Preparation of liposomes using supercritical carbon dioxide technology: Effects of phospholipids and sterols
Bibliographic record
Abstract
Liposomes were prepared utilizing a supercritical carbon dioxide (SC-CO 2 ) process. A phospholipid suspension was first equilibrated with CO 2 at 300 bar and then depressurized at a constant pressure and rate. The effects of phospholipid concentration, phospholipid type, sterol concentration and sterol type on particle size, uniformity, zeta potential and morphology were investigated. With increasing soy lecithin concentration (5–30 mM) at 50 °C, the smallest particle size of liposomes (146.1 ± 0.8 nm) was obtained at 30 mM with the polydispersity index (PdI) of 0.398 ± 0.008. Increased phospholipid concentration was favorable for the formation of smaller size vesicles with higher uniformity. Longer chain length of fatty acids in pure phospholipids resulted in a larger particle size with more spherical shape while a phospholipid with unsaturated fatty acyl chains resulted in increased asymmetry. With elevated β-sitosterol concentration (10%–50%), particle size and PdI increased to 245.5 ± 7.14 nm and 0.514 ± 0.018, respectively, with decreased absolute zeta potential . 6-Ketocholestanol showed the smallest diameter and PdI of liposomes with the most spherical shape among all sterol types tested. Soy lecithin exhibited the highest stability of vesicular systems due to its highest absolute zeta potential (− 58.3 ± 2.17 mV) among all the phospholipid types. The SC-CO 2 method demonstrated superior characteristics of liposomes over traditional thin film hydration method for a smaller size and PdI as well as enhanced intactness without leakage. It might offer a promising way to reduce usage of sterol in liposome formulations while eliminating organic solvent usage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".