Thermal oxidative degradation of cyanate- and amine-cured epoxy resins: Experiment and ReaxFF simulation
Bibliographic record
Abstract
Thermal oxidative degradation behaviors of epoxy resin cured with cyanate ester and conventional amine are experimentally evaluated in terms of heat resistance and flame retardancy, and the degradation mechanisms are further investigated using ReaxFF molecular dynamics simulations. Thermogravimetric analysis revealed that both resins exhibit multi-step decomposition behavior, with the cyanate-cured resin showing nearly double the residual weight (char formation) compared to the amine-cured resin. ReaxFF thermal degradation simulations of a fully cured resin model, with and without oxygen, and fragmentation models were performed to elucidate the heat resistance properties associated with different molecular structures. The experimental and simulation results suggest that the degradation follows a two-step process: the first stage involves pyrolysis of the main chain, and the second stage involves oxidation of the resulting products. The high heat resistance of the triazine ring in the cyanate-cured resin delays the transition to the second stage of degradation, resulting in high thermal oxidation resistance. • Thermal oxidative degradation behavior of cyanate- and amine-cured epoxy resin. • Cyanate-cured epoxy resin exhibited higher thermal stability in thermogravimetry. • ReaxFF captured two-step thermal oxidative degradation and revealed its mechanism. • Triazine ring acts to delay the transition to the intense second decomposition stage.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".