Ultra small angle x-ray scattering in complex mixtures of triacylglycerols
Bibliographic record
Abstract
Ultra-small angle x-ray scattering (USAXS) has been used to elucidate, in situ, the aggregation structure of unsheared model edible oils. Each system comprised one or two solid lipids and a combination of liquid lipids. The 3D nano- to micro-structures of each system were characterized. The length scale investigated, using the Bonse-Hart camera at beamline ID-15D at the Advanced Photon Source, ANL, ranged from 300 Å-10 µm. Using the Unified Fit model, level-1 analysis showed that the scatterers were 2D objects with either a smooth, a rough, or a diffuse surface. These 2D objects had an average radius of gyration Rg1 between 200-1500 Å. Level-2 analysis displayed a slope between -1 and -2. Use of the Guinier-Porod model gave s ≈ 1 thus showing that it was cylinders (TAGwoods) aggregating with fractal dimension 1 ≤ D2 ≤ 2. D2 = 1 is consistent with 1D structures formed from TAGwoods, while D2 = 2 implies that the TAGwoods had formed structures characteristic of diffusion or reaction limited cluster-cluster aggregation (DLCA/RLCA). These aggregates exhibited radii of gyration, Rg2, between 2500 and 6500 Å. Level-3 analyses showed diffuse surfaces, for most of the systems. These interpretations are in accord with theoretical models which studied crystalline nano-platelets (CNPs) coated with nano-scale layers arising from phase separation at the CNP surfaces. These layers could be due to either liquid-liquid phase separation with the CNPs coated, uniformly or non-uniformly, by a diffuse layer of TAGs, or solid-liquid phase separation with the CNPs coated by a rough layer of crystallites.A fundamental understanding of the self-organizing structures arising in these systems helps advance the characterization of fat crystal networks from nanometres to micrometres. This research can be used to design novel fat structures that use healthier fats via nano- and meso-scale structural engineering.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".