Bibliographic record
Abstract
In this study, water removal techniques were experimentally developed as a diagnostic tool for the investigation of polymer electrolyte membrane (PEM) fuel cell performance in order to achieve consistent, reliable and repeatable performance. The three water removal methods developed include cell orientation, cell shaking and hydraulic permeation, and instantaneous improvement in the fuel cell performance had been observed when these methods were employed. All the experiments were carried out with a single PEMFC with the active surface area of 50 cm2. Pure hydrogen was used as fuel and air was used as oxidant. It was found that operating the fuel cell at an orientation of 45 degrees towards gas outlet ports helped removing trapped liquid water from the cathode side; when cell assembly was shaken during the fuel cell operation, liquid water was observed to be removed from the cell structure and cell performance was improved. Water removal by hydraulic permeation was achieved by maintaining the pressure of hydrogen stream 10 kPa lower than that of air stream, which helped water removal by the anode gas stream. It was observed that the simultaneous application of these water removal methods allowed the test results obtained to be consistent, reliable and repeatable, and in general it increased the performance by 25% in ohmic polarization region and 30% in mass transport dominant region. Then these methods were applied to investigate the effects of the various operating conditions on the performance of the PEM fuel cell, including the effect of reactants temperature, pressure, stoichiometry, humidification and cell aging.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".