Synergistic Cross-linking and Reinforcing Enhancement of Rubber Latex with Cellulose Nanocrystals for Glove Applications
Bibliographic record
Abstract
The effect of cellulose nanocrystals (CNCs) on the reinforcing, cross-linking, and solvent barrier properties of lightly cross-linked natural rubber (NR) latex films for dipped goods applications were investigated. Predispersed CNCs, activating, and curing agents were mixed with natural rubber latex and allowed to mature for 2 h. Films were then prepared from the matured latex via dipping and solvent casting processes. The incorporation of CNCs in the NR latex led to remarkable improvement in tensile strength and modulus with progressively thinner films. An increase in the cross-linking density of the rubber films were observed as a result of the incorporation of CNCs, which was observed from proton nuclear magnetic resonance analysis, and toluene swelling studies. This was likely attributed to an enhanced dispersion of the zinc oxide (ZnO) used as a cross-linking activator as observed from electron dispersive X-ray spectroscopy (EDX). A possible mechanism for the improved dispersion of ZnO in the latex in the presence of CNCs was the formation of Zn–CNC complexes. Higher cross-linking densities also led to lower water absorption over a prolonged time period. The nanocomposite thin films showed low permeability to a nonpolar solvent vapor, such as tetrahydrofuran (THF), but increased permeability to water vapor. A practical application of the observed barrier properties in dipped rubber goods could be in gloves, where permeation of perspiration from hands, is allowed while preventing the passage of nonpolar solvents.
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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".