Pushing the Limits with Cellulose Nanocrystal Loadings in Latex‐Based Pressure‐Sensitive Adhesive Nanocomposites
Bibliographic record
Abstract
Abstract Cellulose nanocrystals (CNCs) are a naturally sourced, nontoxic, nanoparticle known to improve tack, peel strength, and shear resistance simultaneously when incorporated into nanocomposite latexes produced for pressure‐sensitive adhesive (PSA) applications. In this study, methods for incorporating CNCs into a butyl acrylate/styrene/acrylic acid (91.5/4.5/4.0 wt%) seeded semibatch emulsion polymerization for the production of PSAs are presented. Past work has revealed a limit of 1.0–2.0 wt% (based on monomer) CNC loadings due to latex instability. In this work, CNC loadings of up to 4.0 wt% in a stable latex with 40 wt% solids are achieved by utilizing all of the available water in the formulation to maximize CNC redispersion. The CNC addition method is studied (i.e., in the seed, in the feed, or partitioned in both) and adhesive performance results indicate that there are no significant benefits to where the CNCs are dispersed in situ during polymerization. However, the quality of the CNC‐water dispersion is pivotal to ensuring latex homogeneity and therefore, adhesive film quality. Though CNCs improved adhesive strength at low concentrations, their enhancing effects are modest when used with a commercially competitive base‐case PSA formulation.
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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".