Preparation, Characterization and Evaluation of Organogel-Based Lipstick Formulations: Application in Cosmetics
Bibliographic record
Abstract
1,3:2,4-Dibenzylidene-D-sorbitol (DBS) and 12-hydroxystearic acid (12-HSA) are well-known as low-molecular-weight organogelators (LMOGs) capable of gelling an organic liquid phase. Considering their unique chemical and physical properties, we assessed their potential effects in new lipstick formulations by discrimination testing; in vitro measurements of the sun protection factor (SPF); and thermal, mechanical and texture analyzes. DBS and 12-HSA were used to formulate four types of lipsticks: L1 (1% DBS), L2 (10% 12-HSA), L3 (1.5% DBS) and L4 (control, no LMOGs). The lipsticks were tested for sensory perception with an untrained panel of 16 consumers. LMOG formulations exhibited higher UVA protection factor (UVA-PF) and in vitro SPF, particularly in the 12-HSA-based lipstick. Regarding thermal properties, the 12-HSA-based lipstick and those without LMOGs were more heat-amenable compared to thermoresistant DBS-based lipsticks. The results also showed the viscoelastic and thermally reversible properties of LMOGs and their effect of increasing pay-off values. In general, the texture analysis indicated that 12-HSA-based lipstick was significantly harder to bend compared to control, while the other formulations became softer and easier to bend throughout the stability study. This work suggests the potential use of LMOGs as a structuring agent for lipsticks, paving the way towards more photoprotective and sustainable alternatives.
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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".