Polymer Nanocomposites for Emulsion‐Based Coatings and Adhesives
Bibliographic record
Abstract
Abstract Emulsion‐based coatings and adhesives are in growing demand due to an increased awareness of health and safety issues arising from solvent‐based polymer manufacturing processes. However, emulsion‐based techniques often require additional development to achieve equal or better application performance compared to solvent‐based processes. The inclusion of nanoparticles in emulsion‐based coatings and adhesives can be considered as a promising means to enhance performance. This paper reviews the current progress on the synthesis of emulsion‐based nanocomposites for coating and adhesive applications and addresses the principles and techniques for nanoparticle dispersions and their inclusion into polymer latexes. The effects of nanoparticle shape and size on the enhancement of nanocomposite properties are also highlighted. Among the reinforcing nanoparticles such as nanoclays, carbon nanotubes, and cellulose nanocrystals (CNCs), CNCs are promising due to their abundance, nontoxicity, and accessible surface hydroxyl groups, which facilitate their compatibility with polymer latexes via physical and chemical treatments.
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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".