Surfactant-Assisted Electrodeposition of Stable Porous Platinum Structures for the Oxygen Reduction Reaction
Notice bibliographique
Résumé
The demands on the alternative energy sector necessitates high quality research and development to produce economical, reliable and environment friendly energy sources. Proton exchange fuel cells (PEFCs) offer a versatile and dependable alternative to conventional energies both in the transportation and stationary power sectors. Their widespread use is, however, still restricted due to their high cost and the limited availability of platinum (Pt) resources. The high cost of PEFCs is attributed in part to the higher loading of Pt based catalyst at the cathode. The reduction of Pt loadings at the cathode is generally accompanied by significant performance losses due to sluggish oxygen reduction reaction (ORR) kinetics. This issue can be resolved by maximizing the Pt utilization without sacrificing the performance. Hence, on-going research and development work is seeking to better utilize Pt with lower loadings along with optimized performance. Compared to their solid or bulk counterparts, porous structures exhibit a high electrochemical active surface area (A ecsa) and, thus, can enhance the Pt utilization. These porous nanostructures can restructure during fuel cell cycling, and structural collapse can lead to an eventual decrease in Pt utilization.1 Stabilizing agents such as surfactants are usually employed during the Pt nanoparticle (NP) synthesis to prevent NP aggregation. These surfactants can also assist in the formation of the mesoporous structure.2 This study describes the electrochemical deposition of a stable porous Pt structure in the presence of surfactants. A site-directed electrodeposition offers advantages to the wet chemical synthesis of Pt nanocatalysts as it ensures that the Pt is deposited onto specific regions of a support that have a sufficient ionic and electrical conductivity. In this study, we demonstrated the use of anionic, cationic, and non-ionic surfactants to produce porous Pt using electrodeposition and the influence of these surfactants in stabilizing the porous structure during the ORR in acidic medium. The conditions for electrodeposition, such as concentration of surfactants and potential for the nucleation and growth stages were each optimized through a series of experiments. These surfactants included cetyl trimethylammonium bromide (CTAB), sodium dodecylsulfate (SDS), and polyethylene glycol octadecyl ether (Brij 78). We utilized scanning electron microscopy techniques to evaluate the porosity of the deposited NPs and their surface coverage. In these experiments, an initial applied pulse was used to induce nucleation of the Pt followed by the growth of these materials with further electrodeposition at a lower potential. These parameters were evaluated for their influence on the final product, such as the porosity, A ecsa, and the surface coverage these materials. The porosity, composition, and crystallinity of these mesoporous particles were confirmed using transmission electron microscopy (TEM) and selected area electron diffraction techniques. These surfactant systems each resulted in the formation of porous Pt. The porous Pt was also subjected to durability testing by cycling the applied potential over a range of oxidizing potentials. Further analysis of these materials by TEM indicated that the mesoporous structure was maintained after the durability tests with a negligible change in their half-wave potential towards the ORR. The durability testing conducted after the removal of surfactants using Soxhlet extractor confirmed the role of the surfactants in helping to stabilize the porous Pt structure.
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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 | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».