Graft modification of starch nanoparticles with pH‐responsive polymers via nitroxide‐mediated polymerization
Bibliographic record
Abstract
Abstract The grafting to approach and nitroxide‐mediated polymerization (NMP) were used to graft modify starch nanoparticles (SNP) with pH‐responsive polymers. SG1‐capped poly(2‐(dimethylamino)ethyl methacrylate‐co‐styrene), P(DMAEMA‐co‐S), and poly(2‐(diethylamino)ethyl methacrylate‐co‐styrene), P(DEAEMA‐co‐S), with relatively low dispersity and high degree of livingness was synthesized in bulk via NMP using a commercial available alkoxyamine. These macroalkoxyamines were then grafted to vinyl benzyl‐functionalized SNP (SNP‐VBC) to obtain pH‐responsive materials. The grafted SNP were characterized by proton nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy, thermogravimetric analysis, and elemental analysis confirming the successful synthesis of these new materials. Low grafting efficiencies (~6%) were observed for both SNP‐grafted materials with pH‐responsive polymers, as expected when using the grafting to approach. The pH‐responsiveness of SNP‐g‐P(DMAEMA‐co‐S) and SNP‐g‐P(DEAEMA‐co‐S) was confirmed by measuring the ζ‐potential at different pH values. At acidic conditions (pH 3–6) the grafted materials were protonated and exhibited positive ζ‐potential, whereas at basic conditions (pH 10–13) the same grafted materials were deprotonated and exhibited negative ζ‐potential.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".