Internal Dynamics of Pyrene-Labeled Polyols Studied Through the Lens of Pyrene Excimer Formation
Bibliographic record
Abstract
Series of pyrene-labeled diols (Py2-DOs) and polyols (Py-POs) were synthesized by coupling a number (nPyBA) of 1-pyrenebutyric acids to diols and polyols to yield series of end-labeled linear (nPyBA = 2) and branched (nPyBA > 2) oligomers, respectively. Pyrene excimer formation (PEF) between an excited and a ground-state pyrene was studied for the Py2-DO and Py-PO samples by analyzing their fluorescence spectra and decays in tetrahydrofuran, dioxane, N,N-dimethylformamide, and dimethyl sulfoxide. Global model-free analysis (MFA) of the pyrene monomer and excimer fluorescence decays yielded the average rate constant ( ) for PEF. After the calculation of the local pyrene concentration ([Py]loc) for the Py2-DO and Py-PO samples, the -vs.-[Py]loc plots were linear in each solvent, with larger and smaller slopes for the Py2-DO and Py-PO samples, respectively, resulting in a clear kink in the middle of the plot. The difference in slope was attributed to a bias for PEF between pyrenes close to one another on the densely branched Py-PO constructs resulting in lower apparent [Py]loc and values. This study illustrated the ability of PEF to probe how steric hindrance along a main chain affects the dynamic encounters between substituents in multifunctional oligomers such as diols and polyols.
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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 | 0.001 |
| 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".