Application of the Carr−Purcell Meiboom−Gill Pulse Sequence for the Acquisition of Solid-State NMR Spectra of Spin-<sup>1</sup>/<sub>2</sub> Nuclei
Bibliographic record
Abstract
The quadrupolar Carr−Purcell Meiboom−Gill (QCPMG) pulse sequence has received much attention in the recent literature for use in the rapid acquisition of solid-state NMR spectra of half-integer quadrupolar nuclei. Herein we investigate the application of the CPMG pulse sequence to enhance the signal-to-noise ratio in the static NMR spectra of spin- 1 / 2 nuclei. CPMG is coupled with techniques such as cross-polarization (CP) and two-pulse phase-modulated (TPPM) proton decoupling. The CPMG and CP/CPMG pulse sequences are applied to a series of different NMR nuclides, including 113 Cd, 199 Hg, 207 Pb, 15 N and 109 Ag. Standard 113 Cd CP/MAS, MAS and static NMR spectra of Cd(NO 3 ) 2 ·4H 2 O are compared with corresponding 113 Cd CPMG and CP/CPMG NMR spectra. Piecewise-acquired wide-line CPMG 199 Hg and 207 Pb NMR spectra of (CH 3 COO) 2 Hg and (CH 3 COO) 2 Pb·3H 2 O, which are so broad that they cannot be uniformly excited by a single short pulse, reveal that chemical shielding tensor parameters can be determined from these rapidly acquired spectra more accurately than with corresponding CP/MAS spectra. CP/CPMG NMR is also applied to acquire NMR spectra of low-γ nuclei such as 15 N in 15 NH 4 15 NO 3 (98%-enriched) and 109 Ag in AgSO 3 CH 3 . The signal obtained from CPMG NMR experiments on stationary samples is comparable to corresponding MAS spectra and much higher than for conventional static NMR spectra, providing an interesting alternative for investigating spin- 1 / 2 nuclei with broad powder patterns.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".