Role of Emulsifiers and SPF Booster in Sunscreen Performance: Assessing SPF, Rheological Behavior, Texture, and Stability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates the impact of emulsifier substitution and booster concentration on sunscreen characteristics, including physical properties, the sun protection factor (SPF), and sensory attributes. The impact of substituting Polysorbate® 80 with Beautyderm® K10 as an emulsifier in sunscreen formulations, along with the effect of increasing concentrations of the Sunhancer™ Eco SPF Booster, was thoroughly evaluated. Spectrophotometric methods were used to determine SPF, while texture analysis and rheological measurements assessed physical characteristics. Stability was evaluated using a centrifuge stress test, and sensory analysis was conducted on the top-performing formulation. The results indicated that the choice of emulsifier and booster concentration significantly influenced SPF values and stability. The influence of booster concentration on textural properties was most significant in formulations containing Beautyderm®. Centrifuge testing revealed phase separation in certain formulations. Notably, the formulations that exhibited the greatest stability were those in which Beautyderm® was combined with either Polysorbate® or Span®. Following the stability test results, the cream formulation containing Beautyderm® and Polysorbate® as emulsifiers was further evaluated through sensory analysis. Independent assessors determined that the sensory attributes of the cream did not undergo significant changes even when zinc oxide was added at a concentration of 1% (w/w) to the formulation. These findings underscore the importance of carefully selecting emulsifiers and boosters to achieve high sun protection efficacy, stability, and desirable sensory properties in sunscreen formulations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it