A Case for Using Randomly Labeled Polymers to Study Long-Range Polymer Chain Dynamics by Fluorescence
Bibliographic record
Abstract
The process of excimer formation was studied for a series of pyrene end-labeled polystyrenes (PS(X)-Py 2 where X is the polymer molecular weight equal to 3, 4.5, 8, 12.7, and 14.6 K) and two series of polystyrenes randomly labeled with pyrene (CoE-PS and CoA-PS) in seven different solvents. The solvent viscosities ranged from 0.41 to 1.92 mPa x s, while the solvent quality ranged from good to poor solvents for polystyrene, as determined by intrinsic viscosity measurements. Steady-state fluorescence spectra of the pyrene-labeled polymers were acquired, and the excimer to monomer ratios showed that excimer formation increased strongly with a decrease in solvent viscosity. The monomer and excimer time-resolved fluorescence decays were also acquired and fitted globally to either the Birks' scheme or the fluorescence blob model (FBM) for the end- or randomly labeled polymers, respectively. All parameters reporting on the long-range polymer chain dynamics (LRPCD) obtained from the analysis of the fluorescence data acquired with the PS(X)-Py 2, CoE-PS, and CoA-PS series yielded virtually identical trends, demonstrating that these fluorescence experiments yield results that are internally consistent with one another. Considering the substantial advantages associated with the preparation and study of randomly labeled polymers, this report presents an appealing case for the use of randomly labeled polymers in the study of LRPCD.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".