Diazaboryl‐naphthyl‐ketone: A New Scaffold with Bright Fluorescence, Aggregation‐Induced Emission, and Application in the Quantitation of Trace Boronic Acids in Drug Intermediates
Bibliographic record
Abstract
This study describes the synthesis, structure, and photophysical properties of a new luminescent polyaromatic boronic acid scaffold, diazaboryl-naphthyl-ketones (DNKs). These stable compounds display extremely bright fluorescence, aggregation-induced emission, positive solvatochromism, and solid-state fluorescence. DFT calculations and X-ray crystallographic study revealed notable electronic and structural differences between these compounds and the parent diaminonaphthalene (DAN) adducts. Acylation of the DAN system causes a localization of both HOMO and LUMO onto the DNK unit, which validates the negligible influence of the B-aryl substituent. The LUMO energy is lowered, and its shape significantly altered. Photophysical data in solution and the solid state revealed blue-shifted, narrowed, and intense emissions for DNKs (up to 89 % quantum yield). The potential utility of the fluorogenic DNK system was demonstrated with a proof-of-concept for the determination of trace boronic acid contaminants in solid samples, down to one-ppm level, using HPLC with fluorescence detection. This method could be useful in pharmaceutical development for the quantitation of difficult-to-detect and potentially mutagenic residual boronic acid from late cross-coupling reactions in drug syntheses.
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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) | 0.000 | 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 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".