Electron backscatter diffraction applied to lithium sheets prepared by broad ion beam milling
Bibliographic record
Abstract
Due to its very low hardness and atomic number, pure lithium cannot be prepared by conventional methods prior to scanning electron microscopy analysis. Here, we report on the characterization of pure lithium metallic sheets used as base electrodes in the lithium-ion battery technology using electron backscatter diffraction (EBSD) and X-ray microanalysis using energy dispersive spectroscopy (EDS) after the sheet surface was polished by broad argon ion milling (IM). No grinding and polishing were necessary to achieve the sufficiently damage free necessary for surface analysis. Based on EDS results the impurities could be characterized and EBSD revealed the microsctructure and microtexture of this material with accuracy. The beam damage and oxidation/hydration resulting from the intensive use of IM and the transfer of the sample into the microscope chamber was estimated to be <50 nm. Despite the fact that the IM process generates an increase of temperature at the specimen surface, it was assumed that the milling parameters were sufficient to minimize the heating effect on the surface temperature. However, a cryo-stage should be used if available during milling to guaranty a heating artefact free surface after the milling process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".