Tailoring adhesion of anionic surfaces using cationic PISA-latexes – towards tough nanocellulose materials in the wet state
Bibliographic record
Abstract
Cationic latexes with Tgs ranging between -40 °C and 120 °C were synthesised using n-butyl acrylate (BA) and/or methyl methacrylate (MMA) as the core polymers. Reversible addition-fragmentation chain transfer (RAFT) combined with polymerisation-induced self-assembly (PISA) allowed for in situ chain-extension of a cationic macromolecular RAFT agent (macroRAFT) of poly(N-[3-(dimethylamino)propyl] methacrylamide) (PDMAPMA), used as stabiliser in so-called surfactant-free emulsion polymerisation. The resulting narrowly distributed nanosized latexes adsorbed readily onto silica surfaces and to model surfaces of cellulose nanofibrils, as demonstrated by quartz crystal microbalance with dissipation monitoring (QCM-D) measurements. Adsorption to anionic surfaces increased when increasing ionic strength to 10 mM, indicating the influence of the polyelectrolyte effect exerted by the corona. The polyelectrolyte corona affected the interactions in the wet state, the stability of the latex and re-dispersibility after drying. The QCM-D measurements showed that a lower Tg of the core results in a more strongly interacting adsorbed layer at the solid-liquid interface, despite a comparable adsorbed mass, indicating structural differences of the investigated latexes in the wet state. The two latexes with Tg below room temperature (i.e. PBATg-40 and P(BA-co-MMA)Tg3) exhibited film formation in the wet state, as shown by AFM colloidal probe measurements. It was observed that P(BA-co-MMA)Tg3 latex resulted in the largest pull-off force, above 200 m Nm-1 after 120 s in contact. The strongest wet adhesion was achieved with PDMAPMA-stabilized latexes soft enough to allow for interparticle diffusion of polymer chains, and stiff enough to create a strong adhesive joint. Fundamental understanding of interfacial properties of latexes and cellulose enables controlled and predictive strategies to produce strong and tough materials with high nanocellulose content, both in the wet and dry state.
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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".