One-Pot Water-Based Hydrophobic Surface Modification of Cellulose Nanocrystals Using Plant Polyphenols
Bibliographic record
Abstract
An environmentally friendly procedure for the surface modification of cellulose nanocrystals (CNCs) in water is presented. Tannic acid (TA), a plant polyphenol, acts as the primer when mixed with CNCs in suspension, which are then reacted with decylamine (DA), the hydrophobe. Schiff base formation/Michael-type addition covalently attaches primary amines with long alkyl tails to CNC-TA, increasing the particle hydrophobicity (contact angle shift from 21 to 74°). After modification, the CNC-TA-DA particles in the water phase separate, allowing for easy collection of modified material. The dried product is readily redispersible in toluene and other organic solvents, as demonstrated by turbidity measurements, dynamic light scattering, optical microscopy, and liquid crystal self-assembly behavior. Electron microscopy, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, solid-state 13 C NMR, and X-ray diffraction support the successful surface modification and indicate that CNC particle morphology is retained. The modified CNCs have a slightly decreased onset of thermal degradation (ca. 10 °C lower) compared with that of unmodified CNCs. We believe that this surface modification strategy presents a scalable, simple, and green approach to the production of hydrophobic biobased nanoparticles which may lend themselves as reinforcing agents in nonpolar polymer composites or stabilizers and rheological modifiers in nonaqueous liquid formulated products.
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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".