Rapid Acquisition of <sup>14</sup>N Solid‐State NMR Spectra with Broadband Cross Polarization
Bibliographic record
Abstract
Nitrogen is an element of utmost importance in chemistry, biology and materials science. Of its two NMR-active isotopes, (14)N and (15)N, solid-state NMR (SSNMR) experiments are rarely conducted upon the former, due to its low gyromagnetic ratio (γ) and broad powder patterns arising from first-order quadrupolar interactions. In this work, we propose a methodology for the rapid acquisition of high quality (14)N SSNMR spectra that is easy to implement, and can be used for a variety of nitrogen-containing systems. We demonstrate that it is possible to dramatically enhance (14)N NMR signals in spectra of stationary, polycrystalline samples (i.e., amino acids and active pharmaceutical ingredients) by means of broadband cross polarization (CP) from abundant nuclei (e.g., (1)H). The BRoadband Adiabatic INversion Cross-Polarization (BRAIN-CP) pulse sequence is combined with other elements for efficient acquisition of ultra-wideline SSNMR spectra, including Wideband Uniform-Rate Smooth-Truncation (WURST) pulses for broadband refocusing, Carr-Purcell Meiboom-Gill (CPMG) echo trains for T2-driven S/N enhancement, and frequency-stepped acquisitions. The feasibility of utilizing the BRAIN-CP/WURST-CPMG sequence is tested for (14)N, with special consideration given to (i) spin-locking integer spin nuclei and maintaining adiabatic polarization transfer, and (ii) the effects of broadband polarization transfer on the overlapping satellite transition patterns. The BRAIN-CP experiments are shown to provide increases in signal-to-noise ranging from four to ten times and reductions of experimental times from one to two orders of magnitude compared to analogous experiments where (14)N nuclei are directly excited. Furthermore, patterns acquired with this method are generally more uniform than those acquired with direct excitation methods. We also discuss the proposed method and its potential for probing a variety of chemically distinct nitrogen environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".