Cathodic Pd<sub>1-X</sub>Ni<sub>X</sub> Nanocatalyst Development for Alkaline Fuel Cell Applications
Notice bibliographique
Résumé
Alkaline fuel cells are a versatile, low-emission alternative energy source for use in transportation, as well as for portable and stationary power applications. These systems use chemical energy from the electrochemical conversion of hydrogen and oxygen gas to produce electrical energy, with only water and heat as by-products. They operate through two interdependent redox reactions within the cell consisting of oxidation of hydrogen fuel at the anode and reduction of oxygen at the cathode. One of the most significant barriers that fuel cells face is the sluggish nature of the oxygen reduction reaction (ORR) and its dependence on platinum (Pt), a rare and costly metal, as a catalyst for the reaction. Through the development and optimization of the structures and materials, the amount of precious metal catalyst can be decreased without sacrificing performance and enabling an increase in the commercial viability of the device. An alternative cathode material is palladium (Pd), which possesses similar chemical properties to Pt but is more stable in alkaline media and available in a greater abundance. Additionally, the catalytic ability of Pd is comparable to that of Pt, but due to the electronic structure of Pd, it bonds oxygen to a stronger extent, leading to a reduced activity. Theoretical analyses of the Pd electronic structure have shown that tuning the metal d-band decreases the oxygen-metal adsorption strength and increases the catalytic ability.1 Tuning of the metal d-band can be achieved experimentally through the addition of ancillary metals, thus altering the electronic structure of the catalyst. Furthermore, volcano trends of metal alloys in acidic media have shown a significant increase in activity when plotted against the oxygen adsorption strength.2 The increase in activity is particularly evident when Pt, for example, is alloyed with ancillary nickel (Ni). Due to the decreased stability of Pt in alkaline media, this study aims to assess the variations in activity of Pd through the addition of varying amounts of Ni. The incorporation of Ni increases the Pd utilization and incorporates a more cost-effective and abundant transition metal, enabling a decrease in the amount of precious metal required for the catalyst. Furthermore, the use of nanoparticles as the basis for the cathode catalyst creates a high surface area on which the ORR can occur. Nanoparticles allow for a more efficient use of the catalyst materials and, consequently, decrease the amount of metal required and the overall cost of the catalyst. This study assesses the viability of palladium-nickel nanocatalysts synthesized using a water-in-oil microemulsion3 method. The synthetic method was optimized to produce small, well-dispersed nanoparticles with a narrow size distribution ideal for the ORR. Furthermore, the synthesis was tuned to produce the desired ratios of Pd to Ni to determine an optimal composition for increased catalytic performance. The analysis of these nanocatalysts includes assessing the relationship between their composition, structure, and function using a combination of electrochemical and microscopy techniques. The results demonstrate a promising catalytic activity, indicating a comparable performance for all compositions. The consistent performance allows for a significant decrease in the precious metal content through the replacement with a more abundant and cost-effective metal. Further studies are being carried out to better understand the stability of these materials in alkaline media under a range of operating conditions relevant to the ORR. These studies aim to enhance the durability and to increase the overall viability of the catalyst. B. Hammer, J.K. Nørskov, Theoretical surface science and catalysis - calculations and concepts, Adv. Catal. 45 (2000) 71-129. A. Holewinski, H. Xin, E. Nikolla, S. Linic, Identifying optimal active sites for heterogeneous catalysis by metal alloys based on molecular descriptors and electronic structure engineering, Curr. Opin. Chem. Eng. 2 (2013) 312-319. S. Lankiang, M. Chiwata, S. Baranton, H. Uchida, C. Coutanceau, Oxygen Reduction Reaction at Binary and Ternary Nanocatalysts Based on Pt, Pd and Au. Electrochim. Acta. 182 (2015) 131–142.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,002 | 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 ».