Relaxation-Assisted Separation of Overlapping Patterns in Ultra-Wideline NMR Spectra
Bibliographic record
Abstract
Efficient acquisition of high-quality ultra-wideline (UW) solid-state NMR powder patterns in short experimental time frames is challenging. UW NMR powder patterns often possess inherently low signal-to-noise (S/N) and usually overlap for samples containing two or more magnetically distinct nuclides, which obscures spectral features and drastically lowers the spectral resolution. Currently, there is no reliable method for resolving overlapping powder patterns originating from unreceptive nuclei affected by large anisotropic NMR interactions. Herein, we discuss new methods for resolving individual UW NMR spectra associated with magnetically distinct nuclei by exploiting their different relaxation characteristics using 2D relaxation-assisted separation (RAS) experiments. These experiments use a non-negative Tikhonov fitting (NNTF) routine to process high-quality T 1 and T 2 eff relaxation data sets to produce high-resolution, 2D spin-relaxation correlation spectra for both spin-1/2 and quadrupolar nuclei in organic and organometallic solids under static ( i.e ., stationary) conditions. It is found that (i) T 2 eff RAS data sets can be acquired in a fraction of the time required for analogous T 1 RAS data sets, because a time-incremented 2D data set is not required for the former, and (ii) Tikhonov regularization is superior to conventional non-negative least-squares fitting, as it more reliably and robustly results in cleaner separation of patterns based on relaxation time constants.
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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 | 0.000 |
| 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".