The control of hydroxyl density on glass particulates surface: Application to anionic polymerization of polyamide 6
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Bibliographic record
Abstract
Monomer polymerization after the impregnation of reinforcements is a relevant option for the manufacturing of thermoplastic composites by liquid processes (infusion or RTM). In the case of polyamide 6 (PA6) synthesized by this process, the system is composed of the monomers, the catalyst, and the activator, which react within the fibrous environment. Therefore, controlling the chemistry of the fiber surface is crucial because it influences not only the polymerization and crystallization processes, but it also controls the fiber-matrix adhesion and the resulting mechanical properties. However, the hydroxyl groups typically present on the glass fiber surface inhibit the polymerization process. The objective of this project is to adapt and control the surface chemistry of glass particulate reinforcements in order to promote the polymerization and crystallization processes of PA6. This work was carried out with glass microbeads comparable in size to the diameter of glass fibers. First, the evolution of the hydroxyl groups surface density on the glass beads, as a function of calcination time, was monitored by thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FTIR). The hydroxyl groups surface density was then tuned in order to allow thesynthesis of anionic polyamide 6 (PA6), which was monitored by differential scanning calorimetry (DSC). Next, in order to further promote the polymerization and crystallization of PA6, and to improve interfacial adhesion between the PA6 and glass particulates, the grafting process of an amino-silane coupling agent at the particulates surface was investigated by TGA and FTIR. The results demonstrate that the competition between re-hydroxylation and condensation of the silane on the surface during the grafting process needs to be carefully balanced in order to maintain fast polymerization kinetics, as revealed by DSC analysis. Overall, the systematic methodology presented in this work can be adapted for various combinations of reactive resins and solid fillers, allowing for the preparation of high performance (nano)composite materials.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it