Effect of fiber surface treatment on mechanical, interfacial, and moisture absorption properties of cattail fiber‐reinforced composites
Bibliographic record
Abstract
Abstract Surface treatment of cattail, a lignocellulosic renewable fiber, was investigated to determine the conditions that would reduce moisture absorption while maximizing the properties of cattail fiber‐reinforced unsaturated polyester composites. Surface modification of cattail fiber was studied by treating them with 2.5, 5, and 10% of 1,6‐diisocyanatohexane (DIH) and 2‐hydroxyethyl acrylate (HEA) for three different immersion times (10, 20, and 30 min). DIH‐HEA treated fibers were preformed into a non‐woven mat and impregnated with unsaturated polyester resin to manufacture composite. The existence of covalent bonds on the treated fibers via NH and CN groups was confirmed by FTIR spectroscopy. The 10% DIH‐HEA resulted in the best results; while the mean diameter of the treated fiber decreased by ~37%, the modulus and the strength of it increased by ~267 and ~151%, respectively. Equilibrium moisture regain of the treated fibers and their composites decreased by ~43% and ~40%, respectively. The tensile modulus of the composites increased by ~171%. Enhancement in tensile strength is observed but could not be quantified due to the difference in Vf and scatter in the data. SEM examination confirmed the enhancement in fiber–matrix bonding due to surface treatment.
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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) | 0.000 | 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".