Plasma functionalization of graphene nanoflakes for non-noble catalyst in fuel cells
Notice bibliographique
Résumé
Two major obstacles currently limit the commercial viability of proton exchange membrane fuel cells (PEMFCs): cost and operating life. The most important contribution to the high cost of these systems is the use of platinum (Pt) as a catalyst, especially on the cathode where the oxygen reduction reaction (ORR) takes place. This thesis is part of the intensive international research efforts to find an alternative substitute for platinum. Doped carbon nanomaterials have been identified as a potential replacement for platinum ORR-electrocatalyst due to their excellent electrical conductivity and chemical resistance in acidic and basic environments. By doping the carbon nanomaterials with nitrogen, in the preferred pyridinic and quaternary forms, iron can be coordinated to complete the catalytic sites on an atomic scale. Nanocrystalline powder has recently been developed in the Plasma Processing Laboratory (PPL) at McGill University. The particles constituting the powder, in the form of graphene nanoflakes (GNFs), are formed by the superposition of ten graphene layers on average and have a spatial extension on the order of hundreds of nanometers. These planes have many terminating edges upon which nitrogen can be incorporated due to their high reactivity. The crystallinity also leads to a highly stable material paving the way for a promising catalyst replacement in the PEMFC.The objective of this thesis is to take these crystalline GNFs and dope them with nitrogen in high quantities on the edges of the graphene planes in pyridinic and quaternary forms to create the catalytic sites necessary for ORR. An inductively-coupled thermal plasma (ICP) is used to dissociate methane at very high temperatures, with homogeneous GNF nucleation commencing shortly after by way of rapid quenching. Nitrogen doping occurs in a second treatment phase by manipulating plasma conditions in order to create excited and dissociated nitrogen species that react at the edges of the GNFs.Nitrogen doping up to 33.4 at.%Ntotal has been demonstrated, which bests any other nitrogen-doped graphene by at least a factor of 2.6 and even the best nitrogen-doped carbonaceous material by 67%. Pyridinic and quaternary nitrogen constitute 8.2 at.%Npyrid and 4.9 at.%Nquat, respectively. This has been done whilst maintaining the crystalline structure and without introducing defects or impurities that would otherwise affect crystallinity and durability of these materials in future potential applications. Sequential in-situ GNF synthesis and deposition/dispersion onto a carbon cloth, which functions as the gas diffusion layer (GDL) in fuel cells, has also been demonstrated. Solid anchoring of the deposited GNFs on the individual carbon fibers is observed, and columnar growth with open film porosity reveals GNF films of micrometer-scale thicknesses. These films also exhibit desirable properties required for the ORR: porosity, homogeneity over a large area, good contact to the electrical transport throughout the network of particles and accessibility to the catalytic sites. The obtained properties seem in fact unmatched by catalytic particle ink applications commonly used in the manufacture of the catalyst layer. This in-situ work is promising and original, establishing a potential new method of producing membrane electrode assemblies (MEAs) in PEM fuel cell manufacturing.This new graphene nanomaterial could also pave the way for its potential use in supercapacitors, solar cells, biosensors, batteries, fuel storage, field-effect transistors, filtration and electrochemical devices, in addition to the fuel cell catalysis applications under study.
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) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,000 | 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 ».