Broadband adiabatic inversion cross‐polarization to integer‐spin nuclei with application to deuterium NMR
Bibliographic record
Abstract
Abstract Solid‐state NMR (SSNMR) spectroscopy of integer‐spin quadrupolar nuclei is important for the molecular‐level characterization of a variety of materials and biological solids; of the integer spins, 2 H ( S = 1) is by far the most widely studied, due to its usefulness in probing dynamical motions. SSNMR spectra of integer‐spin nuclei often feature very broad powder patterns that arise largely from the effects of the first‐order quadrupolar interaction; as such, the acquisition of high‐quality spectra continues to remain a challenge. The broadband adiabatic inversion cross‐polarization (BRAIN‐CP) pulse sequence, which is capable of cross‐polarization (CP) enhancement over large bandwidths, has found success for the acquisition of SSNMR spectra of integer‐spin nuclei, including 14 N ( S = 1), especially when coupled with Carr–Purcell/Meiboom–Gill pulse sequences featuring frequency‐swept WURST pulses (WURST‐CPMG) for T 2 ‐based signal enhancement. However, to date, there has not been a systematic investigation of the spin dynamics underlying BRAIN‐CP, nor any concrete theoretical models to aid in its parameterization for applications to integer‐spin nuclei. In addition, the BRAIN‐CP/WURST‐CPMG scheme has not been demonstrated for generalized application to wideline or ultra‐wideline (UW) 2 H SSNMR. Herein, we provide a theoretical description of the BRAIN‐CP pulse sequence for spin‐1/2 → spin‐1 CP under static conditions, featuring a set of analytical equations describing Hartmann–Hahn matching conditions and numerical simulations that elucidate a CP mechanism involving polarization transfer, coherence exchange, and adiabatic inversion. Several experimental examples are presented for comparison with theoretical models and previously developed integer‐spin CP methods, demonstrating rapid acquisition of 2 H NMR spectra from efficient broadband CP.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".