Impact of End-Group Association and Main-Chain Hydration on the Thermosensitive Properties of Hydrophobically Modified Telechelic Poly(<i>N</i>-isopropylacrylamides) in Water
Bibliographic record
Abstract
We examine the influence of the macromolecule chain length on the cloud point temperature ( T cp ) and the temperature of the coil-to-globule transition ( T M ) in aqueous solutions of hydrophobically modified (HM) telechelic poly( N -isopropylacrylamides) (PNIPAM) ranging in concentration from 0.01 to 35 g L -1 (0.1−310 mmol of NIPAM L -1 ). The telechelic HM-PNIPAM samples with n -octadecyl termini were obtained by RAFT polymerization of NIPAM in dioxane in the presence of S -1- n -octadecyl- S ‘-(α,α‘-dimethyl-α‘ ‘- N - n -octadecylacetamide)trithiocarbonate as a chain transfer agent. Their molar mass ( M n ) ranged from 12 000 to 49 000 g mol -1 with a polydispersity index lower than 1.20. The cloud point temperatures, measured by monitoring the temperature-induced changes in scattering intensity, decreased significantly with increasing polymer concentration, this effect being more pronounced with decreasing polymer molar mass. In contrast, the temperature of the PNIPAM chain coil-to-globule collapse (30 ± 1 °C) was only slightly affected by solution concentration and polymer molecular weight. These results are interpreted in terms of the coexistence of two phenomena: association of the n -octadecyl terminal groups and hydration of the PNIPAM chains.
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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".