Mathematical Modelling and Experimental Analysis of Thin, Low-Loading Fuel Cell Electrodes
Bibliographic record
Abstract
The use of expensive platinum in PEFC remains a major barrier to achieve a low cost fuel cell stack. To reduce stack cost, thin, low loading electrodes have been studied at various laboratories [1]. Experimental data showed that the cell performance is severely limited for cathode electrode loadings below 0.1 mg/cm2. This decreased performance has been attributed to several physical processes [2]. First, the oxygen reduction reaction (ORR) is a multi-step reaction that does not follow Tafel kinetics. Low loading electrodes might operate following a different ORR pathway. Second, due to the higher local mass flux to the Pt sites, mass transport resistances in the ionomer thin film, presumably covering the catalytic sites, might limit oxygen transport to the reaction sites. Finally, water management issues are exacerbated due to the reduced thickness and thereby increased water production rate per unit volume. Experimental and theoretical investigations in thin, low loading fuel cell electrodes are being performed in our laboratory. Thin, low loading fuel cell electrodes of various ionomer and Pt loadings, and Pt/C content were fabricated and tested under a variety of operating conditions. In order to accurately control catalyst/ionomer loading and layer uniformity, the electrodes were deposited on a membrane using a material inkjet printer. Results show that cells with cathode Pt loadings of 0.025 mg/cm2 are severely limited in the kinetic region with cell voltages as low as 0.6V at 200 mA/cm2 under hydrogen/air conditions at ambient pressure. At cell voltages of 0.2 V however, mass activities as high as 50 A/mgPt and current densities above 1 A/cm2were observed [3]. Macro-scale mathematical modelling studies of low loading electrodes have either been based on simplified one-dimensional models, or have relied on simple Tafel kinetics for the ORR. In this work, meso- and macro-scale simulations of thin, low loading electrodes are performed using OpenFCST, an open-source fuel cell simulation software developed by our research group [4]. A multi-dimensional, non-isothermal, two-phase membrane electrode assembly (MEA) model was developed. The Butler-Volmer/Tafel representations of the HOR and ORR were replaced by multi-step reaction kinetic models, i.e., the dual path and double trap models proposed by Wang et al. and modified by our group [5]. To account for thin film mass transport resistances, a pseudo-agglomerate model that accounts only for the ionomer thin film was implemented. The model is justified by microscopy observations and our previous mathematical modelling results that suggest that, based on the expected agglomerate size, the ionomer thin film is the most important micro-scale parameter [6]. The use of multi-step kinetic and ionomer thin film models improves fuel cell predictions for both conventional and low loading electrodes in the kinetic, ohmic and mass transport regions. Water build-up in thin, low loading electrodes is likely to be exacerbated. Several researchers have proposed a variety of pore-size distribution (PSD) based models to account for the layers' micro-structures [7]. These models however have not yet been implemented in a multi-dimensional MEA model. A dual wettability PSD based model was implemented in OpenFCST. The PSD based model has been compared to the common saturation-based two-phase flow model in porous media, e.g., [8]. Preliminary results show that the PSD model is able to identify discontinuities in the saturation profiles inside the MEA as a result of the MPL hydrophobic nature and small PSD. The saturation-based model on the other hand, provides a continuous saturation profile. Focused ion beam-scanning electron microscopy (FIB-SEM) and nano-computed tomography (nanoCT) have been performed on our low loading electrodes. A statistical analysis tool was developed and used to compute multiple correlation functions to assess porosity, active area and pore connectivity. Using the statistical correlation functions and a novel different phase neighbors based simulated annealing strategy, meso-scale reconstructions of the electrode have been created [9]. The electrode reconstructions can be used to estimate the effective transport properties in each phase of the electrode using OpenFCST. [1] J. Wee et al., J. Power Sources, 2007, 165(2) 667. [2] J. Owejan et al., JES, 2013, 160(8), F824. [3] S. Shukla et al., Electrochimica Acta, 2015, 156:289. [4] M. Secanell et al., ECSTrans., 2014, 64(3): 655. [5] J. Wang et al., JES., 2006, 153(9), A1732; J. Wang et al., J. Phys. Chem. A, 2007, 111, 12702; Moore et al., JES, 2013, 160(6), F670. [6] M. Moore et al., JES., 2014, 161(8):E3125. [7] A. Weber. JES., 2004, 151(10), A1715. [8] D. Natarajan, JES, 2001, 148(12), A1324. [9] L. Pant et al., Phys.Rev.E, 2014, 90, 023306. Figure 1
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".