Rapid parameter optimization of low signal‐to‐noise samples in NMR spectroscopy using rapid CPMG pulsing during acquisition: application to recycle delays
Bibliographic record
Abstract
A method is presented that combines Carr-Purcell-Meiboom-Gill (CPMG) during acquisition with either selective or nonselective excitation to produce a considerable intensity enhancement and a simultaneous loss in chemical shift information. A range of parameters can theoretically be optimized very rapidly on the basis of the signal from the entire sample (hard excitation) or spectral subregion (soft excitation) and should prove useful for biological, environmental, and polymer samples that often exhibit highly dispersed and broad spectral profiles. To demonstrate the concept, we focus on the application of our method to T(1) determination, specifically for the slowest relaxing components in a sample, which ultimately determines the optimal recycle delay in quantitative NMR. The traditional inversion recovery (IR) pulse program is combined with a CPMG sequence during acquisition. The slowest relaxing components are selected with a shaped pulse, and then, low-power CPMG echoes are applied during acquisition with intervals shorter than chemical shift evolution (RCPMG) thus producing a single peak with an SNR commensurate with the sum of the signal integrals in the selected region. A traditional (13)C IR experiment is compared with the selective (13)C IR-RCPMG sequence and yields the same T(1) values for samples of lysozyme and riverine dissolved organic matter within error. For lysozyme, the RCPMG approach is ~70 times faster, and in the case of dissolved organic matter is over 600 times faster. This approach can be adapted for the optimization of a host of parameters where chemical shift information is not necessary, such as cross-polarization/mixing times and pulse lengths.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".