Effect of Time on the Rate of Long Range Polymer Segmental Intramolecular Encounters
Bibliographic record
Abstract
The kinetics of encounters between the pyrene pendants randomly attached along a polystyrene chain (Py-PS) were monitored with a fluorescence blob model (FBM) as an external quencher was added to the solution to decrease the lifetime of the excited pyrene. The fluorescence decays acquired with the Py-PS samples yielded a measure of the volume Vblob probed by an excited pyrene during its lifetime in the form of N0blob, the number of monomers found within Vblob, and K0blob, which is inversely proportional to Vblob. Both N0blob and K0blob(-1) were found to increase with increasing probing time as the excited pyrene was allowed to probe a larger Vblob volume. The rate constant for pyrene-pyrene encounters was obtained from the product (KblobNblob). (KblobNblob) was found to decrease with increasing probing time, in agreement with scaling arguments suggesting that, as the probing time increases, the exited pyrene probes a larger Vblob where the local concentration of ground-state pyrenes in the polymer coil, [Py]loc, is smaller. K0blob, which is inversely proportional to Vblob, was found to scale as N0blob(alpha), where alpha equaled -1.5 and -1.2 in DMF and THF, respectively. The alpha exponents found for the Py-PS samples are in the same range as those found for other polymers exhibiting a random polymer coil conformation in solution and where much smaller than those obtained with more compact structured alpha-helical polypeptides randomly labeled with pyrene. Master curves were also constructed that describe how K0blob and the product (KblobNblob) scale as a function of solvent viscosity, probing time, and N0blob. These scaling laws illustrate the opposite effects that probing time and viscosity have on N0blob, V0blob, and the product (KblobNblob).
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".