NMR Crystallographic Journey from Light to Heavy Atoms of Mercury(II)-DOTAM Complexes and Extraction of Related Structural Parameters
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Complexes of macrocyclic ligands are routinely used as MRI contrast agents and radionuclide carriers for PET and SPECT diagnostics and radiotherapy. This study explores the structural and electronic environments of two materials containing [Hg(dotam)] 2+ cations, using an integrated approach combining single-crystal X-ray diffraction (SC-XRD), multinuclear solid-state magnetic resonance (ssNMR) spectroscopy ( 13 C, 15 N, 199 Hg), and relativistic density functional theory (DFT) calculations. SC-XRD revealed distinct coordination motifs, including octa- and heptacoordinated [Hg(dotam)] 2+ cations. Scalar and spin–orbit relativistic DFT computations accurately reproduced 13 C and 15 N chemical shifts, with a root-mean-square deviation of ∼0.7 ppm for 13 C and ∼4.8 ppm for 15 N, highlighting the importance of relativistic heavy atom effects. For 199 Hg NMR, relativistic cluster-based methods (ADF/ReSpect) outperformed nonrelativistic approaches. An empirical regression model ( χ̅ ) linked 199 Hg shifts to the coordination number ( CN ) and averaged donor electronegativity (χ̅) ( R 2 = 0.86), enabling rapid structural inference. The isotropic 199 Hg shift correlates with the charge on the Hg atom, influencing the p -type frontier molecular orbitals and their paramagnetic contributions to NMR shielding. This work highlights the potential of 199 Hg NMR as a structural descriptor and offers a strategy for NMR crystallography involving heavy elements with possible implications for catalysis, ionic liquids, and Hg-based pharmaceuticals.
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) | 0.002 | 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".