Synthesis and characterization of siloxane‐based cyanate ester elastomers from readily available materials: a top‐down approach
Bibliographic record
Abstract
Abstract A number of siloxane‐based cyanate ester (SiCE) elastomers were prepared from commercially available starting reagents employing a hydrosilylation reaction. Each elastomer was designed to utilize 2,4,6‐tris(allyloxy)‐1,3,5‐triazine as a crosslinker and multifunctional vinyl component in the hydrosilylation reaction, ensuring that the triazine rings were completely formed, and thus the elastomers resemble fully cured cyanate ester networks. The hydride‐terminated siloxane components used were varied from small‐molecule siloxanes to pre‐polymers of different molecular weights. Attenuated total reflectance Fourier transform infrared analysis confirmed the successful hydrosilylation reaction and complete curing of the SiCE elastomers via functional group analysis. Thermal characterization by thermogravimetric analysis and differential scanning calorimetry demonstrated that thermal properties of the elastomers could be tailored depending on the type of siloxane component that was utilized. The gel content of the elastomers was also determined. Investigations into the effects of a platinum catalyst on the elastomers determined that the presence of the catalyst affected the thermochemical stability of the SiCE elastomers. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.
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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) | 0.001 | 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".