Versatile synthesis of siloxane‐based graft copolymers with tunable grafting density
Bibliographic record
Abstract
Abstract A versatile synthetic platform is reported that affords high molecular weight graft copolymers containing polydimethylsiloxane (PDMS) backbones and vinyl‐based polymer side chains with excellent control over molecular weight and grafting density. The synthetic approach leverages thiol‐ene click chemistry to attach an atom‐transfer radical polymerization (ATRP) initiator to a variety of commercially available poly(dimethylsiloxane‐ co ‐methylvinylsiloxane) backbones (PDMS‐ co ‐PVMS), followed by controlled radical polymerization with a wide scope of vinyl monomers. Selective degradation of the siloxane backbone with tetrabutylammonium fluoride confirmed the controlled nature of side‐chain growth via ATRP, yielding targeted side‐chain lengths for copolymers containing up to 50% grafting density and overall molecular weights in excess of 1 MDa. In addition, by using a mixture of thiols, grafting density and functionality can be further controlled by tuning initiator loading along the backbone. For example, solid‐state fluorescence of the graft copolymers was achieved by incorporating a thiol‐containing fluorophore along the siloxane backbone during the thiol‐ene click reaction. This simple synthetic platform provides facile control over the properties of a wide variety of grafted copolymers containing flexible PDMS backbones and vinyl polymer side chains.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".