Polymer Nanoparticles via Living Radical Polymerization in Aqueous Dispersions: Design and Applications
Bibliographic record
Abstract
In the past decade, living radical polymerization (LRP) has revolutionized academic research in the fields of free-radical polymerization and materials design. Sophisticated macromolecular architectures, designed for a variety of applications and end-use properties, can now be synthesized using relatively simple LRP chemistries that do not require stringent oxygen or moisture free environments, subzero reaction temperatures, or highly purified reagents. Publications abound not only in the fundamentals of LRP but also its use in designing tailor-made polymers and polymer–hybrid composites. Corporate research organizations have also been actively involved in LRP, with numerous patents being issued annually. Despite the intense research interest, however, comparatively few products have been commercialized, with high process costs being a primary factor. Most commercial free-radical polymerizations are conducted in aqueous dispersions due to significantly lower process costs compared to bulk or solution polymerizations. Successful widespread commercialization of LRP will be advantaged by the development of waterborne processes yielding aqueous dispersions of nanoparticles. Conducting LRP within nanoparticles (i.e., using nanoscale particles as self-contained chemical reactors or “nanoreactors”) enables faster reaction times and if harnessed properly will provide better control over the polymer livingness; it also has the potential in the control of the particle mesostructure and microstructure. Recent progress in LRP dispersions is presented with a discussion of outstanding issues and challenges as well as the outlook for adoption of LRP dispersions by industry.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".