Electrostatic Surface Potentials and Chalcogen‐Bonding Motifs of Substituted 2,1,3‐Benzoselenadiazoles Probed via <sup>77</sup>Se Solid‐State NMR Spectroscopy
Bibliographic record
Abstract
Abstract Chalcogen bonds (ChB) are moderately strong, directional, and specific non‐covalent interactions that have garnered substantial interest over the last decades. Specifically, the presence of two σ‐holes offers great potential for crystal engineering, catalysis, biochemistry, and molecular sensing. However, ChB applications are currently hampered by a lack of methods to characterize and control chalcogen bonds. Here, we report on the influence of various substituents (halogens, cyano, and methyl groups) on the observed self‐complementary ChB networks of 2,1,3‐benzoselenadiazoles. From molecular electrostatic potential calculations, we show that the electrostatic surface potentials (ESP) of the σ‐holes on selenium are largely influenced by the electron‐withdrawing character of these substituents. Structural analyses via X‐ray diffraction reveal a variety of ChB geometries and binding modes that are rationalized via the computed ESP maps, although the structure of 5,6‐dimethyl‐2,1,3‐benzoselenadiazole also demonstrates the influence of steric interactions. 77Se solid‐state magic‐angle spinning NMR spectroscopy, in particular the analysis of the selenium chemical shift tensors, is found to be an effective probe able to characterize both structural and electrostatic features of these self‐complementary ChB systems. We find a positive correlation between the value of the ESP maxima at the σ‐holes and the experimentally measured 77Se isotropic chemical shift, while the skew of the chemical shift tensor is established as a metric which is reflective of the ChB binding motif.
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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) | 0.001 | 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".