EXPLORING BRANCHED SILICONE POLYMERS TO TAILOR THE PROPERTIES OF GELS
Bibliographic record
Abstract
Silicones are useful in a variety of applications due to their diverse properties. The materials gain additional value when the basic constituents, oils and elastomers, are combined to create silicone gels. These materials possess excellent tunable properties such as moldability, tack, and adhesion, which are useful in certain circumstances. Only linear oils are currently used to make commercial silicone gels. While the materials initially possess desirable properties, over time the linear silicone oil can bleed out, and naturally, this is problematic for a variety of reasons. Among other things, the physical properties of the gel change and the oil that leaches out can be problematic. We test in this thesis the hypothesis that the use of branched silicone oils, as opposed to linear materials, in a gel could lead to lower levels of bleed (or slower release). There is currently very little research in the literature on the effect of adding branches to linear silicone polymers. This thesis explores the synthesis of branched structures (dendrons) synthesized using the Piers Rubinsztajn reaction. These compounds were subsequently grafted onto linear SiH bearing silicone polymers at different frequencies through a hydrosilylation reaction. The branched silicones were characterized by NMR and the viscosity of the various oils was measured; the latter property correlated with the frequency of branching. The viscosity increased in a linear fashion until a maximum viscosity was observed, at which point further branching led to a slight decrease in viscosity; this trend was observed with silicone backbones at three molecular weights. The branched silicone oils, capped with vinylpentamethyldisiloxane to remove remaining SiH sites, were then incorporated into gels. The Young’s modulus was measured and bleed measurements were collected twice over a ten-day period. Both measurements demonstrated that branching silicone polymers influenced the properties relative to linear silicone oils of comparable molecular weight. We discuss the possible origins of these differences.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".