Modeling of a two-stage polymerization considering glass fibre sizing using molecular dynamics
Bibliographic record
Abstract
Fibre reinforced polymers are an important class of materials due to their light weight, high strength, and stiffness. However, there is a lack of knowledge about the interaction of fibre surface, sizing (fibre coating), and resin. Often only idealised academic systems are studied, and only rarely realistic systems that are used in an industrial context. Therefore, methods for studying the behaviour of complex sizing are highly desirable, especially as they play a crucial role in the performance of fibre reinforced polymers. Here, a simplified, yet industrially used resin system is extended using molecular dynamics simulations by adding a fibre surface and sizing layers. Furthermore, a common coupling agent was selected, and several additional assumptions were made about the structure of the sizing. Based on this, a systematic procedure for the development of a final cured system is introduced: a condensation reaction to form oligomers from coupling agent monomers is conducted. Subsequently, a two stage reaction, a polyurethane reaction and a radical polymerisation, is modelled based on an established approach. Using the final cured system, evaluations of averaged quantities during the reactions are carried out. Moreover, the system is evaluated along the normal direction of the fibre surface, which proves a spatial analysis of the fibre-sizing-resin interface.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".