Lysine-Based Silicone Surfactants
Bibliographic record
Abstract
Highly efficient silicone surfactants are typically based on polyether hydrophiles. As part of a program to increase the natural content of silicones, we describe the synthesis of silicone surfactants with amino acid hydrophiles (cysteine, arginine, and lysine). The compounds were prepared using a radial thiol–ene reaction with vinylsilicones for cysteine derivatives and a catalyst-free aza-Michael reaction with arginine and lysine. Short chain surfactants with silicone monomer:hydrophile ratios of 5:1 or less (e.g., telechelic silicones of lysine-linker-(Me2OSi)n-linker-lysine n = 10) were ineffective at stabilizing emulsions of silicone oil (D4): water. However, excellent surfactants were realized as the chain length (n) increased to 25 or 50, stabilizing water-in-oil emulsions with high water content (80% or 90%). The surfactants, especially the longer chain compounds, were stable against pH except <4 or >9 and survived freeze/thaw cycles. These surfactants contain 12–25% natural materials, improving their sustainability compared to those containing synthetic hydrophiles.
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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) | 0.002 | 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".