Long-Range Polymer Chain Dynamics of Pyrene-Labeled Poly(<i>N</i>-isopropylacrylamide)s Studied by Fluorescence
Bibliographic record
Abstract
The long-range polymer chain dynamics (LRPCD) of poly( N -isopropylacrylamide) (PNIPAM) were studied by steady-state and time-resolved fluorescence. Two types of fluorescently labeled PNIPAM constructs were considered. One polymer set, named Py 2 –PNIPAM–Y, where 5.6 kDa < Y < 44 kDa is the molecular weight of the polymer, consisted of five monodisperse PNIPAMs that carried 1-pyrenyl-butyl groups at each chain end (functionalization level >75%). The second polymer set consisted of five polydisperse PNIPAMs 75 kDa < M n < 104 kDa randomly labeled with pyrene (0.1 mol % < pyrene content < 6.0 mol %). Pyrene was selected as its ability to form an excimer from the encounter of two pyrenyl pendants covalently attached onto the polymer yields quantitative information about the LRPCD of a given backbone. The fluorescence data were analyzed according to the Birks scheme for the pyrene end-labeled PNIPAMs and the fluorescence blob model (FBM) for the randomly labeled PNIPAMs. The parameters describing the process of pyrene excimer formation were found to yield equivalent trends regardless of the PNIPAM construct as would be expected since both PNIPAM constructs are essentially pyrene-labeled PNIPAM in nature. Comparison of the parameters describing the LRPCD of PNIPAM and polystyrene obtained with either end- or randomly labeled polymers led to the conclusion that PNIPAM and polystyrene have similar LRPCD in solution as expected from the similarity in bulkiness of their side-chain. This study confirms the claim made first with polystyrene that randomly labeled polymers can be used to obtain quantitative information on the LRPCD of a given polymeric backbone and that they constitute an appealing alternative to end-labeled polymers.
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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".