Effect of Viscosity on Long-Range Polymer Chain Dynamics in Solution Studied with a Fluorescence Blob Model
Bibliographic record
Abstract
Fluorescence dynamic quenching experiments were conducted on three series of pyrene-labeled polystyrenes in nine organic solvents to evaluate the effect that viscosity has on their long-range polymer chain dynamics (LRPCD). Two series of polystyrenes were randomly labeled with the chromophore pyrene by either a short and rigid amide linker for the CoA-PS series or a long and flexible ether linker for the CoE-PS series. The third series was obtained by end-labeling five monodisperse polystyrenes with pyrene. The monomer and excimer fluorescence decays of all pyrene-labeled polymers were acquired and analyzed with the fluorescence blob model (FBM) for the randomly labeled polymers and the Birks’ scheme for the end-labeled polymers. The FBM analysis yielded the rate of excimer formation inside a blob, k blob, and the size of a blob, N blob . Birks’ scheme analysis yielded the rate of end-to-end cyclization, k cy, for a polystyrene chain length equal to N . After normalization, the products k blob × N blob for the randomly labeled polystyrenes and k cy × N for the end-labeled polystyrenes were found to yield identical trends, confirming that any pyrene-labeled polystyrene construct reports the same information on the LRPCD of the polystyrene backbone. The products k blob × N blob and k cy × N increased linearly with the inverse of viscosity, η −1, for η < 1 mPa·s as expected for a diffusion-controlled process. However, the trends obtained with k blob × N blob and k cy × N did not pass through the origin when η −1 → 0, suggesting that excimer formation is more efficient than expected in high-viscosity solvents. N blob was found to decrease with increasing viscosity. k blob did not change much with viscosity in all but the most viscous solvent. The product η × k blob was found to scale as ( N blob ) −1.73, where the exponent of −1.73 agrees with that expected from Flory’s theoretical predictions.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| 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".