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 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".