Titrametric Characterization of pH-Induced Phase Transitions in Functionalized Microgels
Bibliographic record
Abstract
The chain and radial functional group distributions in carboxylic acid-functionalized poly(N-isopropylacrylamide)-based microgels have significant impacts on the types of swelling responses exhibited by the microgels upon the application of a temperature and/or pH stimulus. Potentiometric, conductometric, and calorimetric titration approaches are used in this work to characterize the chain distributions of -COOH groups in five microgels prepared using different -COOH-functionalized monomers. A direct correlation is observed between the kinetically predicted formation of functional monomer blocks within the microgel, the excess Gibbs free energy of ionization, and the apparent pK(a) versus degree of ionization profiles generated from potentiometric titration. Isothermal titration calorimetry (ITC) can be used to quantify the relative number of functional groups present in microgels prepared with the same functional monomer and/or identify differences between microgels with the same bulk -COOH content but different chain distributions. In particular, microgels prepared with diacid-functionalized monomers exhibit a characteristic two-step ITC profile. For microgels with the same bulk -COOH content, the heat of ionization measured via ITC increases systematically with the overall change in both the pK(a) and the excess Gibbs free energy for microgels prepared with monoacid-functionalized monomers. Diacid monomer-functionalized microgels have lower ionization enthalpies attributable to the break-up of hydrogen-bonded intramolecular ring complexes upon carboxylic acid ionization. The inferred chain functional group distributions can be used to understand differences in microgel swelling across the pH-induced phase 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 | 0.000 |
| 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) | 0.001 | 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".