Polymerization Induced Self-Assembly of Alginate Based Amphiphilic Graft Copolymers Synthesized by Single Electron Transfer Living Radical Polymerization
Bibliographic record
Abstract
Alginate-based amphiphilic graft copolymers were synthesized by single electron transfer living radical polymerization (SET-LRP), forming stable micelles during polymerization induced self-assembly (PISA). First, alginate macroinitiator was prepared by partial depolymerization of native alginate, solubility modification and attachment of initiator. Depolymerized low molecular weight alginate (∼12 000 g/mol) was modified with tetrabutylammonium, enabling miscibility in anhydrous organic solvents, followed by initiator attachment via esterification yielding a macroinitiator with a degree of substitution of 0.02, or 1-2 initiator groups per alginate chain. Then, methyl methacrylate was polymerized from the alginate macroinitiator in mixtures of water and methanol, forming poly(methyl methacrylate) grafts, prior to self-assembly, of ∼75 000 g/mol and polydispersity of 1.2. PISA of the amphiphilic graft-copolymer resulted in the formation of micelles with diameters of 50-300 nm characterized by light scattering and electron microscopy. As the first reported case of LRP from alginate, this work introduces a synthetic route to a preparation of alginate-based hybrid polymers with a precise macromolecular architecture and desired functionalities. The intended application is the preparation of micelles for drug delivery; however, LRP from alginate can also be applied in the field of biomaterials to the improvement of alginate-based hydrogel systems such as nano- and microhydrogel particles, islet encapsulation materials, hydrogel implants, and topical applications. Such modified alginates can also improve the function and application of native alginates in food and agricultural applications.
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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) | 0.001 | 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".