Effects of Fiber Modification on the Mechanical Properties of Solution-Cast Poly(vinyl chloride) Films Reinforced by Aramid Fibers
Bibliographic record
Abstract
This study compared the effectiveness of three fiber treatment methods for reinforcing polyvinyl chloride (PVC) with aramid fibers. Two of them used phosphoric and nitric acids, respectively, the latter of which was introduced for the first time. Acid etching roughens the fiber surface for increased friction and mechanical interlocking with PVC. The third approach treated fibers with a dimethyl sulfoxide (DMSO)/potassium hydroxide (KOH) deprotonation procedure. A more streamlined procedure was introduced in this study which omitted alkyl functionalization in favor of ethanol precipitation. Deprotonation disrupts the crystalline structure in the fibers and unbundle them into aramid nanofibers (ANFs). All treatment procedures lead to substantial improvements in composite mechanical properties. Compared with phosphorous acid, treatment by nitric acid results in higher tensile strength and Young’s modulus. Composites with ANFs show highest tensile strength among all cases. Fractography reveals distinct failure mechanisms between composites with acid-treated fibers versus those with ANFs. The acid-treated fibers exhibit interfacial delamination during material failure, indicating that enhanced composite strength is mainly attributed to stronger interfacial forces. In contrast, ANFs do not carry load in the same way as the original fibers do, but their smaller dimensions allow nanoscale dispersion, which can suppress microcrack formation during deformation. Our systematic evaluation of fiber treatment options provides critical insights for tailoring fiber-matrix interactions in non-reactive thermoplastic systems, advancing the application potential of flexible PVC in load-bearing environments.
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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".