Organomodified Silicone Elastomer (OMSE) for Next Generation Personal Care Products
Bibliographic record
Abstract
Personal care (PC) products containing commercial silicone elastomer (SE) blends offer superior sensory, due to their silky and non-greasy characteristics. These products contain functional organic actives; namely sunscreens, antibacterial agents and dyes. The functional performance of the PC products is determined by the extent of deposition of the actives on the skin or hair. However, this performance is limited due to incompatibility of organic actives with the silicone elastomer (SE) blend. This review described recent development of organomodified silicone elastomers (OMSE) for enhanced compatibility and superior deposition of actives on the skin or hair. OMSEs contain covalently linked functional actives to the cross-polymeric silicone backbone. OMSE were synthesized by reacting alkenyl functional derivatives of avobenzone, benzylidene malonate, curcumin or hydroxyanthraquinone, suitable crosslinker, solubilizing (e.g. CnH2n i.e. alkyl) / emulsifying agents (e.g. polyether) with the poly(dimethyl)-co-(methylhydrogen)siloxane copolymer in the presence of platinum catalyst. OMSE showed superior functional performance due to uniform molecular distribution of sunscreen / dye molecules on the substrate surface. Covalently attached sunscreen / dye molecules on the cross-polymeric silicone backbone would show enhanced safety profile due to negligible possibility of penetration through skin.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".