Solid-State 17O NMR, Crystallographic, and Computational Studies of Epoxides and Related Molecules
Bibliographic record
Abstract
The aim of this thesis was to study quadrupolar nuclei using solid-state 17O NMR spectroscopy, X-ray crystallography, and computational methods. This thesis reports the successful synthesis and characterization of three 17O-labelled epoxides, [17O]-(2S*,3S*)-2,3-bis(4-nitrophenyl)oxirane, [17O]-(2S*,3R*)-2,3-bis(4-nitrophenyl)oxirane, and [17O]-2,2,3-triphenyloxirane. The 17O NMR tensors (measured using solid-state 17O NMR spectroscopy and calculated using computational methods) and structural parameters (measured using single crystal X-ray crystallography and calculated using computational methods) of the three epoxides were compared. The dependence of ethereal 17O NMR tensors on bond geometry was computationally analyzed. A new model for visualizing quadrupolar coupling tensor components using valence p-orbital population anisotropies was also developed. This model is a simple adaption to the Townes-Dailey method that allows for the visualization of each quadrupolar coupling tensor component (χii ii = xx, yy, zz) using only a single parameter (ΔPii), whereas the traditional Townes-Dailey method requires three parameters. This new model was demonstrated to deliver comparable quadrupolar coupling tensor components to those calculated using direct g09 NMR calculation methods for 14N, 17O, and 127I. Finally, iodosylbenzene, a candidate for containing the strongest halogen bonds, was studied using solid-state 17O NMR and computational methods. Using computational methods, its σ-hole was quantified and its magnitude was compared to that of other halogen bond donors.
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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 | 0.001 |
| Open science | 0.001 | 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".