Volumetric Studies of Aqueous Polymer Solutions Using Pressure Perturbation Calorimetry: A New Look at the Temperature-Induced Phase Transition of Poly(<i>N</i>-isopropylacrylamide) in Water and D<sub>2</sub>O
Bibliographic record
Abstract
We report the first application of pressure perturbation calorimetry (PPC) to determine the hydration properties of poly( N -isopropylacrylamide) (PNIPAM) in H 2 O and in D 2 O as the solutions undergo a temperature-induced phase transition. The technique, which measures the heat change resulting from a pressure change above a solution of PNIPAM placed in a microcalorimeter cell, yields the temperature dependence of the coefficient of thermal expansion, α p, of the polymer in solution and the change in volume of the solvation layer around the polymer chain. In the temperature ranges below and above the phase transition, α p of PNIPAM in H 2 O increased linearly with temperature. It underwent a sharp increase at the transition temperature, T max, then rapidly decreased. The phase transition was accompanied by an increase in the partial specific volume of the hydrated polymer. This increase was significantly higher for solutions of PNIPAM in D 2 O, compared to H 2 O. A study by PPC of the phase transition of hydrophobically modified PNIPAM samples that undergo micellization in water demonstrated that the hydration of the polymeric micelles varies significantly as a function of the degree of hydrophobic substitution and length of the alkyl group linked to the polymer.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".