Bibliographic record
Abstract
Pre-catalyzing fabric method is a newly developed technique applied in manufacturing thick composites with hand layup process. By applying a peroxide catalyst to the fabric instead of mixing it into the bulk resin system, this technique can slow down the polymerization reaction rate and subsequently reduce the internal temperature of thick composites. In this study, two kinds of pre-catalyzing methods are developed: one uses polystyrene as the catalyst binder; another uses epoxy resin as the binder. The experimental results indicate that the pre-catalyzing method using polystyrene binder can eliminate the peak exothermic temperature, and the method using epoxy binder can limit this temperature to be below 39 C. The latter method has shorter curing time than the former one. The degree of cure for both methods can be more than 87% with low exothermic temperature after cure, and with this degree of cure, the laminate is rigid enough for further post cure. The degree of cure can be improved to be more than 97% by leaving the samples for more than five weeks in ambient temperature. Compared with the polystyrene binder which made the interlaminar shear strength decrease by 12.4%, the epoxy resin binder has the better characteristic, not to exhibit this decrease.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".