Steady-State Free Precession (SSFP) NMR Spectroscopy for Sensitivity Enhancement in Complex Environmental and Biological Samples Using Both High-Field and Low-Field NMR
Bibliographic record
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a valuable and complementary tool in environmental research, but it is underutilized due to the cost, size, and maintenance requirements of standard “high-field” NMR spectrometers. “Low-field” NMR spectrometers are a financially and physically accessible alternative, but their lower sensitivity and increased spectral overlap limit the analysis of heterogeneous environmental/biological media, especially with fast-relaxing samples that produce broad, low-intensity spectra. This study therefore investigates the potential of the steady-state free precession (SSFP) experiment to enhance signal-to-noise ratios (SNRs) of fast-relaxing, complex samples at both high- and low-field. SSFP works by obtaining steady-state transverse signal using a train of equally spaced radiofrequency pulses with the same flip angle and a time between pulses less than the transverse relaxation time, allowing for thousands of scans to be summed in a short time period. Here, 13 C-SSFP is applied to samples of varying complexity (egg white, dissolved organic matter, and crude oil) at low-field and at high-field for testing and comparison. The potential of in vivo SSFP NMR is additionally investigated by applying 31 P-SSFP to live Eisenia fetida at high-field. In some samples, SSFP increased 13 C SNR by over 2000% at both high-field and low-field compared to standard 13 C NMR and enabled detection of peaks that were not observable by standard 13 C NMR. Ultimately, SSFP holds great potential for improving analysis of fast-relaxing, complex samples, which could in turn make low-field NMR spectroscopy a more effective tool not only in environmental/biological research but also in numerous other disciplines.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".