |
| 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".