Practical Aspects of Modern Routine Solid-State Multinuclear Magnetic Resonance Spectroscopy: One-Dimensional Experiments
Bibliographic record
Abstract
New solid-state NMR (SSNMR) methods and applications continue to blossom such that a diverse array of physical, chemical, and biological problems are now being addressed using a variety of SSNMR experiments. While SSNMR is far from routine for chemists in the manner that a technique such as solution NMR is, there are nevertheless numerous applications of SSNMR which would be beneficial to many “non-specialists”, e.g., synthetic chemists seeking to characterize their materials. This article gathers together practical details for those one-dimensional experiments which, in a broad sense, are considered “routine ” in a modern SSNMR laboratory. Emphasis is placed on providing information and over 300 key references in a manner which will be useful for the novice and the non-specialist. For practicing SSNMR spectroscopists, it is hoped that this article will serve as a valuable reference in the laboratory. In addition to providing a brief review of pulsed Fourier transform NMR, the article discusses experimental details relating to the study of solid samples containing spin- 1 2 nuclei, non-integer quadrupolar nuclei, and deuterium.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".