Naturally Derived Silicone Surfactants Based on Saccharides and Cysteamine
Bibliographic record
Abstract
Silicone surfactants are widely used in many industries and mostly rely on poly(ethylene glycol) (PEG) as the hydrophile. This can be disadvantageous because commercial PEG examples vary significantly in polydispersity-constraining control over surface activity of the surfactant-and there are environmental concerns associated with PEG. Herein, we report a three-step synthetic method for the preparation of saccharide-silicone surfactants using the natural linker, cysteamine, and saccharide lactones. The Piers-Rubinsztajn plus thiol-ene plus amidation process is attractive for several reasons: if employed in the correct synthetic order, it allows for precise tailoring of both hydrophobe and hydrophile; it permits the ready utilization of natural hydrophiles cysteamine and saccharides in combination with silicones, which have significantly better environmental profiles than PEG; and the products exhibit interesting surface activities.
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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".