Low Voltage Scanning Transmission Electron Microscopy as a Viable Tool for Routine Analysis of Materials Science Specimens
Notice bibliographique
Résumé
Over the last two decades, scanning transmission electron microscopy (STEM) with lower accelerating voltages compared to the more conventional voltages (200, 300 kV) was explored. Since these low voltages were compatible with the scanning electron microscope (SEM) range, these instruments were naturally selected as potential providers of transmission capabilities at lower costs [1-3]. In addition to these financial considerations, it has been demonstrated that working at voltages between 20 and 30 kV allowed to produce sufficiently small probes, but at the same time, to significantly increase the scattering in the sample, thus generating images with higher contrasts compared to higher accelerating voltages. The advantage of LV-STEM was recognized quickly and led to the development of commercial systems, and more recently, the Hitachi SU-9000EA [4]. Monte Carlo modelling has proven to be a useful support to develop LV-STEM-in-SEM. Figure 1A shows the transmission coefficient ηTrs as a function of the accelerating voltage (E0) for carbon, iron, and gold thin films of 20 and 80 nm. It shows clearly that most of the materials at these thicknesses will provide transmission larger than 90 %, except for very high atomic number (Z) materials like gold. An example of bright-field imaging of a plasmonic magnesium nanoparticle given in Figure 1B shows a high-resolution lattice contrast with 0.24 nm spot identified in the image FFT (inset), confirming the imaging capabilities of a 30 kV STEM. At the same time, the beam broadening subsequent to the increased scattering inside the sample is kept low for medium to low Z materials. We typically measured 1.9 nm with Cu Ka line versus 1.6 nm in bright-field mode from a line profile across a T1 precipitate (Al2CuLi) in an 80 nm thick Al-Li-Cu alloy specimen [5]. The combination of a small probe size with increased interactions with the specimen gives STEM-in-SEM a greater efficiency in mapping specimen composition. Figure 2A shows an EDS map recorded from a carbon nanotube (CNT) covered with TiO2 nanoparticles where the distribution of those particles as well as the iron seeds inside the CNT are very well resolved with high contrast. Similarly, this increase in scattering allows to record high quality elemental maps of lithium in a AA2099 Al-Cu-Li alloy (Figure 2C) using the 3-window method (Figure 2B). In this presentation, we will show how low voltage STEM with a SEM can provide realistic information about the composition, chemistry, and crystal structure of material science specimens. Various examples of low voltage high-resolution imaging and spectroscopy will be given, including EDS, CBED/4DSTEM and EELS. (A) Transmission coefficients (ηTrs) as a function of beam accelerating voltage (E0) for C, Fe and Au films of 20 and 80 nm, (B) High-resolution lattice image of a Mg nanoparticle at E0 = 30 kV showing 0.24 nm diffraction spots in inset. Dotted line in (A) highlights E0 = 30 kV. (A) EDS map of a CNT covered with TiO2 nanoparticles with the corresponding SE image, (B) Principle of the 3-windows method with Li EELS spectrum on SU-9000EA and (C) HAADF image and elemental & jump ratio maps from an AA2099-T8 condition alloy showing the lithium-based precipitates. Note that no smoothing was applied to the EDS map in A.
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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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,002 |
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 ».