Exploiting supercooled medium-chain lipids and surfactant combinations for solid-phase dominant nanostructured lipid carrier production
Bibliographic record
Abstract
) to create a natural concentration gradient favouring a solid phase loading. By strategically combining these medium-chain lipids with selective surfactants, particularly PEG-100 stearate, we enabled the controlled transformation of initially supercooled melts into organised type I imperfect crystal structures that effectively encapsulate CsA within the solid lipid matrix. This approach allowed single-step hybrid NLC production at temperatures up to 20 °C lower than conventional methods while reducing homogenisation power requirements by 38%. Our optimised formulations maintained particle size (<200 nm), polydispersity (<0.3), and spherical morphology during four-week storage at 4 °C, room temperature, and 40 °C. Most notably, the controlled CsA release profiles in simulated intestinal fluid confirmed successful drug entrapment within the solid lipid matrix rather than the liquid phase. This work presents a robust strategy for producing stable, solid-phase dominant NLCs at reduced processing temperatures, with significant implications for controlled drug delivery and continuous manufacturing processes.
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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 | 0.000 |
| 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".