Nanoscale Structure–Property Relationships of Cyanate Ester as a Function of Extent of Cure
Bibliographic record
Abstract
Cyanate esters are key thermosetting resins for composite materials that require structural integrity and resistance to elevated temperatures. Because cyanate ester composites require relatively high processing temperatures, they are susceptible to the formation of process-induced residual stresses, which compromise their overall strength and durability. Process modeling is a key strategy for optimizing processing parameters to minimize such residual stresses. A necessary component of effective and efficient process modeling of composites is computationally established resin property evolution relationships for a range of processing parameters. In this study, the physical, mechanical, and thermal properties of a cyanate ester resin are established as a function of processing time and temperature using experimentally validated molecular dynamics modeling. The results show that the properties are strongly dependent on the processing temperature. At processing temperatures above 160 °C, the properties quickly approach their fully cured values, whereas at processing temperatures below 140 °C, the chemical cross-linking is significantly inhibited, and processing times to complete cure are relatively long. The evolution of the physical, mechanical, and thermal properties as a function of processing time is established, which is critical data needed as input into multiscale process modeling and optimization of cyanate ester composites for computationally driven composite design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".