Development of a Fluorogenic Reactivity Palette for the Study of Nucleophilic Addition Reactions Based on <i>meso</i>-Formyl BODIPY Dyes
Bibliographic record
Abstract
Abstract We describe herein a fluorescence-based assay to characterize and report on nucleophilic addition to carbonyl moieties and highlight the advantages a fluorescence-based assay and multiplex analysis can offer. The assay relies on the fluorogenic properties of meso-formyl boron-dipyrromethene (BODIPY) dyes that become emissive following nucleophilic addition. A reactivity palette is assembled based on the increasing electrophilic character of five meso-formyl BODIPY compounds tested. We show that increasing rates of emission enhancement correlate with the decreasing electrophilic character of BODIPY dyes in the presence of an acid catalyst and a nucleophile. These results are consistent with the rate-limiting step involving activation of the electrophile. Increasing product formation is shown to correlate with the increasing electrophilic character of the BODIPY dyes, as expected based on thermodynamics. In addition to providing rates of reaction, analysis of the fluorescence parameters for the reaction mixtures, including emission quantum yields and fluorescence lifetimes, enables us to determine the extent of reactant conversion at equilibrium (in our case the estimated yield of a transient species) and the presence of different products, without the need for isolation. We anticipate that our reactivity palette approach, combined with the in-depth fluorescence analysis discussed herein, will provide guidelines toward developing fluorogenic assays of reactivity offering multiplex information, beyond fluorescence intensity.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".