Modification of Cellulose Nanocrystals with Quaternary Ammonium-Containing Hyperbranched Polyethylene Ionomers by Ionic Assembly
Bibliographic record
Abstract
In this article, we demonstrate the first surface modification of cellulose nanocrystals (CNCs) with quaternary ammonium-containing ionomers by ionic binding of their positively charged ammonium ions onto the negatively charged surface of CNCs. A range of hyperbranched polyethylene ionomers (I1–I6) having different ionic content (0.2–2.3 mol %) has been designed and employed for this purpose. The simple dropwise addition and mixing of the aqueous dispersion of CNCs with the ionomer solution in tetrahydrofuran (THF) conveniently renders the ionomer-modified CNCs (mCNC1–mCNC6). The presence of adsorbed ionomers on the modified CNCs is confirmed with spectroscopic and X-ray diffraction evidence and quantified through thermogravimetric analysis. The effects of the ionomer to CNC feed mass ratio and the ionomers of different ionic content on the modification have been examined. A study on the morphology of the modified CNCs by atomic force microscopy discloses the occurrence of side-to-side and/or end-to-end assembly of the CNC rods due to the “cross-linking” or bridging effects of the multidentate ionomers. Because of the hydrophobic hyperbranched polyethylene segments in the adsorbed ionomers, the modified CNCs can be dispersed in nonpolar or low-polarity organic solvents (such as THF, toluene, and chloroform). In particular, the THF dispersions of modified CNCs prepared with ionomers having ionic content ≥0.7 mol % (I3–I6) behave as thixotropic organo-gels at concentrations ≥40 mg mL –1 . Further, the modified CNCs better disperse than unmodified CNCs in a hydrophobic ethylene–olefin copolymer (EOC) elastomer matrix and show better thermal stability than a surfactant-modified CNC sample. Tensile testing confirms that the EOC composites, filled with the ionomer-modified CNCs, are significantly reinforced with a tensile modulus nearly doubled that of neat EOC, and they demonstrate better elongation at break relative to those filled with unmodified CNCs or surfactant-modified CNCs.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".