High resolution <scp>STEM</scp> and <scp>EELS</scp> investigation of <scp>N</scp> ‐doped carbon allotropes decorated with noble metal atom catalysts
Notice bibliographique
Résumé
Graphene's unique properties make this material an ideal catalyst support for use in the proton exchange membrane fuel cell (PEMFC). Graphene offers increased electrical conductivity and a larger surface area for catalyst deposition compared to other catalyst support material, such as carbon black.[1,2]. Utilizing graphene as the electrode support also results in increased chemical stability due to the sp 2 bonding; however, this precludes the availability of dangling bonds for chemisorption, thus leading to a poor Pt distribution and the formation of large nanoparticles (NPs). Functionalization can be used to introduce nucleation sites into the graphene lattice, where N‐dopants have been shown to increase the Pt‐C binding energy.[3] The atomic layer deposition (ALD) technique creates ultra‐small NPs (<1 nm) which, when combined with the enhanced catalyst binding energy from the N‐doped graphene support can produce stable Pt clusters/atoms, resulting in increased Pt utilization while subsequently reducing the cost.[4] To fully understand and design a more efficient PEMFC the material must be characterized at the atomic level. This can be accomplished by the use of aberration‐corrected transmission electron microscopy (TEM). High resolution TEM (HRTEM) can be utilized to observe the structure of the graphene lattice, while high‐angle annular dark‐field (HAADF) scanning transmission electron microscopy (STEM) can be used to examine the Pt clusters' size and distribution. Furthermore, electron energy loss spectroscopy (EELS) can be utilized to examine local chemical composition and bonding of the probed atoms from nanometer scaled areas in order to reveal the coordination of the N‐dopant species in the graphene lattice.[4] Here we used a FEI Titan 80‐300 Cubed TEM equipped with aberration correctors of the probe and imaging forming lens, and a monochromator for optimal imaging at a low accelerating voltage. Low‐energy condition HRTEM (figure 1) and STEM imaging were used to investigate thermally‐exfoliated graphene (figure 2). From these observations, we deduced that the graphene lattice maintained its short range order; however, the long range order was lost due to the presence of steps, folds, defects, and incomplete exfoliation.[4] The mass‐thickness dependence in HAADF imaging confirmed the presence of numerous steps and ledges in the N‐doped graphene nanosheets. More importantly the Z‐contrast in the HAADF images revealed that Pt was present as stable single atoms and clusters on the N‐doped graphene and that Pt NPs were not detected.[4] Lastly, using EELS and the N‐K edge fine structures, we probed the distribution of N‐dopants within the nanosheets (figure 3). Significant variations in the local concentration of N‐dopants were observed among the graphene sheets, in which an inhomogeneous distribution was discovered. Such effects have not been observed in previous broad beam techniques.[4] To futher investigate the effect of N‐doping, mutliwalled N‐doped CNTs (N‐CNTs) were produced and ALD was utilized to deposit Pd. Initial investigations showed the stabilization of single Pd atoms with N‐doping; however, small NPs were also formed (figure 4). It was demonstrated with EELS that many of the N‐CNTs were filled with N 2 gas. Using controlled evacuation of the multiwalled CNTs induced by the electron beam, we were able to reveal the intrinsic N‐type doping within the CNT walls.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».