Morphology‐Controlled Synthesis of Poly(oxyethylene)silicone or Alkylsilicone Surfactants with Explicit, Atomically Defined, Branched, Hydrophobic Tails
Bibliographic record
Abstract
Silicone surfactants are widely used in commerce because of the unusual surface activity when compared with fluorocarbon or hydrocarbon surfactants. However, most silicone surfactants are comprised of ill-defined mixtures, which preclude the development of an understanding of structure-surface activity relationships. Herein, we report a synthetic strategy that permits exquisite control over silicone structure by using the B(C(6)F(5))(3)-catalyzed condensation of hydro- and alkoxysilanes. Six different, precise hydrophobes were then mated to hydrophilic poly(oxyethylene)s of three different molecular weights by a metal-free click cyclization to generate a library of explicit silicone surfactants. These compounds were calculated to have a relatively linear value range of the hydrophilic-lipophilic balance, ranging from about 8 to about 15. The solubility of some of the compounds was too low to measure a critical micelle concentration (CMC). The others exhibited a broad range of surface tension values at the CMC that depend both on the length of the hydrophilic tail and, more importantly, the nature of the hydrophobic head group. Subtle distinctions in surfactant-related properties, which can be attributed to the three-dimensional structures, can be seen for compounds with comparable numbers of hydrocarbons and silicon groups.
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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) | 0.000 | 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 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".