CO<sub>2</sub> Enrichment in Anode Loop and Correlation with CO Poisoning of Low Pt Anodes in PEM Fuel Cells
Bibliographic record
Abstract
Abstract In automotive fuel cell systems anode fuel re‐circulation is often used to achieve high hydrogen utilization rates which reduces the hydrogen consumption of the fuel cell car, as well as it is an appropriate way to control hydrogen emissions. During operation hydrogen is consumed, while residual gases increase in the previously mentioned hydrogen loop. Besides nitrogen, we have found that CO2 accumulates in the anode loop and concentrations between 150–350 ppm were measured for varying current densities. We attribute this finding to CO2 crossover from the cathode to the anode and subsequent enrichment in the anode loop. To study the effect of this relatively small CO2‐concentration on the cell performance, tests were conducted with a proton exchange membrane (PEM) 45 cm2 single test cell with contaminated hydrogen/air feed. The data clearly indicate that electrochemical reduction of CO2 to CO takes place which has a significant impact on the cell performance due to blocked catalyst sites by CO affecting the current density of the hydrogen oxidation reaction (HOR). The measurements with hydrogen containing CO2 were matched with hydrogen plus CO measurements to quantify the impact and to determine a “CO‐equivalent concentration” for CO2. Consequences for the operation strategy of fuel cell systems are given.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".