Inverse Miniemulsion ATRP: A New Method for Synthesis and Functionalization of Well-Defined Water-Soluble/Cross-Linked Polymeric Particles
Bibliographic record
Abstract
A new methodology for the synthesis and functionalization of nanometer-sized colloidal particles consisting of well-defined, water-soluble, functional polymers with narrow molecular weight distribution (M(w)/M(n) < 1.3) was developed, utilizing atom transfer radical polymerization (ATRP) of water-soluble monomers in an inverse miniemulsion. The optional introduction of a disulfide-functionalized cross-linker allowed for the synthesis of cross-linked (bio)degradable nanogels. Dynamic light scattering (DLS) and atomic force microscopy (AFM) measurements indicated that these particles possessed excellent colloidal stability. ATRP in inverse miniemulsion led to materials with several desirable features. The colloidal particles preserved a high degree of halogen chain-end functionality, which enabled further functionalization. Cross-linked nanogels with a uniformly cross-linked network were prepared. They were degraded to individual polymeric chains with relatively narrow molecular weight distribution (M(w)/M(n) < 1.5) in a reducing environment. Higher colloidal stability, higher swelling ratios, and better controlled degradability indicated that the nanogels prepared by ATRP were superior to their corresponding counterparts prepared by conventional free radical polymerization (RP) in inverse miniemulsion.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".