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Record W4401000410 · doi:10.1093/mam/ozae044.323

Low Voltage Scanning Transmission Electron Microscopy as a Viable Tool for Routine Analysis of Materials Science Specimens

2024· article· en· W4401000410 on OpenAlexaff
Nicolas Brodusch, Raynald Gauvin

Bibliographic record

VenueMicroscopy and Microanalysis · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceScanning transmission electron microscopyTransmission electron microscopyScanning confocal electron microscopyElectronTransmission (telecommunications)Energy filtered transmission electron microscopyScanning electron microscopeVoltageMicroscopyConventional transmission electron microscopeOptoelectronicsAnalytical Chemistry (journal)NanotechnologyOpticsChemistryComposite materialElectrical engineeringPhysicsEngineeringEnvironmental chemistryNuclear physics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.301
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractno

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