Particle Radius Distribution Model-Based Analysis of PEFC Cathode Catalyst Degradation Mechanisms
Bibliographic record
Abstract
One of the main obstacles for the commercialization of polymer electrolyte fuel cells (PEFC) is degradation. A major source for performance decline is the cathode catalyst layer. The continuous optimization of the compound catalyst layer towards lower catalyst loading, higher mass activity and better electrochemical performance on the one hand has increased degradation and performance decline on the other hand. In state-of-the-art nanoparticle based catalysts, this decline of the performance is linked to a loss of electrochemically active surface area (ECSA) over time [1]. Furthermore, this ECSA loss was found to correlate to changes in the catalyst’s particle radius distribution (PRD) [2]. There are three mechanisms that could be responsible to changes in the particle radius distribution: 1. Ostwald ripening, i.e. dissolution of especially the smaller, less stable particles and redeposition predominantly on larger particles; 2. Coagulation of particles, i.e. merging of two smaller particles forming a bigger particle and thus decreasing the overall surface energy; 3. Inactivation of particles, e.g. by detachment from the carbon substrate, loss of electronic or ionic connection or by passivating with impurities. These degradation mechanisms lead to a decrease of ECSA and thus to a loss in specific activity of the catalyst. The current work attempts to link experimentally observed ECSA loss to the proposed degradation mechanisms. The presented model [3-7] combines the Lifshitz- Slyozov-Wagner theory for dissolution and redeposition [4,5] with Smoluchowski’s coagulation theory [6] and includes a simple deactivation term [7]. The model is able to describe temporal changes in the particle radius distribution and its moments, i.e. the number of active particles, mean particle radius, electrochemically active surface area and the active Pt mass. Properly parametrized, this model is able to give valuable insight into the structural changes in the catalyst layer that occur during PEFC degradation. In order to parametrize the model, it was fitted to experimental results of accelerated stress tests (AST, see Fig. 1) [3] that were performed under different conditions (temperature, pH, upper potential limit and waveform of the potential). It was attempted to identify the prevailing mechanism(s) and determine the kinetic parameters of the degradation process under these conditions. However, ambiguities in the fits were found, i.e. different sets of parameters corresponding to different physical scenarios could fit the experimental data. Those scenarios as well as their origins, likelihood and implications under different conditions will be discussed systematically in this talk. References [1] P. Ferreira et al. J. Electrochem. Soc. 152, A2256 (2005) [2] K. J. Mayrhofer et al. Electrochem. Comm. 10, 1144 (2008) [3] C. A. Rice et al., Electrochim. Acta, accepted (2015) [4] S.G. Rinaldo et al., J. Phys. Chem. C 114, 5773 (2010) [5] S.G. Rinaldo et al., Electrochem. Solid State Letters 14(5), B47 (2011) [6] S.G. Rinaldo et al., Phys. Rev. E 86, 041601 (2012) [7] S.G. Rinaldo et al., Phys. Chem. Chem. Phys. 16, 26876 (2014) Fig. 1. Measured and fitted ECSA loss at 70°C with upper potential limit of 0.9 and 1.2V and square wave (SW) or triangular wave form (TW) of the potential. 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 | 0.000 | 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 | 0.001 |
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".