Temperature-Sensitive Properties of Poly(<i>N</i>-isopropylacrylamide) Mesoglobules Formed in Dilute Aqueous Solutions Heated above Their Demixing Point
Bibliographic record
Abstract
The kinetics of merging and/or chain exchange between mesoglobules formed in dilute aqueous solutions of fluorescently labeled poly( N -isopropylacrylamides) (PNIPAM) heated above their demixing temperature ( T dem ) were monitored at specific temperatures between 30 and 50 °C via nonradiative energy transfer (NRET) using polymers carrying ∼0.25 mol % of either naphthyl (Np, energy donor) or pyrene (Py, energy acceptor). Dynamic and static light scattering measurements (DSL and SLS) indicated that PNIPAM-Py and PNIPAM-Np solutions form stable mesoglobules when heated above 30 °C and that the size and size distribution of the mesoglobules depend on solution concentration and, more importantly, on the sample thermal history. Fluorescence depolarization measurements performed on mesoglobular solutions of PNIPAM-Np gave the temperature dependence of the probe anisotropy, an indication of the microviscosity sensed by the probe. The results show that samples heated within the ∼31 °C < T < 36 °C range consist of fluidlike particles able to merge and grow in size. At higher temperatures the mesoglobules act as rigid spheres unable to merge upon collision. These observations are interpreted in terms of the various mechanisms invoked to account for the stability of the PNIPAM mesoglobular phase.
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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 | 0.001 |
| 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".