Monte Carlo study of electron-beam penetration and backscattering in multi-walled carbon nanotube materials: The effect of different scattering models
Bibliographic record
Abstract
A systematic study of electron-beam penetration and backscattering in multi-walled carbon nanotube (MWCNT) materials for beam energies of ∼0.3 to 30 keV is presented based on event-by-event Monte Carlo simulation of electron trajectories using state-of-the-art scattering cross sections. The importance of different analytic approximations for computing the elastic and inelastic electron-scattering cross sections for MWCNTs is emphasized. We offer a simple parameterization for the total and differential elastic-scattering Mott cross section, using appropriate modifications to the Browning formula and the Thomas-Fermi screening parameter. A discrete-energy-loss approach to inelastic scattering based on dielectric theory is adopted using different descriptions of the differential cross section. The sensitivity of electron penetration and backscattering parameters to the underlying scattering models is examined. Our simulations confirm the recent experimental backscattering data on MWCNT forests and, in particular, the steep increase of the backscattering yield at sub-keV energies as well as the sidewalls escape effect at high-beam energies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".