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Record W1974538175 · doi:10.1017/s143192761201392x

The Influence of the Sample Thickness on the Lateral and Axial Resolution of Aberration-Corrected Scanning Transmission Electron Microscopy

2013· article· en· W1974538175 on OpenAlexaff
Ranjan Ramachandra, Hendrix Demers, Niels de Jonge

Bibliographic record

VenueMicroscopy and Microanalysis · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsUniversité de Sherbrooke
FundersNational Institute of General Medical Sciences
KeywordsResolution (logic)Materials scienceTransmission electron microscopySilicon nitrideScanning electron microscopeOpticsScanning transmission electron microscopyCathode rayMembraneHigh-resolution transmission electron microscopySiliconMicroscopyImage resolutionScanning confocal electron microscopyElectronNanotechnologyChemistryOptoelectronicsPhysicsComposite material

Abstract

fetched live from OpenAlex

The lateral and axial resolution of three-dimensional (3D) focal series aberration-corrected scanning transmission electron microscopy was studied for samples of different thicknesses. The samples consisted of gold nanoparticles placed on the top and at the bottom of silicon nitride membranes of thickness between 50 and 500 nm. Atomic resolution was obtained for nanoparticles on top of 50-, 100-, and 200-nm-thick membranes with respect to the electron beam traveling downward. Atomic resolution was also achieved for nanoparticles placed below 50-, 100-, and 200-nm-thick membranes but with a lower contrast at the larger thicknesses. Beam broadening led to a reduced resolution for a 500-nm-thick membrane. The influence of the beam broadening on the axial resolution was also studied using Monte Carlo simulations with a 3D sample geometry.

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)0.004
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: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.004
GPT teacher head0.273
Teacher spread0.269 · 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
GenreMethods

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

Citations9
Published2013
Admission routes1
Has abstractyes

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