Recent developments in cocrystal engineering <i>via</i> σ-hole interactions. Synergies from X-ray diffraction, solid-state NMR and computational chemistry
Bibliographic record
Abstract
Non-covalent -hole interactions, including e.g., halogen bonds, chalcogen bonds, pnictogen bonds, and tetrel bonds, offer a substantial range of cocrystal engineering opportunities in part owing to their tuneability and directionality.Simultaneously, the diversity of elements capable of acting as -hole donor and acceptor atoms presents interesting opportunities and challenges for study via solid-state nuclear magnetic resonance, given the broad array of isotopic properties associated with these elements including their natural abundances, nuclear quadrupole moments, and magnetogyric ratios (e.g., 13 C, 15 N, 17 O, 19 F, 77 Se, 125 Te, 127 I, 121/123 Sb, etc.).In this talk, I will provide a survey of our recent efforts to engineer, characterize, and understand the structural chemistry of several novel cocrystals featuring halogen bonds (Fig. 1), chalcogen bonds (Fig. 2), pnictogen bonds, and/or tetrel bonds [1][2][3][4][5][6].Synthetic methods include slow evaporation, ball milling, gentle mechanochemical grinding, and resonant acoustic mixing.Efforts are made to understand the relationship between experimentally measured NMR and NQR parameters, e.g., quadrupolar coupling and chemical shift tensors, with the aid of advanced density functional theory computations and X-ray diffraction data.Such relationships can be difficult to quantify due to the important role of crystal packing effects and of relativity on the NMR parameters.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".