Particle Radius Distribution Model-Based Analysis of PEFC Cathode Catalyst Degradation Mechanisms
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».