Heteronuclear Polarization Transfer under Steady-State Conditions: The INEPT-SSFP Experiment
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide NMR finds a wide range of applications, ranging from fundamental chemistry to medical imaging. The technique, however, has an inherently low signal-to-noise ratio (SNR)─particularly when dealing with nuclei having low natural abundances and/or low γs. In these cases, sensitivity is often enhanced by methods that, similar to INEPT, transfer polarization from neighboring 1 Hs via J -couplings. In 1958, Carr proposed an alternative approach to increase NMR sensitivity, which involves generating and continuously detecting a steady-state transverse magnetization, by applying a train of pulses on an ensemble of noninteracting spins. This study broadens Carr’s steady-state free precession (SSFP) framework to encompass the possibility of adding onto it coherent polarization transfers, allowing one to combine the SNR-enhancing benefits of both INEPT and SSFP into a single experiment. Herein, the derivation of the ensuing INEPT-SSFP (ISSFP) sequences is reported. Their use in 13 C NMR and MRI experiments leads to ca. 300% improvements in SNR/ unit time over conventional J -driven polarization transfer experiments, and sensitivity gains of over 50% over 13 C SSFP performed in combination with 1 H decoupling and NOE. These enhancements match well with numerical simulations and analytical evaluations. The conditions needed to optimize these new methods in both spectroscopic and imaging studies are discussed; we also examine their limitations, and the valuable vistas that, in both analytical and molecular imaging NMR, could be opened by this development.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".