From well‐defined diblock copolymers prepared by a versatile atom transfer radical polymerization method to supramolecular assemblies
Bibliographic record
Abstract
Abstract The synthesis of well‐defined diblock copolymers by atom transfer radical polymerization (ATRP) was explored in detail for the development of new colloidal carriers. The ATRP technique allowed the preparation of diblock copolymers of poly(ethylene glycol) (PEG) (number‐average molecular weight: 2000) and ionic or nonionizable hydrophobic segments. Using monofunctionalized PEG macroinitiator, ionizable and hydrophobic monomers were polymerized to obtain the diblock copolymers. This polymerization method provided good control over molecular weights and molecular weight distributions, with monomer conversions as high as 98%. Moreover, the copolymerization of hydrophobic and ionizable monomers using the PEG macroinitiator made it possible to modulate the physicochemical properties of the resulting polymers in solution. Depending on the length and nature of the hydrophobic segment, the nonionic copolymers could self‐assemble in water into nanoparticles or polymeric micelles. For example, the copolymers having a short hydrophobic block (5 < degree of polymerization < 9) formed polymeric micelles in aqueous solution, with an apparent critical association concentration between 2 and 20 mg/L. The interchain association of PEG‐based polymethacrylic acid derivatives was found to be pH‐dependent and occurred at low pH. The amphiphilic and nonionic copolymers could be suitable for the solubilization and delivery of water‐insoluble drugs, whereas the ionic diblock copolymers offer promising characteristics for the delivery of electrostatically charged compounds (e.g., DNA) through the formation of polyion complex micelles. Thus, ATRP represents a promising technique for the design of new multiblock copolymers in drug delivery. © 2001 John Wiley & Sons, Inc. J Polym Sci Part A: Polym Chem 39: 3861–3874, 2001
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".