Ionic liquid‐based observation technique for nonconductive materials in the scanning electron microscope: Application to the characterization of a rare earth ore
Bibliographic record
Abstract
A new approach for preparing geological materials is proposed to reduce charging during their characterization in a scanning electron microscope. This technique was applied to a sample of the Nechalacho rare earth deposit, which contains a significant amount of the minerals fergusonite and zircon. Instead of covering the specimen surface with a conductive coating, the sample was immersed in a dilute solution of ionic liquid and then air dried prior to SEM analysis. Imaging at a wide range of accelerating voltages was then possible without evidence of charging when using the in-chamber secondary and backscattered electrons detectors, even at 1 kV. High resolution x-ray and electron backscatter diffraction mapping were successfully obtained at 20 and 5 kV with negligible image drifting and permitted the characterization of the microstructure of the zircon/fergusonite-Y aggregates encased in the matrix minerals. Because of the absence of a conductive layer at the surface of the specimen, the Kikuchi band contrast was improved and the backscatter electron signal increased at both 5 and 20 kV as confirmed by Monte Carlo modeling. These major developments led to an improvement of the spatial resolution and efficiency of the above characterization techniques applied to the rare earth ore and it is expected that they can be applied to other types of ores and minerals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".