The polarity sensitivity factor of some fluorescent probe molecules used for studying supramolecular systems and other heterogeneous environments
Bibliographic record
Abstract
Fluorescence spectroscopy provides an excellent technique for investigating heterogeneous systems, due to its high sensitivity and the large effect of the local environment on molecular emission. In addition, the use of polarity-sensitive fluorescent probes as guests in supramolecular host–guest inclusion complexes can be exploited in fluorescent sensors. This paper identifies, tabulates, and quantifies a series of useful polarity-sensitive fluorescent probes, with a wide range of polarity-dependent fluorescence responses. The degree of polarity sensitivity is quantified using the polarity sensitivity factor (PSF), developed in our laboratory. In most cases, such polarity-sensitive probes show increased emission as the local polarity is decreased (PSF > 1); 10 such probes are described. However, less commonly, “reverse polarity dependence” can occur in which probe emission decreases with decreasing polarity (PSF < 1); four such probes are described. The mechanism for the observed polarity-induced fluorescence changes will also be discussed in selected representative cases. The purpose of this paper is to present details on a broad arsenal of polarity-sensitive fluorescence probes with varying properties, with potentially useful applications in the study of heterogeneous systems, including inclusion phenomena, and in practical applications such as fluorescent sensors, which will be useful to researchers studying supramolecular and other heterogeneous systems using fluorescence spectroscopy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".