Steady State Free Precession NMR without Fourier Transform: Redefining the Capabilities of <sup>19</sup>F NMR as a Discovery Tool
Bibliographic record
Abstract
F NMR holds great potential, however, sensitivity limitations lead to long analysis times and/or the possibility of not detecting low concentration species. Steady State Free Precession (SSFP) NMR collects the signal in a steady state regime, allowing 100's of acquisitions in the timespan of a single traditional NMR scan. Unfortunately, data truncation from SSFP leads to artifacts and spectral broadening with Fourier transform, hindering interpretation. When non-Fourier based time-domain analysis is used, namely, complete reduction to amplitude frequency tables (CRAFT), limitations of SSFP are eliminated while sensitivity gains are retained. This work introduces the combined approach, then applies it for the measurement of PFCs in environmental and biological samples. In all cases, the approach reduces analysis time from many hours to minutes and/or greatly increases the range of compounds detected. For example, when PFOA was spiked into human blood, the detection limit improved ~50-fold vs standard NMR, while in a standard mixture, the approach detected compounds missed by LC-MS/MS. The technique can be adapted to any nucleus providing a facile approach to reduce experiment time and improve sensitivity of NMR in general.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".