Practical Aspects of Recording Ultra-Wideline NMR Patterns under Magic-Angle Spinning Conditions
Bibliographic record
Abstract
Many NMR-active nuclei give rise to solid-state NMR spectra that span extremely large frequency regions due to the effects of large anisotropic NMR interactions; such spectra, which can range from 250 kHz to several MHz in breadth, have been termed ultra-wideline (UW) NMR spectra. UWNMR spectra are often too broad to be uniformly excited by conventional pulse sequences that implement rectangular radiofrequency (RF) pulses. Therefore, they are typically acquired with specialized pulse sequences and frequency-swept (FS) pulses; however, such experiments are conducted predominantly upon stationary samples (i.e., static NMR with no magic-angle spinning, MAS). Herein, we demonstrate how to implement Carr–Purcell Meiboom–Gill (CPMG) type pulse sequences that utilize rectangular pulses to acquire high-quality wideline and UWNMR spectra under MAS conditions, which are useful for providing uniformly excited patterns with substantial signal enhancements in comparison to conventional MAS NMR spectra and identifying peaks and/or patterns corresponding to magnetically nonequivalent sites. We discuss the pulse timings, delays, and the duration of windowed acquisition periods that are necessary for using CPMG-type pulse sequences for T 2 -dependent enhancement under MAS conditions while allowing for chemical shift resolution and maintaining a conventional spinning-sideband (SSB) manifold, as well as protocols for processing these spectra. Careful consideration is given to pulse lengths and RF amplitudes used in these pulse sequences. Using several spin- 1 / 2 (i.e., 119 Sn, 207 Pb, 195 Pt) nuclei and one integer-spin quadrupolar nucleus (i.e., 2 H), we show how sensitivity-enhancing protocols, including CPMG and cross-polarization (CP), can be used to deliver high-quality MAS NMR spectra, which feature high signal-to-noise ( S / N ) ratios and uniformly excited SSB manifolds. The methods outlined herein are facile to implement and offer the potential to open up MAS NMR experiments to a wide variety of elements from across the periodic table.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".