Formulation of sunscreens with enhancement sun protection factor response based on solid lipid nanoparticles
Bibliographic record
Abstract
Solid lipid nanoparticle (SLN) was regarded as new topical delivery systems for pharmaceutical and cosmetic active ingredients. The purpose of this study is to develop carrier systems for organic and inorganic sunscreens based on a matrix composed of carnauba wax and decyl oleate. Formulae (F1-F7) were prepared using butyl methoxydibenzoylmethane and octyl methoxycinnamate as organic components, and titanium dioxide (TiO(2) ) was used as inorganic component. Both types of sunscreens were incorporated into SLN formulations using classical method of preparation. To evaluate the effect of the pigments on the nanoparticles, particle size was measured using Mastersizer particle size analyser. UV-protection abilities of formulations were investigated by the in vitro sun protection factor test (SPF). Further parameters determined were spreadability as well as viscosity. The rheological behaviour of the formulations was also carried out. From the plot of log of shear stress vs. log of shear rate, the slope of the plot representing flow index and ontology of the y-intercept indicating consistency index was calculated. The formulae showed a flow index of 0.2074-0.4005 indicating pseudoplastic flow behaviour. Significant increases in SPF values up to about 50 were reported after the encapsulation by using organic and inorganic filters in Canada wax and decyl oleate. So, SLN could be appropriate vehicles to carry organic and inorganic sunscreens. The rational combination of cinnamates, titanium dioxide and Zinc oxide has shown a synergistic effect to improve the SPF of cosmetic preparations.
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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".