Incorporating Hydrophobic Cellulose Nanocrystals inside Latex Particles via Mini‐Emulsion Polymerization
Bibliographic record
Abstract
Abstract Hydrophobic cellulose nanocrystals (CNCs) are encapsulated inside poly(butyl acrylate/vinyl acetate/acrylic acid) latex particles via mini‐emulsion polymerization (MEP). To achieve a genuine MEP, the effects of the concentration of surfactants (sodium dodecyl sulfate/Disponil A3065) and a hydrophobic agent (octadecyl acrylate (ODA)) are optimized in the presence of 0.5 wt% CNC. Using a combination of surfactant and ODA concentrations leading to a particle nucleation method restricted to the monomer droplets, the effects of CNC loading up to 1.5 wt% on the polymerization process and final nanocomposite properties are studied. Despite an increase in particle size and a lower rate of polymerization at higher CNC loadings, the droplet nucleation mechanism remains dominant up to 1.25 wt% CNC loading. Pressure‐sensitive adhesive (PSA) performance for nanocomposites produced using hydrophobic CNCs in MEP decreases whereas performance improves considerably when using hydrophilic and partially hydrophobic CNCs in conventional emulsion polymerization. These results shed light on emulsion polymerization technique selection and CNC surface properties for greener industrial production of water‐based PSAs.
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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".