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Record W4220661877 · doi:10.1017/s1431927622000472

High-Energy Electron Scattering in <i>Thick</i> Samples Evaluated by Bright-Field Transmission Electron Microscopy, Energy-Filtering Transmission Electron Microscopy, and Electron Tomography

2022· article· en· W4220661877 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicroscopy and Microanalysis · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsUniversity of AlbertaNational Institute for Nanotechnology
Fundersnot available
KeywordsElectron tomographyElectronTransmission electron microscopyCathode rayScanning transmission electron microscopyConventional transmission electron microscopeIntensity (physics)Mean free pathScatteringEnergy filtered transmission electron microscopy

Abstract

fetched live from OpenAlex

Energy-filtering transmission electron microscopy (TEM) and bright-field TEM can be used to extract local sample thickness $t$ and to generate two-dimensional sample thickness maps. Electron tomography can be used to accurately verify the local $t$. The relations of log-ratio of zero-loss filtered energy-filtering TEM beam intensity ($I_{{\rm ZLP}}$) and unfiltered beam intensity ($I_{\rm u}$) versus sample thickness $t$ were measured for five values of collection angle in a microscope equipped with an energy filter. Furthermore, log-ratio of the incident (primary) beam intensity ($I_{\rm p}$) and the transmitted beam $I_{{\rm tr}}$ versus $t$ in bright-field TEM was measured utilizing a camera before the energy filter. The measurements were performed on a multilayer sample containing eight materials and thickness $t$ up to 800 nm. Local thickness $t$ was verified by electron tomography. The following results are reported:• The maximum thickness $t_{{\rm max}}$ yielding a linear relation of log-ratio, $\ln ( {I_{\rm u}}/{I_{{\rm ZLP}}})$ and $\ln ( {I_{\rm p}}/{I_{{\rm tr}}} )$, versus $t$.• Inelastic mean free path ($\lambda _{{\rm in}}$) for five values of collection angle.• Total mean free path ($\lambda _{{\rm total}}$) of electrons excluded by an angle-limiting aperture.• $\lambda _{{\rm in}}$ and $\lambda _{{\rm total}}$ are evaluated for the eight materials with atomic number from $\approx$10 to 79.The results can be utilized as a guide for upper limit of $t$ evaluation in energy-filtering TEM and bright-field TEM and for optimizing electron tomography experiments.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.170
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.281
Teacher spread0.276 · 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