Understanding Non-Covalent Interactions in Diphenyldiselenide and Diphenylselenide Cocrystals Using a Combined <sup>77</sup>Se Magic-Angle Spinning Solid-State NMR and Quantum Chemical Analysis Approach
Bibliographic record
Abstract
Chalcogen bonds are σ-hole interactions that arise from the net attractive forces between an electron-deficient chalcogen atom (such as selenium) and a Lewis base. In recent years, chalcogen bonds have become important noncovalent interactions, playing a key role in building supramolecular structures and functional materials. Given their significance, there is a continuous interest in gaining a deeper understanding of chalcogen interactions. In this study, we examined systems involving Se-I interactions, where diphenyldiselenide and diphenylselenide serve as selenium sources, while molecular iodine and 1,4-diiodotetrafluorobenzene act as iodine donors. We explore the intricate interplay between selenium's chemical environment and its role in noncovalent interactions, with a focus on Se···I chalcogen bonds and halogen bonds. An interdisciplinary approach combining solid-state NMR, single-crystal X-ray diffraction, and advanced quantum chemical analyses, such as the Quantum Theory of Atoms in Molecules (QTAIM), Non-Covalent Interactions analysis (NCI), the Extended Transition-State Method with Natural Orbitals for Chemical Valence (ETS-NOCV), and Interactive Quantum Atoms (IQA), were used to investigate the electronic and structural factors influencing selenium's behavior. By analyzing the chemical shift tensors, we demonstrate how they are influenced by both halogen and chalcogen bonding roles, in addition to the effects of crystal packing and weak interactions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".