How switching the substituent of a pyrene derivative from a methyl to a butyl affects the fluorescence response of polystyrene randomly labeled with pyrene
Bibliographic record
Abstract
A series of polystyrenes randomly labeled with 1-pyrenebutanol were prepared by copolymerizing styrene and 1-pyrenebutylacrylate yielding the CoBuE–PS series. Solutions of CoBuE–PS were prepared in nine organic solvents having viscosities ranging from 0.36 to 5.5 mPa·s and the fluorescence spectra and pyrene monomer and excimer fluorescence decays were acquired. Analysis of the fluorescence spectra yielded the IE/IM ratio, whereas analysis of the fluorescence decays with the fluorescence blob model (FBM) yielded the parameters N blobo , blob × Nblob> , and k blobo . These parameters were compared to those obtained with two other series of pyrene-labeled polystyrenes, which had been studied earlier, namely CoA–PS and CoE–PS where pyrene was attached to the polymer backbone via a methylamide and benzyl methylether linker, respectively. Although the parameters IE/IM, N blobo , blob × Nblob>, and k blobo took different values according to the specific nature of the linker connecting pyrene to the polystyrene backbone, they exhibited trends that were quite similar for all the pyrene-labeled polystyrene constructs. The excellent agreement between the parameters retrieved for the three different types of pyrene-labeled polystyrenes suggests that the FBM accounts satisfyingly for differences in the nature of the label used, while still retrieving information pertinent to the polymer of interest.
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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".