Enhancing Cellulose Nanofibril Compatibility with Epoxy Resins through a Water-Based Surface Hydrophobization Strategy
Bibliographic record
Abstract
Although the high specific strength and modulus of cellulose nanofibrils (CNFs) make them promising for composite reinforcement, their hydrophilicity significantly reduces the CNF-polymer interfacial compatibility and promotes CNF aggregation in polymer matrices, thus impacting the final composite properties. To alleviate this issue, we hydrophobized CNFs with a tannic acid (TA) primer layer and hexylamine (HA) hydrophobe using a quick, one-pot, and fully water-based strategy. The modified CNFs (CNF-TA-HA) had a water contact angle of 100°, which was stable long term when stored in an aqueous suspension. Using straightforward colorimetric assays, we showed that the CNF-TA and TA-HA reactions both followed a distinctive "two-stage" process in which the CNF surface was modified almost instantly, followed by the slow diffusion of reagents into CNF bundles, which is unique to the highly entangled and polydisperse industrially produced CNFs used here. Adding 1% w/w oven-dried CNF-TA-HA to commercial epoxy improved tensile modulus and tensile strength by 36 and 48%, respectively, compared to 1% w/w unmodified CNFs, and improved tensile modulus by 7% compared to the neat epoxy resin. These enhanced properties were achieved despite CNF aggregation induced by aggressive oven drying and low fiber fraction (where additives would normally just act as defects). Our TA-alkylamine hydrophobization strategy is translatable across a wide range of cellulose nanomaterial sizes and morphologies and offers a green pathway toward tailored compatibility (without degrading the CNF properties) to ultimately improve 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) | 0.001 | 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".