(Invited) Electrochemical Methods for Surface Composition Determination of Alloy and Core/Shell Nanoparticles
Notice bibliographique
Résumé
The surface characteristics of metallic nanoparticles (NPs), especially the atomic percent and distribution of each component, is critical to their catalytic and electrocatalytic behavior. Therefore, efforts are underway to design NP catalysts with full control of their composition, both in the bulk and at the surface. In our work, Pt-Ru and Pt-Ir NPs, either in alloy or core@shell forms, are of great interest, due to their suitability for a wide range of electrocatalytic and sensing applications, such as for the oxidation of formic acid, alcohols, and ammonia, for oxygen evolution and reduction in regenerative fuel cells, in the reduction of hydrogen peroxide, and as an electron mediating matrix in glucose biosensors 1–8. In the present work, PtxIry alloys, Ircore@Ptshell, Rucore@Ptshell, and Rucore@Pt-Irshell NPs were synthesized using the simple and controllable polyol method 9. In the case of PtxIry alloy NPs, two sequential heating steps were employed to minimize the possibility of surface enrichment of Pt or Ir during NP formation. In the case of Ircore@Ptshell and Rucore@Ptshell NPs, the Ptshell, with a coverage of between 0.1 and 2 monolayers (MLs), was controllably deposited on the surface of the Ircore and Rucore NPs, while for the Rucore@Pt-Irshell NPs, one ML of the PtxIry alloy shell, containing different Pt:Ir ratios, was deposited on the Rucore. Wavelength dispersive X-ray spectroscopy (WDS) and energy dispersive X-ray spectroscopy, coupled with high-resolution transmission electron microscopy (EDS/HRTEM) and powder X-ray diffraction (PXRD), were then used to determine the bulk composition and homogeneity of the NPs. To determine the outer surface composition of these NPs, the underpotential deposition/stripping of Cu and oxalic acid oxidation have been used previously, e.g., for Pt-Ru NPs 10–12. However, the effect of NP size, Pt-Ru interactions at the surface, and the degree of Ru oxidation on the catalytic activity have not been determined. In other prior work 13, CO stripping was used to establish the stability of PtRu NPs by tracking the changes in the surface composition. In the present work, several electrochemical fingerprinting methods were developed (underpotential deposition/removal of H atoms, CO stripping, and surface oxide reduction) to determine the precise coverage and thickness (fraction of MLs) of the Ptshell on the Ircore@Ptshell and Rucore@Ptshell, NPs and the Pt:Ir ratio at the surface of the PtxIry alloy and Rucore/Pt-Irshell NPs, as well as the real surface area of the exposed metals. Figure 1 1,14,15 shows the CO stripping voltammetry of the NPs under study here as an example of how the peak potential and splitting correlate with the NP surface composition. A comparison will be given between the surface areas and compositions obtained by each of the electrochemical methods used here, as well as with what can be inferred from TEM imaging methods. References: E. N. El Sawy, H. T. Handal, V. Thangadurai, and V. I. Birss, J. Mater. Chem. A, 4, 15400–15410 (2016) E. N. El Sawy and P. G. Pickup, Electrocatalysis, 7, 1–9 (2016). E. N. E. N. El Sawy, H. A. H. A. El-Sayed, and V. I. V. I. Birss, Phys. Chem. Chem. Phys., 17, 27509–27519 (2015) A. Allagui et al., Int. J. Hydrogen Energy, 38, 2455–2463 (2013). M. Zeng, X. X. Wang, Z. H. Tan, X. X. Huang, and J. N. Wang, J. Power Sources, 264, 272–281 (2014) E. Antolini, Acs Catal., 4, 1426–1440 (2014) P. Holt-Hindle, S. Nigro, M. Asmussen, and A. Chen, Electrochem. commun., 10, 1438–1441 (2008) A. S. Jhas, H. Elzanowska, B. Sebastian, and V. Birss, Electrochim. Acta, 55, 7683–7689 (2010) H. Bonnemann and K. S. Nagabhushana, in Metal Nanoclusters in Catalysis and Materials Science,, p. 21–48, Elsevier, Amsterdam (2008) C. L. Green and A. Kucernak, J. Phys. Chem. B, 106, 1036–1047 (2002) C. N. Van Huong and M. J. Gonzalez-Tejera, J. Electroanal. Chem. Interfacial Electrochem., 244, 249–259 (1988) C. Bock and B. MacDougall, J. Electrochem. Soc., 150, E377–E383 (2003) P. Ochal et al., J. Electroanal. Chem., 655, 140–146 (2011) E. N. El Sawy, H. a El-Sayed, and V. I. Birss, Chem. Commun., 50, 11558–11561 (2014) E. N. El Sawy, thesis, University of Calgary, Canada (2013). Figure 1
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| 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 | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,009 |
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 ».