Water-induced self-assembly of mixed gelator system (ceramide and lecithin) for edible oil structuring
Why this work is in the frame
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Bibliographic record
Abstract
Ceramide (CER) is a highly effective organogelator with numerous health implications. Lecithin (LEC) is often used as a crystal modifier to improve gel properties. It was found in this study that in the presence of a small amount of water, CER and LEC self-assembled at specific ratios and form oleogels in sunflower oil (SFO), and the structure of which differed from any of the mono-component gel structures. To the best of our knowledge, this is the first water-induced multi-component oleogelation reported to date. To clarify when gelation occurs, a phase diagram of the four-component system SFO/CER/LEC/water was established and characterized. In the gel region, confocal laser scanning microscopy revealed that water was evenly distributed in the self-assembled microstructure. To clarify the vital role of water in formation of stable gel, a series of samples with varying ratios in CER and LEC with and without water were explored. The thermal properties, viscoelastic properties, microstructures, as well as crystal packing were investigated. With water added, the microscopic structures of CER/LEC gels changed from three-dimensional dendritic crystals to two-dimensional rod-like or platelet-like crystals, which are more effective at structuring liquid oil. A different reflection was observed (d-spacing of 14.9 Å), suggesting the rearrangement in crystal packing of ceramide and lecithin molecules, compared to the d-spacing in oleogels with single gelator (13.1 Å and 15.3 Å, respectively for ceramide and lecithin). Fourier transform infrared spectroscopy results indicated the presence of interaction between water and gelators, which confirmed the importance of water in self-assembly of this mixed-gelator system. Water-induced conversion of oleogelation provides alternatives in producing hard-stock fat replacers as well as possibilities to incorporate water-soluble nutrients into oil-based products.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it