Impact of Increased Anode CO Tolerance on Performance of Hydrocarbon-Fueled PEM Fuel Cell Systems
Bibliographic record
Abstract
Recent advances in anode electrocatalysts for low-temperature PEM fuel cells are increasing tolerance for CO in the H2-rich anode stream. This study explores the impact of current day and future advances in CO-tolerant electrocatalysts on the system efficiency of low-temperature Nafion-based PEM fuel cell systems operating in conjunction with a hydrocarbon autothermal reformer and a preferential CO oxidation (PROx) reactor for CO clean-up. This study explores the effects of incomplete H2 cleanup by preferential oxidation reactors for partial CO removal, in combination with reformate-tolerant stacks. Empirical fuel cell performance models were based upon voltage-current characteristic from single-cell MEA tests at varying CO concentrations with new alloy reformate-tolerant electrocatalysts tested in conjunction with this study. A system-level model for a 5 kW maximum liquid-fueled system has been used to study the trade-offs between the improved performance with decreased CO concentration and the increased penalties from the air supply to the PROx reactor and associated reduction in H2 partial pressures to the anode. As CO tolerance is increased over current state-of-the-art Pt alloy catalysts system efficiencies improve due to higher fuel cell voltages. Furthermore, increasing CO tolerance of anode electrocatalysts allows for increased reformer efficiency by reducing PROx CO conversion requirements.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".