Cellulose Nanocrystals/Polymer Nanocomposites for Application in Adhesives
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are rod-shaped nanoparticles derived from cellulose, the most abundant polymer in the world. CNCs are as strong as Kevlar™, have a high aspect ratio (traditional nanoparticles are spherical) and thus, a higher surface area, which makes them ideal for use in nanocomposites. In addition, CNCs are considered the only safe nanomaterial according to Health Canada. In this thesis, CNCs were used to produce nanocomposites via in situ semi-batch emulsion polymerization. The target application for these nanocomposites was as pressure sensitive adhesives (PSAs). In the past, CNCs have been blended with polymers rather than added in situ. Emulsion polymerization is considered a more sustainable method to synthesize polymers compared to say, solution polymerization. However, adhesives synthesized using this method tend to have a lower shear strength due to poor gel network formation. As a result, conventional emulsion-based PSAs suffer from the inability to increase certain adhesive properties (e.g., tack and peel strength) while simultaneously increasing shear strength. In this thesis, we demonstrate how the use of CNCs via in situ emulsion polymerization overcomes this classic problem. Two polymer systems were tested: isobutyl acrylate (IBA)/n-butyl acrylate (BA)/methyl methacrylate (MMA) and 2-ethylhexyl acrylate (EHA)/BA/MMA. The use of CNC with IBA, a relatively hydrophilic monomer, rather than with EHA, a highly hydrophobic monomer, resulted in the simultaneous improvement of tack, peel strength and shear strength of the PSA films. Dynamic mechanical analysis (DMA) also indicated improved storage and loss moduli with increasing CNC content, further supporting the reinforcing effect of the CNCs within the PSA. EHA followed similar trends as IBA for conversion, particle size, viscosity, pH, glass transition temperature and gel content. On the other hand, the use of CNC with EHA yielded less improvement in adhesive properties due to poor dispersion of the CNCs because of the hydrophobic repulsion by the EHA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".