Double Chalcogen Bonds: Crystal Engineering Stratagems via Diffraction and Multinuclear Solid‐State Magnetic Resonance Spectroscopy
Bibliographic record
Abstract
Abstract Group 16 chalcogens potentially provide Lewis‐acidic σ‐holes, which are able to form attractive supramolecular interactions with electron rich partners through chalcogen bonds. Here, a multifaceted experimental and computational study of a large series of novel chalcogen‐bonded cocrystals, prepared using the principles of crystal engineering, is presented. Single‐crystal X‐ray diffraction studies reveal that dicyanoselenadiazole and dicyanotelluradiazole derivatives work as promising supramolecular synthons with the ability to form double chalcogen bonds with a wide range of electron donors including halides and oxygen‐ and nitrogen‐containing heterocycles. Extensive 77Se and 125Te solid‐state nuclear magnetic resonance spectroscopic investigations of cocrystals establish correlations between the NMR parameters of selenium and tellurium and the local chalcogen bonding geometry. The relationships between the electronic environment of the chalcogen bond and the 77Se and 125Te chemical shift tensors were elucidated through a natural localized molecular orbital density functional theory analysis. This systematic study of chalcogen‐bond‐based crystal engineering lays the foundations for the preparation of the various multicomponent systems and establishes solid‐state NMR protocols to detect these interactions in powdered materials.
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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".