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Enregistrement W4412909153 · doi:10.1093/mam/ozaf048.205

In Situ Scanning Electron Microscopy Observation of Metal Nanoparticles during Heating

2025· article· en· W4412909153 sur OpenAlexaff
Maryam Golozar, Dian Yu, M. Gabriel, Stas Dogel, Hooman Hosseinkhannazer, Vladimir Kitaev, Jane Y. Howe

Notice bibliographique

RevueMicroscopy and Microanalysis · 2025
Typearticle
Langueen
DomaineMaterials Science
ThématiqueGold and Silver Nanoparticles Synthesis and Applications
Établissements canadiensNorcada (Canada)Wilfrid Laurier UniversityUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésIn situMaterials scienceScanning electron microscopeNanoparticleMetalElectron microscopeNanotechnologyMicroscopyOpticsChemistryMetallurgyComposite materialPhysicsOrganic chemistry

Résumé

récupéré en direct d'OpenAlex

Metal nanoparticles offer promising applications in facet-selective catalysis and surface plasmon resonance (SPR) [1-3]. Among these nanoparticles, silver nanoparticles have shown great potential due to their excellent optoelectronic properties and the ability to control their shape. However, the chemical stability of silver nanoparticles raises concerns. One approach to improve their chemical stability is to coat the nanoparticles with gold [1, 4]. However, the stability of these gold-coated nanoparticles and their microstructural behavior including facet preservation during heating still needs to be studied for high temperature applications. To monitor the effects of increasing temperature, in situ scanning electron microscopy (SEM) is an effective technique. In situ SEM allows for the observation of morphological change in the nanoparticles during heating, which provides insights into how their synthesis can be altered to enhance their properties. In this work, a Hitachi SU7000 SEM was used to investigate the microstructural evolution of nanoparticles during heating. The SEM heating holder was developed by Hitachi, and the heating chip was manufactured and optimized by Norcada Inc. (Edmonton Canada). For the initial experiments, gold-plated pentagonal silver nanorods were used. These nanoparticles were dropcast onto the heating chip, and images of various particles were captured at elevated temperatures during the heating process. Figure 1 presents the heating profile of the nanoparticles including the specific temperatures at which images were captured. Figure 2 shows SEM images of a cluster of four particles obtained with the upper detector (UD). Images were recorded at an accelerating voltage of 8 kV to minimize the effect of beam damage, prevent charging, and capture the surface structures. At room temperature (Figures 2A and 3A), the facets of the nano particles are clearly visible. As the temperature reaches 200 °C (Figure 2B), these facets begin to fade, and with further heating, the particles start to consolidate. At around 600 °C (Figures 2C and 3B), new lines appear on the particles, possibly cracks in the gold shell, which could lead to the sublimation of the silver inside. This behavior is observed around 600-700 °C, which coincides with the volatilization temperature of silver under low pressure, which is 680 °C [5]. Videos obtained during the heating process further illustrate the evolution of these nanoparticles. Figure 3 shows the behavior of two nanoparticles adjacent to each other during heating. At room temperature (Figure 3A), the facets are clearly visible. As the temperature increases, the facets gradually disappear and by 600 °C (Figure 3B), the nanoparticles begin to consolidate and merge together. At higher temperatures (Figure 3C), when only a shell of the nanoparticles remains, the merging of the particles becomes more apparent. The in situ SEM heating set-up used in this study proves to be an excellent method for investigating these nanoparticles. Further research will focus on gold-plated silver nanoparticles with silicate shells and nanoparticles of different geometries to determine the optimal strategies for further stabilizing silver nanoparticles at high temperature. Additionally, the study will monitor the morphological changes at different heating rates and profiles [6]. Heating profile of the nanoparticles from room temperature (RT) until 1000 °C. The red dots show the temperatures at which the SEM images in Figure 2 were captured at. SEM images of a cluster of nanoparticles at temperatures marked with red dots in Figure 1. Scale bar 100 nm. SEM images of two nanoparticles (A) coalesced and merged (B), and sublimed (C) during heating. Scale bar 100 nm.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,011
Tête enseignante GPT0,275
Écart entre enseignants0,264 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentnon

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