Effect of Solvent Quality on the Level of Association and Encounter Kinetics of Hydrophobic Pendants Covalently Attached onto a Water-Soluble Polymer
Bibliographic record
Abstract
Pyrene-labeled poly( N, N -dimethylacrylamide)s were prepared by radical copolymerization of N, N -dimethylacrylamide with N -(1-pyrenylmethyl)acrylamide. The progress of the copolymerization reaction was followed by 1 H NMR to ensure that the pyrene-labeled monomer was homogeneously incorporated into the polymer backbone. Since pyrene is hydrophobic and poly( N, N -dimethylacrylamide) is water-soluble, a set of water-soluble associative polymers was generated. The effect of solvent quality toward these associative polymers was investigated. The level of association and the kinetics of encounter between pendants were determined in N, N -dimethylformamide (DMF), acetone, water, and mixtures of acetone and water. Analysis of the fluorescence decays with a blob model yields quantitative results which agree with the qualitative information retrieved by other techniques (static light scattering, intrinsic viscosity, UV−vis absorption). The level of association between hydrophobic pendants was found to be small in acetone (a good solvent for pyrene). It increases when water is added to the solution, since pyrene is insoluble in water. The level of association is smaller in DMF than in acetone, because DMF is a better solvent than acetone for the polymer, and swelling of the polymer coil results in a decrease of the interactions existing between the pyrene groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".