Facile synthesis of phenyl‐rich functional siloxanes from simple silanes
Bibliographic record
Abstract
Abstract Phenyl‐rich silicone polymers are used for their excellent thermal properties and high refractive indices. Traditional syntheses of these polymers utilize cationic or anionic equilibration, which limits the molecular weights that can be achieved due, in part, to the coproduction of cyclic monomers that must be removed. Kinetically controlled processes may reduce the impact of these limitations, but require high temperatures, alkyllithium initiators and an inert atmosphere; precise structures are difficult to access. The Piers‐Rubinsztajn reaction, combined with hydrolysis, allows the synthesis of highly ordered, Si‐H terminated, phenyl‐rich silicone homo‐ and copolymers comprised of phenylmethyl, diphenyl and, dimethylsilicone monomers. The processes are mild and permit a high level of structural control, including alternating copolymers with different levels of phenyl content (Ph/Si = 0.3–1.5) with molecular weights up to ~100 kDa. Yet higher molecular weights could be achieved—M n up to 300 kDa—when phenyl‐rich siloxanes were incorporated into block copolymers with dimethylsilicones (Ph/Si = 0.4). Unlike kinetic processes in which cyclic byproducts are formed by redistribution or backbiting (particularly at high conversion), in this process cyclics form near the onset of the reaction and only with low molecular weight starting materials (< 4 siloxane units).
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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".