CO Tolerance of Nano-architectured Pt-Mo Anode Electrocatalysts for PEM Fuel Cell Systems
Bibliographic record
Abstract
Enhanced CO tolerance of PEM fuel cell anode electrocatalysts is essential for improving the performance of PEM fuel cell systems operating with hydrocarbon reformers. This work explores the CO tolerance of PEM fuel cell membrane electrode assemblies (MEAs) fabricated with two promising nano-architectured Pt-Mo anode electrocatalysts -- Pt0.8Mo0.2 alloy and MoOx@Pt core-shell -- which demonstrated extremely high CO tolerance in previous thin-film electrode studies. By holding all other MEA components constant, polarization tests in pure H2 and H2 streams contaminated with up to 1000 ppm CO provided a basis for assessing the relative CO tolerance of the catalysts. Anode electrocatalyst stability was also investigated by operating the MEAs in 100 ppm CO over several days. Commercial and in-house fabricated MEAs with a conventional anode catalyst were used for comparison. Pt0.8Mo0.2 demonstrated the highest performance in CO, with a voltage drop of only 95 mV in 100 ppm CO at 0.5 A/cm 2 , compared to drops of 230 mV for PtRu and 260 mV for the core-shell electrocatalyst. However, the MoOx@Pt electrochemical performance, with its reduced Pt content, was comparable to the highly active Pt0.8Mo0.2 electrocatalyst for CO concentrations below 50 ppm on a per gram of precious metal basis, and preliminary stability studies indicate that the core-shell structure may also provide protection against detrimental Mo leaching in the acidic electrolyte. Both Mo-containing catalysts were poorly utilized, perhaps owing to residual surface contamination from the synthesis procedures, suggesting that their performance could be significantly improved with further optimization of fabrication procedures. A system-level model was also used to explore the impact of current-day and potential advances in CO tolerant electrocatalysts on the system performance of a PEM fuel cell system operating in conjunction with a hydrocarbon autothermal reformer and a preferential CO oxidation (PROx) reactor for CO clean-up. Empirical models of CO tolerance fuel cell performance were based on experimental data obtained with the Pt0.8Mo0.2 alloy tested in the experimental portion of this study. As CO tolerance was increased, system efficiencies improved due primarily at conditions where the fuel cell stack operated at high current densities, and the improvement is largely to higher fuel cell voltages and to a lesser extent to reductions in parasitic loads. Furthermore, increased fuel cell CO tolerance permitted significantly lower PROx CO selectivities and CO conversions without the significant penalties in overall system efficiency observed with the present-day CO tolerance of Pt alloy electrocatalysts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".