Fluorescence Enhancement/Quenching Based on Metal Orbital Control: Computational Studies of a 6-Thienyllumazine-Based Mercury Sensor
Bibliographic record
Abstract
To understand the sensing behaviors of molecular fluorescent probes, lumazine (Lm) and 6-thienyllumazine (TLm) and their complexation with metal(II) ions ([(L)nM(H2O)m](2+), M = Cd(2+) and Hg(2+)) were examined by scalar relativistic density functional theory (DFT). A red shifting from L to [(L)nM(H2O)m](2+) was found. This is due to the metal affinity that stabilizes the LUMOs of [(L)nM(H2O)m](2+) greater than the HOMOs. Singlet excited-state structures of L and [(L)nM(H2O)m](2+) (M = Cd(2+) and Hg(2+)) were fully optimized using time-dependent DFT (TDDFT). Their fluorescent emissions in aqueous solution were calculated to be 371 nm (Lm), 439 nm (cis-TLm), and 441 nm (trans-TLm), agreeing with experimental values of 380 nm for Lm and 452 nm for TLm. Theoretical support is presented for a sensing mechanism of photoinduced charge transfer of the L probe. The mechanism of the chelation-enhanced fluorescence (CHEF) and the chelating quenched fluorescence (CHQF) is explained. Fluorescence amplification (for Cd(2+)) is due to blocking of the nitrogen lone pair orbital due to the stabilizing interaction with the vacant s-orbital of the metal ion, while fluorescence quenching (Hg(2+)) results from the energy of the LUMO of the metal ion being between HOMO and LUMO of the sensor. Effects of structure rearrangements on the fluorescence spectra of the sensors are insignificant. This proposed mechanism of metal orbital controlled fluorescence enhancement/quenching suggests a development concept in the future design of fluorescent turn-on/off sensors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".