A Solid-State <sup>17</sup>O Nuclear Magnetic Resonance Study of Nucleic Acid Bases
Bibliographic record
Abstract
We report a systematic solid-state (17)O NMR study of free nucleic acid bases: thymine (T), uracil (U), cytosine (C), and guanine (G). Site-specifically (17)O-enriched samples were synthesized:[2-(17)O]thymine (1), [4-(17)O]thymine (2), [2-(17)O]uracil (3), [4-(17)O]uracil (4), [2-(17)O]cytosine (5), and [6-(17)O]guanine monohydrate (6). Magic-angle-spinning (MAS) and static (17)O NMR spectra were acquired at 11.75 T for compounds 1-6, from which information about the (17)O chemical shift and electric field gradient tensors was obtained. Extensive quantum chemical calculations were performed at the B3LYP/6-311++G(d,p) level of theory for (17)O NMR properties in various molecular models. The calculated (17)O NMR tensors are highly sensitive to the description of intermolecular hydrogen-bonding interactions at the target oxygen atom. A reasonably good agreement between experimental solid-state (17)O NMR data and B3LYP/6-311++G(d,p) calculations is achievable only in molecular cluster models where a complete hydrogen-bond network is considered. Using this theoretical approach, we also investigated the (17)O NMR tensors in two unusual structures: guanine- and uracil-quartets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".