Improved pressure‐sensitive adhesive performance using carboxylated cellulose nanocrystals via blending
Bibliographic record
Abstract
Abstract Carboxylated cellulose nanocrystals (cCNCs) were blended with high‐quality commercial latex to enhance key pressure‐sensitive adhesive (PSA) properties: tack, peel strength, and shear adhesion. Initially, a 2 5–1 fractional factorial design was used to evaluate the effect of five factors: cCNC type (never‐dried vs. dried re‐dispersed), the use of sonication to disperse the cCNC, cCNC loading, blend temperature, and mixing speed. The regression analysis identified optimal blend conditions and the three most significant factors. It was found that increasing mixing speed had the strongest positive impact on all three PSA properties. A subsequent design of experiments looked at using a different mixing system—a homogenizer—and the data were viewed relative to the mixing power. Further increase in mixing beyond the original design framework led to increase in shear adhesion but decreased tack and peel strength. Nevertheless, in all cases, the PSA properties of the blended latexes exceeded that of the base‐case latex without cCNCs. The second factor of importance was the cCNC loading. The blending of cCNCs at levels beginning at 0.5 phm (parts per hundred parts monomer) led to the simultaneous improvement in all three PSA properties compared to the base‐case latex. The 1 phm level appeared to provide the best impact on the PSA properties. Finally, the need to sonicate the cCNCs dispersions before blending was significant. The results were further supported by rheological measurements, which demonstrated significant increase in viscosity with cCNC addition. This study clearly demonstrates the effectiveness of cCNC blending to improve all PSA properties simultaneously and provides practical insights for industrial‐scale application.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".