Correlating Pyrene Excimer Formation with Polymer Chain Dynamics in Solution. Possibilities and Limitations
Bibliographic record
Abstract
Four types of pyrene-labeled polystyrene samples (Py−PS) were prepared and the process of excimer formation between the pyrene labels was characterized by steady-state and time-resolved fluorescence to assess the effect the mode of pyrene incorporation into a polymer has on the kinetics of excimer formation. The pyrene label was incorporated into the PS backbone by either (1) reacting 1-pyrenemethoxide with a chloromethylated polystyrene backbone to yield the GrE−PS series, (2) copolymerizing styrene with 4-(1-pyrenyl)methoxymethylstyrene to yield the CoE−PS series, (3) copolymerizing styrene with N -(1-pyrenylmethyl)acrylamide to yield the CoA−PS series, or (4) polymerizing α,ω-dicarboxyl end-capped polystyrenes with l -lysine-1-pyrenemethylamide dihydrochloride to yield the ES−PS series. Steady-state and time-resolved fluorescence experiments demonstrated that the long and flexible linker of GrE−PS and CoE−PS enabled more efficient excimer formation than the short and rigid linker of CoA−PS, and that spacing the pyrene pendants in ES−PS led to a strong reduction in excimer formation. The fluorescence blob model (FBM) was applied to analyze quantitatively the monomer and excimer fluorescence decays of the four Py−PSs. The FBM analysis confirmed that the longer ether linker of GrE−PS and CoE−PS enabled the excited pyrene label to probe a larger volume inside the polymer coil. The level of clustering of the pyrene pendants was found to be minimal for ES−PS, as expected from its structural design. Interestingly, the pyrene pendants were twice more clustered for GrE−PS than for CoE−PS, despite both polymers having an identical chemical structure. The results for the GrE−PS and CoE−PS series suggest that reacting groups distribute themselves differently in a copolymer whether they are incorporated by a grafting onto reaction or copolymerization.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".