Mechanical performance of wool fabrics grafted with methacrylamide and 2-hydroxyethyl methacrylate by the Kawabata Evaluation System for Fabric method
Bibliographic record
Abstract
Wool fabrics were graft copolymerized with methacrylamide (MAAm) and 2-hydroxyethyl methacrylate (HEMA) in aqueous media, using a chemical redox system as an initiator. After grafting, the mechanical properties related to the hand evaluation, such as tensile, shearing, bending, compression and surface properties of wool fabrics, were evaluated by means of the Kawabata Evaluation System for Fabric. The surface morphology was examined by scanning electron microscopy. The results revealed that the surface morphology and low-stress mechanical properties of wool fabrics were remarkably changed after grafting with HEMA. The weight gain of the wool fabrics grafted with HEMA increased rapidly in the initial grafting stage and reached saturation level at 17 wt% after 20 minutes. Small deposits of oligomers were visible on wool surfaces and typical scale patterns were changed after grafting wool fabric with HEMA. The slope of the shearing hysteresis curves in the weft and warp directions for wool fabrics grafted with HEMA was increased compared with the control and wool fabrics grafted with MAAm. These results imply that the changes in tensile, shearing, bending and compression behavior of grafted wool fabrics are due to the reduction of the free internal volumes of the fabrics, leading to a tightening of its texture.
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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) | 0.001 | 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".