Modelling of pulsed electron beam induced graphite ablation: Sublimation versus melting
Bibliographic record
Abstract
Pulsed electron beam ablation (PEBA) has recently emerged as a very promising technique for the deposition of thin films with superior properties. Interaction of the pulsed electron beam with the target material is a complex process, which consists of heating, phase transition, and erosion of a small portion from the target surface. Ablation can be significantly affected by the nature of thermal phenomena taking place at the target surface, with subsequent bearing on the properties, stoichiometry and structure of deposited thin films. A two stage, one-dimensional heat conduction model is presented to describe two different thermal phenomena accounting for interaction of a graphite target with a polyenergetic electron beam. In the first instance, the thermal phenomena are comprised of heating, melting and vaporization of the target surface, while in the second instance the thermal phenomena are described in terms of heating and sublimation of the graphite surface. In this work, the electron beam delivers intense electron pulses of ∼100 ns with energies up to 16 keV and an electric current of ∼400 A to a graphite target. The temperature distribution, surface recession velocity, ablated mass per unit area, and ablation depth for the graphite target are numerically simulated by the finite element method for each case. Based on calculation findings and available experimental data, ablation appears to occur mainly in the regime of melting and vaporization from the surface.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".