Cellulose Nanocrystal (CNC)–Latex Nanocomposites: Effect of CNC Hydrophilicity and Charge on Rheological, Mechanical, and Adhesive Properties
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs), a sustainable nanomaterial, are in situ incorporated into emulsion-based pressure-sensitive adhesives (PSAs). Commercially available CNCs with different surface hydrophilicity and surface charge (CNC101 and CNC103 from CelluForce) are used to explore their role in PSA property modification. Viscosity measurements and atomic force microscopy reveal differences in degree of association between the CNCs and the latex particles depending on the surface properties of the CNCs. The more hydrophilic and higher surface charge CNCs (CNC101) show less association with the latex particles. Dynamic strain sweep tests are used to analyze the strain-softening of the nanocomposites based on CNC type and loading. The CNC101 nanocomposites soften at lower strains than their CNC103 counterparts. This behavior is confirmed via dynamic frequency tests and modeling of the nanocomposites' storage moduli, which suggest the formation of CNC aggregates of, on average, 3.8 CNC101 and 1.3 CNC103 nanoparticles. Finally, PSA properties, i.e., tack, peel strength, and shear strength, simultaneously increase upon addition of both CNC types, although to different extents. The relationship between the PSA properties and CNC surface properties confirms that the less hydrophilic CNCs lead to improved CNC dispersion in the PSA films and therefore, enhance PSA properties.
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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".