Sample Preparation Method for Characterization of Fine Solids in Athabasca Oil Sands by Electron Microscopy
Bibliographic record
Abstract
This study introduces a new sample preparation method for characterization of fine solids in Athabasca oil sands using electron microscopy. The method uses a combination of microtome and focused ion beam (FIB) techniques to produce samples for both scanning and transmission electron microscopy (SEM/TEM). In this procedure, SEM images and X-ray maps provide general microstructure and compositional information, with detailed analysis provided through TEM analysis of site-specific electron transparent sections prepared using FIB methods. The method is particularly useful for identifying low concentrations of metals and their compounds and is demonstrated for the identification of fine iron oxide particles in the froth stream of an Athabasca oil sands sample after hot-water extraction. Fine particles (<2 μm fraction of oil sands minerals) are embedded in a polymer resin and sectioned using an ultramicrotome to prepare a fresh, flat surface for SEM analysis. An iron-rich region is located using X-ray mapping in the SEM. A thin section of the region of interest is prepared using FIB, which is then characterized using TEM imaging, electron diffraction, and X-ray microanalysis. Specifically, nanosized hematite and magnetite particles were identified in the froth stream.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".