Batch Mixing for the <i>In Situ</i> Grafting of Epoxidized Rubber onto Cellulose Nanocrystals
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are sustainable nanomaterials commonly employed as biofillers in polymer composites. However, they disperse poorly in hydrophobic polymers in their pristine state, leading to premature failure under stress. Thus, there is an ongoing research effort to enhance the dispersion of CNCs in various polymer matrices to obtain their touted reinforcing potential. In this work, the CNC formation of covalent grafts with epoxidized natural rubber (ENR) and the formation of secondary hydrogen bonds in a base-catalyzed reaction was studied at varying temperatures. The grafting reaction was confirmed using Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and toluene swelling experiments. ENR-CNCs processed at 180 °C showed an 83% increase in tensile strength compared to neat ENR. While the 180 °C treated ENR reinforced with CNCs showed superior properties, all temperature-treated composites exhibited improved tensile strength and elongation at break. Toluene swelling tests confirming the ENR-CNC composites’ insolubility treated at 180 and 220 °C. This was corroborated by a 342% increase in Mooney viscosity and improved rheological properties. Overall, the catalyzed thermal treatment of the ENR-CNC composite system generates covalent cross-links between the CNCs and ENR that result in enhanced physicomechanical properties. Such composites could be employed as a masterbatch filler for other hydrophobic rubbers, which can consequently enhance the compatibility of CNCs with the rubbers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".