Comparison of Kikuchi diffraction geometries in the scanning electron microscope
Bibliographic record
Abstract
Recent advances in scanning electron microscope (SEM) based Kikuchi diffraction have demonstrated the important potential for transmission and reflection methods, like transmission Kikuchi diffraction (TKD) and electron backscatter diffraction (EBSD). Furthermore, with the advent of compact direct electron detectors (DED) it has been possible to place the detector in a variety of configurations within the SEM chamber. This motivates the present work where we explore the similarities and differences of the different geometries that include on-axis TKD & off-axis TKD using electron transparent samples, as well as more conventional EBSD. Furthermore, we compare these with the newest method called “reflection Kikuchi diffraction” RKD where the sample is placed flat in the chamber and the detector is placed below the pole piece. Through remapping collected diffraction patterns , all these methods can be used to generate an experimental “diffraction sphere” that can be used to explore diffraction from any scattering vector from the unit cell, as well as the ability to perform band profile analysis. This diffraction sphere approach enables us to further probe specific differences between the methods, including for example thickness effects in TKD that can result in the generation of diffraction spots, as well as electron scattering path length effects that result in excess and deficiency variations, as well as inversion of bands in experimental patterns.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".