Polymerization compounding composites of nylon‐6,6/short glass fiber
Bibliographic record
Abstract
Abstract Nylon‐6,6 was grafted onto the surface of short glass fibers through the sequential reaction of adipoyl chloride and hexamethylenediamine onto the fiber surface. Grafted and unsized short glass fibers (USGF) were used to prepare composites with nylon‐6,6 via melt blending. The glass fibers were found to act as nucleating agents for the nylon‐6,6 matrix. Grafted glass fiber composites have higher crystallization temperatures than USGF composites, indicating that grafted nylon‐6,6 molecules further increase crystallization rate of composites. Grafted glass fiber composites were also found to have higher tensile strength, tensile modulus, dynamic storage modulus, and melt viscosity than USGF composites. Property enhancement is attributed to improved wetting and interactions between the nylon‐6,6 matrix and the modified surface of glass fibers, which is supported by scanning electron microscopy (SEM) analysis. The glass transition (tan δ) temperatures extracted from dynamic mechanical analysis (DMA) are found to be unchanged for USGF, while in the case of grafted glass fiber, tan δ increases with increasing glass fiber contents. Moreover, the peak values (i.e., intensity) of tan δ are slightly lower for grafted glass fiber composites than for USGF composites, further indicating improved interactions between the grafted glass fibers and nylon‐6,6 matrix. The Halpin‐Tsai and modified Kelly‐Tyson models were used to predict the tensile modulus and tensile strength, respectively.
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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) | 0.001 | 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) | 0.002 | 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".