Detailed Monte Carlo Simulation of electron transport and electron energy loss spectra
Bibliographic record
Abstract
A computer program for detailed Monte Carlo simulation of the transport of electrons with kinetic energies in the range between about 0.1 and about 500 keV in bulk materials and in thin solid films is presented. Elastic scattering is described from differential cross sections calculated by the relativistic (Dirac) partial-wave expansion method with different models of the scattering potential. Inelastic interactions are simulated from an optical-data model based on an empirical optical oscillator strength that combines optical functions of the solid with atomic photoelectric data. The generalized oscillator strength is built from the adopted optical oscillator strength by using an extension algorithm derived from Lindhard's dielectric function for a free-electron gas. It is shown that simulated backscattering fractions of electron beams from bulk (semi-infinite) specimens are in good agreement with experimental data for beam energies from 0.1 keV up to about 100 keV. Simulations also yield transmitted and backscattered fractions of electron beams on thin solid films that agree closely with measurements for different film thicknesses and incidence angles. Simulated most probable deflection angles and depth-dose distributions also agree satisfactorily with measurements. Finally, electron energy loss spectra of several elemental solids are simulated and the effects of the beam energy and the foil thickness on the signal to background and signal to noise ratios are investigated. SCANNING 38:475-491, 2016. © 2015 Wiley Periodicals, Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".