How latex film formation and adhesion at the nanoscale correlate to performance of pressure sensitive adhesives with cellulose nanocrystals
Bibliographic record
Abstract
Emulsion polymerized latex-based pressure-sensitive adhesives (PSAs) are more environmentally benign because they are synthesized in water but often underperform compared to their solution polymerized counterparts. Studies have shown a simultaneous improvement in the tack, and peel and shear strength of various acrylic PSAs upon the addition of cellulose nanocrystals (CNCs). This work uses atomic force microscopy (AFM) to examine the role of CNCs in (i) the coalescence of hydrophobic 2-ethyl hexyl acrylate/ n -butyl acrylate/methyl methacrylate (EHA/BA/MMA) latex films and (ii) as adhesion modifiers over multiple length scales. Thin films with varying solids content and CNC loading were prepared by spin coating. AFM revealed that CNCs lowered the solids content threshold for latex particle coalescence during film formation. This improved the cohesive strength of the films, which was directly reflected in the increased shear strength of the EHA/BA/MMA PSAs with increasing CNC loading. Colloidal probe AFM indicated that the nano-adhesion of thicker continuous latex films increased with CNC loading when measured over small contact areas where the effect of surface roughness was negligible. Conversely, the beneficial effects of the CNCs on macroscopic PSA tack and peel strength were outweighed by the effects of increased surface roughness with increasing CNC loading over larger surface areas. This highlights that CNCs can improve both cohesive and adhesive PSA properties; however, the effects are most pronounced when the CNCs interact favourably with the latex polymer and are uniformly dispersed throughout the adhesive film. This article is part of the theme issue ‘Bio-derived and bioinspired sustainable advanced materials for emerging technologies (part 1)’.
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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 | 0.000 |
| 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".