pH Responsivity and Micelle Formation of Gradient Copolymers of Methacrylic Acid and Methyl Methacrylate in Aqueous Solution
Bibliographic record
Abstract
A series of gradient copolymers of methacrylic acid (MAA)/methyl methacrylate (MMA) with four end-to-end composition profiles (uniform, linear gradient, triblock with linear gradient midblock, and diblock) but all having an average chain composition of ̅F(MMA) ≈ 0.5 and an average chain length of 200 were synthesized via model-based, computer-programmed, semibatch atom-transfer radical copolymerization (ATRcoP). These samples allowed us to investigate systematically the effects of the gradient composition profile on the pH responsivity and micelle formation of the copolymers in an aqueous solution. Measurements included light transmittance, TEM, AFM, DLS, (1)H NMR, and pH titration. It was found that linear gradient, triblock, and diblock copolymers formed spherical micelles at high pH. The micelles of the linear gradient copolymer contained MMA units in their hydrophilic shells, and those of the triblock and diblock copolymers had all of their MMA units residing in their cores. The composition profile showed a strong effect on the degree of acid dissociation at a given pH. The conformational transition of the copolymer chains was determined by both the pH value and composition profile. Copolymers having sharper gradients required a lower pH to trigger the conformational transition and a narrower pH range to complete the transition.
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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 | 0.001 |
| 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".