Computational investigations of the electronic structure of molecular mercury compounds: ion-selective sensors
Bibliographic record
Abstract
This thesis presents the basic concepts of electronic structure theory and the chemical properties of mercury. The theoretical foundation of DFT and the consequences of relativity are also introduced. The electronic structure of Hg(II) ions, [Hg(L)n(H2O)m]q (L = HO-, Cl-, HS-, S2-) has been studied. We show, in this thesis, that the charge transfer (that is calculated from the hard-soft-acid-base principle (Pearson’s principle)), the total NBO charge and the interaction energies are strongly correlated. Our studies indicate the effect of the solvent on the global electrophilicity, the charge transfer and consequently the interaction strength between Hg(II) and ligand L. The formation constants, logK, of Hg2+−complexes are calculated. The procedure that we follow in this thesis to calculate the formation constants, logK’s, are in good agreement with the extrapolated experimental values. We introduce and explain why it is important adding water molecules explicitly during the calculations of the logK. The recommended logK value of HgS is 27.2. We examined two different types of organic compounds as sensors for heavy metal ions: lumazine (Lm) and 6-thienyllumazine (TLm). We found that the simple calculation of pKa values using DFT methods and implicit solvent models failed to reproduce the experimental values. However, calculated orbital energies and gas phase acidities both indicate that the compound TLm is inherently more acidic than the parent species Lm. We demonstrate that: (1) we need to take in our consideration the population of each tautomer and conformer during the calculations of the pKa values, and (2) thienyl group has indirect effect on the acidity of the proton on N1 in the uracil ring. Last but not least, the fluorescence spectrum of the sensors (L) and their [(L)nM(H2O)m]2+ complexes (L = Lumazine (Lm) and 6-thienyllumazine (TLm) and M = Cd2+and Hg2+) are calculated using time dependent DFT (TDDFT). The results show that TDDFT is in good agreement with experimental results. This chapter provides a new concept in the design of fluorescence turn-on/off sensors that has wider applicability for other systems. Finally, we provide a summary of the works compiled in this thesis and an outlook on potential future work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".