EDX Elemental Mapping of Trace Amounts of Ir on the Surface of Pt Cubic Nanoparticles for Ammonia Electro-Oxidation
Notice bibliographique
Résumé
Advances in the field of nanomaterials necessitate corresponding advancements in elemental analysis. Energy dispersive spectroscopy (EDS) performed in scanning electron microscopes is a critical technique that enables determination of spatial compositional distributions. Elements such as Pt and Ir find frequent application in fuel cell technology [1], and understanding their functioning requires knowledge of their elemental spatial distribution. However, when these elements are used together, with trace amounts of one, element mapping is exceptionally challenging with EDS. This is due to their dominant M emission peaks being only 70 eV apart, whereas the typical best energy resolution for EDS is 120 eV. Ir and Pt L-edges have a greater separation of nearer 270 eV, but their intensities have only 10% of the M-edges. To minimize the electron dose received by the sample and the acquisition time, we show that it is possible to distinguish Pt-Ir concentrations by analyzing the base regions of the combined Pt/Ir Mα-β peaks, along with observing the presence of much weaker signature of the M3N4 peak in the 2.2 to 2.3 keV range. From our calibration data, this peak appears stronger in Ir than in Pt. These differences appear to not be reliably determined using common EDS data analysis software [2]. We show this by creating synthetic EDS spectra composed from calibration spectra from isolated Pt and Ir samples in the same scanning electron microscope, at the same beam conditions, as in Figure 1(a). Comparing these synthetic model spectra to peak normalized spectra collected from the inner-most and outer-most regions of Pt/Ir nanoparticles (Figure 1(c)), shows that the Ir concentration increases towards the outermost edges Figure 1(b). The presence of Ir is further corroborated by electrochemical studies on the ammonia electro-oxidation reaction (AOR), results of which are shown in Figure 2. These measurements reveal characteristic properties of a Pt-Ir alloy, such as a negative shift in overpotential and increased resilience to poisoning during the AOR, even with small quantities of Ir deposited on the surface of cubic Pt(100) nanoparticles [3, 4]. Having access to information on the spatial distribution of Ir on and within Pt by this relatively simple analysis technique, provides access to important information to further optimize the process of creating Pt/Ir nanoparticles and understand their surface chemistry. (a) Synthetic peak normalized EDS spectra in the region of Mα/β peak for Pt-Ir of various concentrations, created by summing calibration spectra. (b) Peak normalized sum spectra from the inner-most (‘core’) and outermost regions (‘shell’). (c) BF-STEM image collected at 20 kV in the SU9000 SEM/STEM of a collection of Pt-Ir nanoparticles, overlaid with color masks indicating the inner- most and outer- most regions used to produce the sum spectra shown in part (b). Electrochemical measurements illustrating the distinction between Pt nanoparticles and Pt-Ir nanoparticles. Left: cyclic voltammetry in 1.0 NH4, 1.0 M NaOH, scan rate of 50 mV/s. Right: potentiostatic measurements at 0.25 V vs SCE, 1.0 M NH4, 1.0 M NaOH.
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 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 | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».