Controlling the Assembly of Nanoparticle Mixtures With Two Orthogonal Polymer Complexation Reactions
Bibliographic record
Abstract
Self-assembly from mixed dispersions of three sizes of monodisperse polystyrene nanoparticles, large (L), medium (M), and small (S), was controlled by coating each particle type with either a monofunctional or bifunctional polymer capable of participating in specific complexation reactions. The complexation reactions were (1) complexation between phenolic polymers and polyethylene glycol (PEG) containing polymers and (2) condensation of phenylboronic acid containing polymers with polyols. These complexation reactions function independently and can be "turned off" independently; phenylboronic acid complexation was reversed by lowering the pH, whereas the interactions of phenolic copolymers with PEG copolymers could be reversed by adding excess PEG homopolymer. The specificity and reversibility of the interactions was demonstrated by the formation of simple binary aggregates from mixtures. The bifunctional copolymers were poly(vinyl phenol-co-diallyldimethyl ammonium chloride), Ph-DADMAC, and poly(3-acrylamide phenylboronic acid-co-PEG methacrylate), PBA-PEG. The monofunctional polymer was polyvinylalcohol, PVA. Ph-DADMAC forms complexes with PBA-PEG (H-bonding) and with anionic surfaces or polymers (electrostatic/polyelectrolyte complexation). PBA-PEG complexes with Ph-DADMAC (H-bonding) and with PVA (boronate ester formation). PVA does not interact with Ph-DADMAC; therefore, PVA coated particles do not deposit onto Ph-DADMAC coated particles.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".