Thermal Degradation of Fluorosilicone Rubber Up to 250 °C
Bibliographic record
Abstract
ABSTRACT Fluorosilicone (FVMQ) rubber is commonly used in many military aircraft applications, such as seals and gaskets, due to its exceptional heat stability, low temperature flexibility, and resistance to fuels and oil. A peroxide-cured and silica-filled FVMQ rubber compound was prepared and subsequently submitted to accelerated heat aging for up to 50 wk at temperatures between 75 and 250 °C. Heat-aged samples were then tested for hardness, physical property characteristics, and crosslink density by solvent swell and double quantum–nuclear magnetic resonance (DQ-NMR). The Arrhenius methodology was applied to the shifted experimental data assuming the principle of time–temperature superposition. The tensile strength and elongation at break both decreased upon thermal treatment. A small stiffness increase was observed by both the hardness and the tensile stress at 10% elongation test data. The characterization of the crosslink density by solvent swell testing was inconclusive. By contrast, the DQ-NMR testing clearly showed that the crosslink density decreases, whereas the number of chain defects increases. The unaged crosslink distribution is heterogeneous in nature, displaying much more heterogeneity than peroxide-cured silicone. Nonlinear Arrhenius behavior was observed between 75 and 250 °C, with a mechanism change at 175 °C. The FVMQ crosslink distribution becomes more heterogeneous with aging. A significant loss of low-molecular-weight FVMQ due to depolymerization and backbiting reactions was observed at higher aging temperatures. Reactions with the silica surface also occurred with the creation of Si–O bonds. No direct evidence of thermo-oxidative and oxygenation reactions was observed. Reaction mechanisms have been proposed to explain the degradation of FVMQ due to heat aging.
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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 | 0.000 |
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".