Highly pH and Temperature Responsive Microgels Functionalized with Vinylacetic Acid
Bibliographic record
Abstract
Temperature-responsive microgels based on poly( N -isopropylacrylamide) (PNIPAM) and functionalized with vinylacetic acid (VAA) are observed to exhibit a host of novel swelling responses compared with equally functionalized microgels prepared using the conventional acrylic acid (AA) and methacrylic acid (MAA) comonomers. VAA−NIPAM microgels are ionized over a narrow pH range and show functional group p K a values which are independent of the degree of ionization. Ionization induces a much larger swelling response in VAA−NIPAM microgels than in the conventional microgels; upon ionization at physiological temperature, VAA−NIPAM swells 3 times more than either AA−NIPAM or MAA−NIPAM. VAA−NIPAM microgels also display sharp, PNIPAM-like thermal deswelling profiles when protonated but, upon ionization, undergo no volume phase transition up to at least 70 °C. The highly responsive and tunable ionization and swelling profiles observed for VAA−NIPAM are consistent with the tendency of VAA to behave as a chain transfer agent, resulting in the incorporation of a large number of well-separated VAA units on highly mobile chain ends at or near the microgel surface. VAA−NIPAM microgels may thus be ideal for use in biomolecule separation, medical diagnostics, and biodelivery applications in which sharp responses to multiple environmental stimuli are required.
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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".