Organization of the Interfacial Film of Nanoemulsions Stabilized by Porphyrin Derivatives
Bibliographic record
Abstract
Photodynamic therapies combining the action of a photosensitizer (PS), molecular oxygen, and light make it possible to destroy certain infectious sites and tumors. The incorporation of photosensitizers in nanocarriers allows for better control of their distribution in tissues and increases their concentration in the area that will be then illuminated. Nanoemulsions of glyceryl trioctanoate (GTO) have been designed in which pyropheophobide a (Pyro-A) or its lipid conjugate (Pyro-Lipid) are both stabilizing and photostimulable agents. In this work, we studied by surface pressure measurements and Brewster angle microscopy (BAM) analysis the organization of the interfacial films of nanodroplets. Comparison of preformed porphyrin nanoemulsions and two porphyrin-GTO mixtures, one mimicking the composition of the nanoemulsions and the other that of a porphyrin-rich interfacial film, highlighted the role of GTO and porphyrin derivatives in the formation, organization, and elasticity of the interfacial films in nanoemulsions. Pyro-Lipid and GTO can mix, and some of the GTO molecules remain inserted in the interfacial film at high surface pressures. In contrast, Pyro-A and GTO do not mix well and tend to segregate, leaving Pyro-A alone in the condensed interfacial film. The results of this study demonstrate the importance of characterizing the interfacial properties of porphyrin derivatives and their interaction with the oil to design stable nanoemulsions with well-controlled optical properties.
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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) | 0.000 | 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".