Multicomponent oleogels of ethylcellulose-binary waxes: atomic force microscopy observation and synergistic effect analysis
Bibliographic record
Abstract
The interactions between ethylcellulose (EC) and waxes in multicomponent oleogel systems are underexplored. This study investigated the structural, functional, and physiochemical properties of rice bran oil (RBO) oleogels structured with various ratios of EC and a binary wax blend (9:1 beeswax (BW): carnauba wax (CRW)), varied in 0.5 % w/w increments at a constant total gelator concentration of 4 % w/w. All multicomponent systems formed self-sustaining oleogels at the low gelator concentration. High-resolution atomic force microscopy (AFM) revealed continuous mesh-like EC chains and their presence on the surface of wax crystals in multicomponent oleogels, suggesting co-existing structures. The oil binding capacity (OBC) of EC oleogel increased significantly from 70.92 % to 99.94 % when the binary wax ratio reached 3.5 % w/w. However, the 0.5EC3.5Wax oleogel exhibited a firmer but brittle gel (G' = 87,440 Pa, yield flow points = 0.12 and 0.39 %, respectively), whereas the 3.5EC0.5Wax oleogel significantly improved the yield and flow points (1.82 and 23.35 %, respectively) but reduced G' (4404 Pa). Temperature ramp tests revealed that higher EC ratios led to early viscoelastic structuring, followed by wax crystallisation that reinforces the final network. The highest gelation onset temperature was observed for 4EC at 94.29 °C, which decreased to 89.54 °C in 3.5EC0.5Wax, with the lowest value of 36 °C identified in the 0.5EC3.5Wax oleogel. Fourier-transform infrared (FTIR) spectroscopy confirmed that the multicomponent oleogels were physically stabilized by hydrogen bonding and van der Waals forces. X-ray diffraction (XRD) showed the presence of β' crystals; the crystallinity and plasticity increased with the binary wax ratio. These results show the potential of preparing stable solid fat substitutes using mixtures of EC and binary wax.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".