Fuel Cell Operation with Oxygen Enrichment
Bibliographic record
Abstract
Abstract Experimental results on the performance of a Ballard 5 kW proton exchange membrane fuel cell stack for different oxygen contents in the oxidant are presented. A description of the experimental setup is given. Polarization, power, and efficiency curves as a function of the current density, for different oxygen concentrations are presented. This detailed characterization of the fuel cell stack behavior is required in order to evaluate the effects of oxygen enrichment on the net power output of the stack. This investigation is done in the framework of a project on stand‐alone power generation systems using renewable energy sources, and based on hydrogen production and storage. An electrolyzer, powered by the excess electrical energy from renewable energy sources, produces hydrogen. The stored hydrogen could then be used to feed an energy conversion device, such as a fuel cell stack, which acts as a secondary power source in periods of high demand. Therefore, a second objective is to evaluate the possibility of using the oxygen produced by the electrolyzer for the enrichment. Other oxygen enrichment techniques such as membrane gas separation and pressure swing adsorption are also discussed. Net available power and system efficiency are used as comparison factors.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".