Pyridinium‐Based Schiff‐Base Fluorescent Chemosensor for Sequential Detection of Al<sup>3+</sup> Ions and TNP: Applications in Cell Imaging and Latent Fingerprint Visualization
Bibliographic record
Abstract
Abstract A fluorescent pyridinium‐based chemosensor (E)‐1‐(2‐(3‐hydroxy‐4‐((pyridin‐2‐ylimino) methyl) phenoxy) ethyl) pyridin‐1‐ium bromide (BzPySB) was synthesized and characterized using various spectroscopic techniques. The chemosensing potential of BzPySB was explored using UV‐vis and fluorescence spectroscopy in the aqueous medium. The turn‐on fluorescence behavior was observed for BzPySB in the presence of Al3+, while other metal ions were non‐responsive. The B−H and Job′s plot confirmed the 1 : 1 stoichiometric ratio of the BzPySB and Al3+. The in situ generated complex BzPySB‐Al3+ offered selectivity toward TNP via fluorescence turn‐off phenomena with high Ksv and LOD values. The “off‐on‐off” sensing mechanism was elucidated through 1H NMR, mass spectrometry, and DFT calculations. The probe also detected Al3+ in plant and MCF‐7 cells, highlighting its potential in biological systems. Moreover, BzPySB exhibited solid‐state luminescent properties credited to weak π‐π interaction, leading to its successful application in the visualization of latent fingerprints.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".