Formulation and evaluation of peel-off gel mask with St. John's Wort oil and activated carbon from pinecone
Bibliographic record
Abstract
Abstract Objective Skin needs care to protect against environmental pollution. The facial skin can be protected such as cream, peel off mask and lotion facemask. This study aim is to develop the antibacterial peel-off mask gel containing St. John's Wort Oil (Hypericum perforatum oil).Methods This peel-off gel mask consists of ascorbic acid, polyvinyl alcohol (PVA, as preservative), polyethylene glycol (peg), glycrine (as plasticizer), polysorbate (tween twenty, as stabilizer), ethanol and distilled water with the addition of St. John's Wort Oil and active carbon. The peel-off physical properties (homogeneity, spreadability, viscosity, film-drying time), chemical properties (pH value, stability and antibacterial activity properties) were examined. Using of an active ingredient in peel off mask, it strengthens the role of peel off mask by opening the clogged pores. Activated carbon was added to this formulation as an active ingredient due to its adsorbent activity. In this present study, activated carbon was obtained from pinecone.Results The specific surface area (SBET) of activated carbon was found to be 536.998 m²/g. The antimicrobial activity of the St. John's Wort Oil was tested against Gram-negative bacteria (Pseudomonas aeruginosa) and Gram-positive bacteria (Staphylococcus aureus) as well as one pathogenic fungus (Candida albicans, ATCC 10231). Disc diffusion method was used to study antimicrobial activity.Conclusion The prepared peel of mask showed a good peeling feature without causing edema or irritation on the skin and that it can increase skin cleansing by removing the dirt in the skin pores.
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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) | 0.001 | 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".