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Record W3039510723 · doi:10.1017/s1431927600036217

The Characterization of Nano Materials in the FE-SEM

2000· article· en· W3039510723 on OpenAlexaff
Raynald Gauvin, Paula Horny

Bibliographic record

VenueMicroscopy and Microanalysis · 2000
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsScanning electron microscopeScanning transmission electron microscopyMaterials scienceDetectorResolution (logic)Secondary electronsOpticsConventional transmission electron microscopeCharacterization (materials science)Nano-Field emission microscopyField electron emissionElectronLens (geology)Field emission gunTransmission electron microscopyHigh-resolution transmission electron microscopyEnvironmental scanning electron microscopeBrightnessMicroscopeNanotechnologyPhysicsDiffractionComputer scienceComposite materialNuclear physics

Abstract

fetched live from OpenAlex

Abstract The observation of nano materials or nano phases is generally performed using Transmission Electron Microscopy (TEM) because conventional Scanning Electron Microscopes (SEM) do not have the resolution to image such small phases. Since the last decade, a new generation of microscopes is available on the market. These are the Field Emission Scanning Electron Microscope (FE-SEM) with a virtual secondary electron detector. The FE-SEM have a higher brightness allowing probe diameter smaller than 2.5 nm with incident electron energy, E0, below 5 keV. Furthermore, what gives FE-SEM outstanding resolution is the virtual secondary electron (SE) detector. The virtual SE detector is located above the objective lens and it is also named a through-the-lens (TTL) detector. Therefore, the SE images are mostly made up of all SE of type I and II, because those of type III, which are generated by the backscattered electrons in the chamber, are not collected.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.255
Teacher spread0.249 · 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 teacher head, 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

Citations0
Published2000
Admission routes1
Has abstractyes

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