Comparison of Startup and Shut down cycles in a non-segmented and segmented polymer-electrolyte-membrane fuel cell hardware
Bibliographic record
Abstract
Platinum dissolution and carbon corrosion from the catalyst layer are two areas of concern for the commercialization of automotive fuel cells [1–4]. Fuel cell Start Up / Shut Down (SU/SD) and fuel starvation events are two major contributors to corrosion. Several protocols are available in the literature to investigate the carbon corrosion and SU/SD [5–7]. According to the reported studies, the corrosion rate increases the performance loss in the membrane electrode assemblies. Purging air, at both cathode and anode, is one of the strategies used to develop SU/SD events [8]. When air is purged into the anode, a hydrogen / air front flows through the flow field channels within the cell. This causes mixed potentials and reverse currents may occur. During start-up, hydrogen is fed to the oxygen-containing anode, which leads to another hydrogen / air front. Raiser et al. reported a reverse-current-decay mechanism which leads to high cathode potentials and thus to accelerated carbon corrosion [8]. Severe catalyst layer degradation effects have been reported when the cell experiences uncontrolled SU/SD [5,7,8]. Uncontrolled SU/SD cycles increase the voltage degradation. The following fundamental aspects need to be addressed to achieve the targeted lifetime with minimum voltage decay: how the carbon corrosion varies spatially; how the corrosion rate affects the performance across the active are, and what is the failure mechanism within and across the cell. To develop a universal SU/SD protocol, we propose a common platform that spatially resolves the voltage loss induced by different SU/SD. This work discusses an electrochemical platform as well as methodologies to spatially resolve the voltage and carbon loss of the membrane electrode assemblies during three different SU/SD protocols [5–7]. Controlled experiments were conducted to verify each protocol in a segmented cell. This paper compares the potential mapping of a membrane electrode assembly induced by three different SU/SD protocols that were reported in the literature [5–7]. A segmented hardware with a 50 cm2 active area was used to map the potential [1,2,4,9]. The cathode catalyst layer capacitance was measured across the segments to monitor the carbon corrosion during start/stop experiments. The pseudo-capacitive effect is used to evaluate the difference in CO2 evolution between each segment. References: [1] S.R. Dhanushkodi, M. Schwager, D. Todd, W. Merida, J. Electrochem. Soc. 161 (2014) F1315. [2] S.R. Dhanushkodi, M. Schwager, W. Mérida, ECS Trans. 64 (2014) 547. [3] S. Dhanushkodi, S. Kundu, M.W. Fowler, M.D. Pritzker, J. Power Sources 267 (2014) 171. [4] S.R. Dhanushkodi, M. Schwager, D. Todd, W. Merida, in:, M. Eikerling, J. Stumper, A. Kulikovsky, K. Mayrhofer (Eds.), 97th Can. Chem. Conf. Exhib. Abstr., 2014. [5] J.H. Kim, E.A. Cho, J.H. Jang, H.J. Kim, T.H. Lim, I.H. Oh, J.J. Ko, S.C. Oh, J. Electrochem. Soc. 157 (2010) B104. [6] E. Zakrisson, The Effect of Start/Stop Strategy on PEM Fuel Cell Degradation Characteristics, Chalmers University of Technology, 2011. [7] J. Durst, A. Lamibrac, F. Charlot, J. Dillet, L.F. Castanheira, G. Maranzana, L. Dubau, F. Maillard, M. Chatenet, O. Lottin, Appl. Catal. B Environ. 139-139 (2013) 416. [8] C.A. Reiser, L. Bregoli, T.W. Patterson, J.S. Yi, J.D. Yang, M.L. Perry, T.D. Jarvi, Electrochem. Solid-State Lett. 8 (2005) A273. [9] M. Schwager, D. Todd, S.R. Dhanushkodi, W. Merida, in:, M. Eikerling, J. Stumper, A. Kulikovsky, K. Mayrhofer (Eds.), 97th Can. Chem. Conf. Exhib. Abstr., Vancouver, BC, 2014.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".