Impact of the Structural and Chemical Parameters of Carbon-Supported Pt(Ni)-Based Catalysts Towards Phosphoric Acid Poisoning for HT-PEMFC Application
Notice bibliographique
Résumé
High-temperature proton exchange membrane fuel cells (HT-PEMFCs) are interesting alternatives to fossil fuel-based technologies for power generation. Their higher operating temperature compared to classic low temperature (LT) PEMFCs is highly beneficial regarding the total system complexity and weight, especially for applications which cannot involve bulky/heavy cooling systems, like aeronautics. However, at the current state-of-art of the technology, the membrane electrode assembly performance in HT-PEMFC does not reach that of LT-PEMFCs and needs to be improved; this requires better catalyst material, catalyst layer structure and membrane doping [1], [2]. A given catalyst performance is largely influenced by phosphoric acid electrolyte poisoning [3], [4] and some studies have already evaluated such poisoning level on various catalysts toward oxygen reduction reaction [5], [6]. The current study further addresses this issue and specifically aims to understand the impact of various parameters which should impact the catalyst activity in H 3 PO 4 electrolytes (loading, nano-particle size and shape, chemistry of the catalyst particles (Pt vs PtNi), density of aggregates, nature of the carbon support) and to evaluate the poisoning effect whether at low potential (anode) or at high potential (cathode). The effect of different parameters was unveiled thanks to a catalyst library (Figure a), analyzed comparatively in 1 M HClO 4 and 1 M H 3 PO 4 electrolytes at room temperature with a classic rotating disk electrode set-up and a gas diffusion electrode set-up. Pseudo CO-Stripping voltammetry, Hupd and CO-stripping voltammetry enable to shed light on the poisoning of the Pt surfaces: the electrochemical surface area (ECSA) is divided by a factor around 2 in H 3 PO 4 electrolyte compared to HClO 4 and varies according to the catalyst properties. The impact on hydrogen oxidation reaction is then noticeable. The poisoning at high potential (> 0.6 V vsRHE ) was mainly evaluated thanks to the ORR activity (Figure b): the Pt nanoparticle size/shape, their loading on the carbon support and alloying with Ni do impact their ORR activity. All the catalysts exhibit significantly lower activity in H 3 PO 4 than in HClO 4 (by a factor ca 10). The mechanisms of H 3 PO 4 -induced poisoning and potential strategies to mitigate it will be detailed. [1] S. S. Araya et al. , “A comprehensive review of PBI-based high temperature PEM fuel cells,” Int J Hydrogen Energy , vol. 41, no. 46, pp. 21310–21344, Dec. 2016, doi: 10.1016/j.ijhydene.2016.09.024. [2] R. E. Rosli et al. , “A review of high-temperature proton exchange membrane fuel cell (HT-PEMFC) system,” Int J Hydrogen Energy , vol. 42, no. 14, pp. 9293–9314, Apr. 2017, doi: 10.1016/j.ijhydene.2016.06.211. [3] B. F. Gomes et al. , “Effect of phosphoric acid purity on the electrochemically active surface area of Pt-based electrodes,” Journal of Electroanalytical Chemistry , vol. 918, Aug. 2022, doi: 10.1016/j.jelechem.2022.116450. [4] N. Sugishima et al ., “Phosphorous Acid Impurities in Phosphoric Acid Fuel Cell Electrolytes: I . Voltammetric Study of Impurity Formation,” J Electrochem Soc , vol. 141, no. 12, pp. 3325–3331, Dec. 1994, doi: 10.1149/1.2059334. [5] Q. He et al , “Influence of phosphate anion adsorption on the kinetics of oxygen electroreduction on low index Pt(hkl) single crystals,” Physical Chemistry Chemical Physics , vol. 12, no. 39, pp. 12544–12555, Oct. 2010, doi: 10.1039/c0cp00433b. [6] K. ‐L. Hsueh et al , “Effects of Phosphoric Acid Concentration on Oxygen Reduction Kinetics at Platinum,” J Electrochem Soc , vol. 131, no. 4, pp. 823–828, Apr. 1984, doi: 10.1149/1.2115707. Figure 1
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».