A Novel Cryo‐SEM Technique for Imaging Vegetable Oil Based Organogels
Bibliographic record
Abstract
Abstract Gels were prepared by cooling dilute solutions (2% wt/wt) of 12‐hydroxystearic acid (12‐HSA) in canola oil and storing them at 30 °C for 24 h. The gel's in‐situ supramolecular network structure was imaged using four techniques: polarized light microscopy (PLM), 3‐dimensional deconvolution polarized light microscopy (3DPLM), and cryo‐scanning electron microscopy (cryo‐SEM) of the xerogel and of an osmium tetroxide vapor fixed gel washed with isobutanol. Most of the canola oil was immobilized in the gel by fixation with osmium tetroxide therefore very little of the canola oil was removed during washing unlike the xerogel where all of the canola oil has been displaced. The in‐situ supramolecular network structure as observed by PLM, was comparable to that seen through the new cryo‐SEM method for fixed organogel. Cryo‐SEM images of the xerogel did not show similar length scales or strand thickness as compared to the PLM images. The lengths of the network strands were much shorter for the xerogel as compared to the osmium tetroxide treated sample and the structures visualized by PLM. Furthermore, the thickness of the strands observed using PLM or cryo‐SEM were in the size range of 3–10 μm while the xerogels had strands in the range of 0.01–0.1 μm thick. Therefore, the removal of canola oil from the gel using 80/20% v/v hexane/acetone with no fixation disrupted the supramolecular network.
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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 | 0.001 |
| 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".