Universal aqueous synthesis of ultra-small polymer-templated nanoparticles: synthesis optimization methodology for counterion-collapsed poly(acrylic acid)
Bibliographic record
Abstract
A long polyelectrolyte chain collapses into a nano-sized particle upon the addition of counterions under appropriate solution conditions. This phenomenon forms the basis for a simple universal method for aqueous synthesis of ultra-small (<10 nm) metal, metal oxide, and other types of nanoparticles in the following manner: the counterion-collapsed polyelectrolyte chains are made stable by crosslinking, effectively trapping the counterions, which are subsequently chemically modified, to form metal nanoparticles via reduction or metal oxides nanoparticles via oxidation, within the collapsed polymer nanoparticle. This highly versatile platform methodology can be applied to almost any polyelectrolyte–counterion pair, making possible the rapid development of syntheses of different nanoparticles within the same chemical environment. Using poly(acrylic acid) as a model system, a methodology for the optimization of conditions for the polyelectrolyte collapse by various mono- and multi-valent metal cations is developed. The optimal counterion concentration did not correlate with ionic strength and metal ion valency and was highly variable from system to system. By monitoring the polyelectrolyte conformation using viscosity and turbidity measurements, the appropriate metal ion concentration for each nanoparticle system was determined.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".