Poly(methyl methacrylate)‐grafted cellulose nanocrystals: One‐step synthesis, nanocomposite preparation, and characterization
Bibliographic record
Abstract
Abstract Cellulose nanocrystals (CNCs) are ideal reinforcing agents for polymer nanocomposites because they are lightweight and nano‐sized with a large aspect ratio and high elastic modulus. To overcome the poor compatibility of hydrophilic CNCs in non‐polar composite matrices, we grafted poly(methyl methacrylate) (PMMA) from the surface of CNCs using an aqueous, one‐pot, free radical polymerization method with ceric ammonium nitrate as the initiator. The hybrid nanoparticles were characterized by CP/MAS NMR, X‐ray photoelectron spectroscopy, infrared spectroscopy, contact angle, thermogravimetric analysis, X‐ray diffraction, and atomic force microscopy. Spectroscopy demonstrates that 0.11 g/g (11 wt %) PMMA is grafted from the CNC surface, giving PMMA‐ g ‐CNCs, which are similar in size and crystallinity to unmodified CNCs but have an onset of thermal degradation 45 °C lower. Nanocomposites were prepared by compounding unmodified CNCs and PMMA‐ g ‐CNCs (0.0025–0.02 g/g (0.25–2 wt %) loading) with PMMA using melt mixing and wet ball milling. CNCs improved the performance of melt‐mixed nanocomposites at 0.02 g/g (2 wt %) loading compared to the PMMA control, while lower loadings of CNCs and all loadings of PMMA‐ g ‐CNCs did not. The difference in Young's modulus between unmodified CNC and polymer‐grafted CNC composites was generally insignificant. Overall, ball‐milled composites had inferior mechanical and rheological properties compared to melt‐mixed composites. Scanning electron microscopy showed aggregation in the samples with CNCs, but more pronounced aggregation with PMMA ‐g‐ CNCs. Despite improving interfacial compatibility between the nanoparticles and the matrix, the effect of PMMA‐ g ‐CNC aggregation and decreased thermal stability dominated the composite performance.
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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".