The Effect of Cellulose Nanocrystal Surface Properties on Emulsion-Based Adhesive Performance
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are attractive nanomaterials due to their superior mechanical properties, renewability, and natural abundance. Their surface hydroxyl groups, along with surface charges induced during their production, allow CNCs to be easily dispersed in an aqueous medium, especially with sustainable water-based production methods such as emulsion polymerization. Moreover, their surface functionality makes them highly suitable for modification, thereby making them even more versatile. Emulsion polymer latexes are heterogeneous mixtures, having a continuous aqueous phase along with a dispersed organic phase. Latex polymers are used in a wide range of applications such as in coating and adhesive films. Because of the bi-phasic nature of emulsion polymerizations, the surface properties of CNCs play a crucial role in their location relative to the organic phase, and how well-dispersed they are in the cast films. In this thesis, three grades of CNCs (Celluforce Inc.) with either hydrophilic, partially-hydrophobic, or hydrophobic surface properties, were combined with conventional emulsion and miniemulsion polymer formulations to investigate their effect on the properties of pressure sensitive adhesive (PSA) films. In the first instance, hydrophilic CNCs were tested in a seeded semi-batch emulsion polymerization. Using a sequential experimental design, the effects of polar comonomer, surfactant, chain transfer agent, and CNC loading on latex stability and PSA properties were studied. By increasing polymer chain entanglements and the work of adhesion, the hydrophilic CNCs were observed to simultaneously improve the three key properties of acrylic-based PSA films, i.e., tack, peel strength and shear strength. In the second part of this project, we compared the role of hydrophilic and partially-hydrophobic CNCs in PSA property modification. Viscosity measurements and atomic force microscopy revealed differences in the degree of association between the two types of CNCs and the latex particles. Dynamic strain-sweep tests showed that hydrophilic CNC nanocomposites softened at lower strains than their partially-hydrophobic counterparts. This behaviour was confirmed via dynamic frequency tests and modelling of the nanocomposites’ storage moduli, which suggested the formation of CNC aggregates of, on average, 3.8 and 1.3 times the length of CNCs. These results confirmed that the partially-hydrophobic CNCs led to improved CNC dispersion in the PSA films and ultimately, enhanced PSA properties. In the third part of the project, mini-emulsion polymerization (MEP) was used to embed the hydrophobic CNCs within the polymer particles in contrast to the hydrophilic and partially-hydrophobic CNCs which resided mainly in the aqueous phase or near the water-particle interface. Higher CNC loadings led to increased particle size, decreased polymerization rate and number of particles, while only slightly increased the viscosity and the work of adhesion. PSA film properties decreased upon the incorporation of hydrophobic CNCs. Transmission electron microscopy showed that CNCs were expelled from the latex particles at higher loadings, suggesting the incompatibility of the acrylic polymer and the CNCs’ modifying agents. The ability to modify CNCs enables one to achieve a range of hydrophilicity/hydrophobicity. This makes them extremely versatile in a heterogeneous mixture such as in an emulsion polymerization. Because emulsion polymers are used in a wide range of applications with a broad spectrum of properties (i.e., not only as adhesives but as non-tacky coatings), our ability to control CNC location relative to the polymer particles in the latex opens the door to a world of high value-added sustainable polymer 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".