Quantifying the Presence of Unwanted Fluorescent Species in the Study of Pyrene-Labeled Macromolecules
Bibliographic record
Abstract
In order to mimic the effect that unwanted fluorescent species have on the process of excimer formation between pyrene labels covalently attached onto macromolecules, the steady-state fluorescence spectra and time-resolved fluorescence decays were acquired for mixtures of pyrene monolabeled and doubly end-labeled 2K poly(ethylene oxide) referred to as Py(1)-PEO(2K) and Py(2)-PEO(2K), respectively, and mixtures of 1-pyrenebutyric acid (PyBA) and a fourth generation dendron end-capped with pyrene (Py(16)-G4-PS). Monolabeled polymers like Py(1)-PEO(2K) and unattached fluorescent labels like PyBA are among the most typical fluorescent impurities that are encountered in the study of fluorescently labeled macromolecules. Our fluorescence experiments revealed that addition of minute amounts of Py(1)-PEO(2K) or PyBA to, respectively, Py(2)-PEO(2K) or Py(16)-G4-PS solutions induced a dramatic reduction of the ratio of the fluorescence intensity of the pyrene excimer to that of the pyrene monomer, namely the I(E)/I(M) ratio. Although the extreme sensitivity of fluorescence in general and the I(E)/I(M) ratio in particular to the presence of fluorescent impurities is a great concern, it is nevertheless reassuring that this effect can be quantitatively accounted for by analyzing the fluorescence decays of the pyrene monomer and excimer globally, according to a protocol which is described in detail in this study. The experiments presented herein demonstrate the importance of studying fluorescently labeled macromolecules that are of the highest purity when probing the rapid internal dynamics of a macromolecule by fluorescence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".