Probing O–H Bonding through Proton Detected <sup>1</sup> H– <sup>17</sup> O Double Resonance Solid-State NMR Spectroscopy
Bibliographic record
Abstract
The ubiquity of oxygen in organic, inorganic, and biological systems has stimulated the application and development of 17 O solid-state NMR spectroscopy as a probe of molecular structure and dynamics. Unfortunately, 17 O solid-state NMR experiments are often hindered by a combination of broad NMR signals and low sensitivity. Here, it is demonstrated that fast MAS and proton detection with the D-RINEPT pulse sequence can be generally applied to enhance the sensitivity and resolution of 17 O solid-state NMR experiments. Complete 2D 17 O → 1 H D-RINEPT correlation NMR spectra were typically obtained in less than 10 h from less than 10 mg of material, with low to moderate 17 O enrichment (less than 20%). Two-dimensional 1 H– 17 O correlation solid-state NMR spectra allow overlapping oxygen sites to be resolved on the basis of proton chemical shifts or by varying the mixing time used for 1 H– 17 O magnetization transfer. In addition, J -resolved or separated local field (SLF) blocks can be incorporated into the D-RINEPT pulse sequence to allow the direct measurement of one-bond 1 H– 17 O scalar coupling constants ( 1 J OH ) or 1 H– 17 O dipolar couplings ( D OH ), respectively, the latter of which can be used to infer 1 H– 17 O bond lengths. 1 J OH and D OH calculated from plane-wave density functional theory (DFT) show very good agreement with experimental values. Therefore, the 2D 1 H– 17 O correlation experiments, 1 H– 17 O scalar and dipolar couplings, and plane-wave DFT calculations provide a method to precisely determine proton positions relative to oxygen atoms. This capability opens new opportunities to probe interactions between oxygen and hydrogen in a variety of chemical systems.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".