Reliable Condensation Curing Silicone Elastomers with Tailorable Properties
Bibliographic record
Abstract
The long-term stability of condensation curing silicone elastomers can be affected by many factors such as curing environment, cross-linker type and concentration, and catalyst concentration. Mechanically unstable silicone elastomers may lead to undesirable application failure or reduced lifetime. This study investigates the stability of different condensation curing silicone elastomer compositions. Elastomers are prepared via the reaction of telechelic silanol-terminated polydimethylsiloxane (HO-PDMS-OH) with trimethoxysilane-terminated polysiloxane ((MeO)3Si-PDMS-Si(OMe)3) and ethoxy-terminated octakis(dimethylsiloxy)-T8-silsesquioxane ((QMOEt)8), respectively. Two post-curing reactions are found to significantly affect both the stability of mechanical properties over time and final properties of the resulting elastomers: Namely, the condensation of dangling and/or unreacted polymer chains, and the reaction between cross-linker molecules. Findings from the stability study are then used to prepare reliable silicone elastomer coatings. Coating properties are tailored by varying the cross-linker molecular weight, type, and concentration. Finally, it is shown that, by proper choice of all three parameters, a coating with excellent scratch resistance and electrical breakdown strength can be produced even without an addition of fillers.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".