Dynamic mechanical analysis of novel cosmeceutical facial creams containing nano‐encapsulated natural plant and fruit extracts
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
BACKGROUND: Cosmetic industry following the recent trends in the relative market has turned its interest in the formation of cosmeceutical products containing natural bioactive ingredients. Natural extracts may reveal undesirable sensory characteristics due to their composition. Encapsulation and nanotechnology are the most promising methods to overcome these drawbacks, opening up new perspectives for the future of cosmeceutical industry. AIMS: The purpose of this study was the use of nano-encapsulated plant and fruit extracts to formulate cosmeceutical facial creams with acceptable rheological characteristics. METHODS: Electrohydrodynamic process was used to encapsulate pomegranate and tea tree oil extracts and incorporate them in facial cosmetic creams. All the formulations including those without additives, were stored at three different temperatures. Subsequently, rheological oscillatory tests (frequency sweep tests) were performed using the dynamic mechanical analysis method in order to evaluate alterations in storage modulus (G'), loss modulus (G''), and complex viscosity (η*). RESULTS: Dynamic mechanical analysis, showed that all formulations are suitable for application in cosmetic industry, while changes due to storage period or the storage temperature were negligible. CONCLUSION: The addition of the selected extracts' nanofibers to formulate cosmeceutical facial creams, developed products with acceptable rheological characteristics that could be decisive for the cosmetics industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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