Experimental Analysis of a Small-Scale Flowing Electrolyte–Direct Methanol Fuel Cell Stack
Bibliographic record
Abstract
A small-scale five-cell flowing electrolyte–direct methanol fuel cell (FE-DMFC) stack with U-type manifold configuration and parallel serpentine flow bed design was studied experimentally. The active area of a single cell was approximately 25 cm2. For every stack cell, diluted sulphuric acid was used as the flowing electrolyte (FE) which was circulated through a porous medium placed between two Nafion® 115 polymer electrolyte membranes. The stack performance was studied over a range of several operating conditions, such as temperature (50–80 °C), FE flow rate (0–17.5 ml/min), methanol concentration (0.5–4.0 M), and methanol solution flow rate (10–20 ml/min). In addition, the stack cell to cell voltage variations and the effects of the FE stream interruption on the output voltage were investigated at various operating loads. Experimental results showed that utilization of the FE effectively reduced methanol crossover and improved the stack power output. It was found that increasing the FE flow rate enhanced the stack capability to operate at higher inlet methanol concentrations without any degradation to the performance. The results also demonstrated that the stack power output can be directly controlled by regulating the FE stream especially at high operating currents.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".