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Record W4280637985 · doi:10.1093/jmicro/dfac022

Spatial resolution in secondary-electron microscopy

2022· article· en· W4280637985 on OpenAlexafffund
R.F. Egerton, Yimei Zhu

Bibliographic record

VenueMicroscopy · 2022
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaOffice of ScienceU.S. Department of Energy
KeywordsScanning transmission electron microscopyResolution (logic)Scanning electron microscopeSecondary electronsScanning confocal electron microscopyElectronImage resolutionConventional transmission electron microscopeOpticsElectron microscopeMaterials scienceHigh-resolution transmission electron microscopyElectron tomographyEnergy filtered transmission electron microscopyTransmission electron microscopyLow-voltage electron microscopeMicroscopyPhysicsNuclear physicsComputer science

Abstract

fetched live from OpenAlex

We first review the significance of resolution and contrast in electron microscopy and the effect of the electron optics on these two quantities. We then outline the physics of the generation of secondary electrons (SEs) and their transport and emission from the surface of a specimen. Contrast and resolution are discussed for different kinds of SE imaging in scanning electron microscope (SEM) and scanning-transmission microscope instruments, with some emphasis on the observation of individual atoms and atomic columns in a thin specimen. The possibility of achieving atomic resolution from a bulk specimen at SEM energies is also considered.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.284
Teacher spread0.277 · 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

Citations18
Published2022
Admission routes2
Has abstractyes

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