Comparing Mitigation Techniques for Effective CO Mitigation in a Polymer Electrolyte Fuel Cell
Bibliographic record
Abstract
The presence of carbon monoxide impurities in hydrogen can be a major deterrent to polymer electrolyte fuel cell (PEFC) performance. CO molecules present in hydrogen fuel rapidly get adsorbed on the platinum (Pt) active sites at the anode electrode and deactivate the surface reactions responsible for hydrogen oxidation [1]. On the other hand, obtaining 99.999% hydrogen purity requires major purification processes driving up the cost of hydrogen fuel [2]. To realise an economical hydrogen fuel price for fuel cell applications, reformate hydrogen containing CO can be used along with a suitable mitigation technique to reduce the adsorption of CO on Pt surface by means of oxidation or stripping. Catalyst modifications, air bleeding [3] and pulsed oxidation [4, 5] are commonly used techniques to resolve CO contamination issues in fuel cells. However, a detailed comparison of these techniques to define the efficiency as well as performance improvement in presence of CO contaminated fuel is not available. In this work, we present a comparison of results and scientific explanation of the use of various mitigation techniques to alleviate the effect of CO on the performance of a PEFC. Experiments were carried out on a single cell fuel cell at 80 ºC having modified anode catalyst (Pt/C and Pt-Ru/C) and a Pt/C cathode catalyst. CO mixed with pure hydrogen was fed to the anode and air was fed to the cathode. The cell potential was monitored when the fuel source was switched from pure hydrogen to CO doped hydrogen at a constant current density to study the impact of poisoning. Anode catalyst modification with ruthenium resulted in 200 mV increase in CO tolerance compared to platinum at a constant current density. Air bleeding in presence of Pt/C and Pt-Ru/C showed similar results in performance which caused a gradual potential loss when operated for extended periods. However, when pulsed oxidation was employed, the cell potential remained close to pure hydrogen potential ranges even for longer testing periods. Acknowledgement: This work was supported by Indian Oil Corporation (R&D) and Simon Fraser University under the SFU-IOCL joint PhD program in clean energy. [1] M. Abdollahi, J. Yu, P. K. T. Liu, R. Ciora, M. Sahimi, T. T. Tsotsis, J. Membrane Science, 390-391, 32 (2012) [2] D. Jansen, J. W. Dijkstra, R. W. van den Brink, T. A. Peters, M. Stange, R. Bredesen, A. Goldbach, H. Y. Xu, A. Gottschalk, A. Doukelis, Energy Procedia 1, 253 (2009) [3] L-Yu Sung, B-Joe Hwang, K-Lin Hsueh, W-Nien Su, C-Chung Yang, J. Power Sources 242 (2013) 264-272 [4] C.G. Farrell, C.L. Gardner and M. Ternan, J. Power Sources, 117, 282 (2007) [5] A. H. Thomason, T. R. Lalk, A. J. Appleby, J. Power Sources 135 (2004) 204-211
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".