Acquisition parameters optimization of a transmission electron forward scatter diffraction system in a cold‐field emission scanning electron microscope for nanomaterials characterization
Bibliographic record
Abstract
Transmission electron forward scatter diffraction (t-EFSD) is a new technique providing crystallographic information with high resolution on thin specimens by using a conventional electron backscatter diffraction (EBSD) system in a scanning electron microscope. In this study, the impact of tilt angle, working distance, and detector distance on the Kikuchi pattern quality were investigated in a cold-field emission scanning electron microscope (CFE-SEM). We demonstrated that t-EFSD is applicable for tilt angles ranging from -20° to -40°. Working distance (WD) should be optimized for each material by choosing the WD for which the EBSD camera screen illumination is the highest, as the number of detected electrons on the screen is directly dependent on the scattering angle. To take advantage of the best performances of the CFE-SEM, the EBSD camera should be close to the sample and oriented towards the bottom to increase forward scattered electron collection efficiency. However, specimen chamber cluttering and beam/mechanical drift are important limitations in the CFE-SEM used in this work. Finally, the importance of t-EFSD in materials science characterization was illustrated through three examples of phase identification and orientation mapping.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".