Development of an<sup>19</sup>F NMR Method for the Analysis of Fluorinated Acids in Environmental Water Samples
Bibliographic record
Abstract
This investigation was carried out to evaluate 19F NMR as an analytical tool for the measurement of trifluoroacetic acid (TFA) and other fluorinated acids in the aquatic environment. A method based upon strong anionic exchange (SAX) chromatography was also optimized for the concentration of the fluoro acids prior to NMR analysis. Extraction of the analyte from the SAX column was carried out directly in the NMR solvent in the presence of the strong organic base, DBU. The method allowed the analysis of the acid without any prior cleanup steps being involved. Optimal NMR sensitivity based upon T1 relaxation times was investigated for seven fluorinated compounds in four different NMR solvents. The use of the relaxation agent chromium acetylacetonate, Cr(acac)3, within these solvent systems was also evaluated. Results show that the optimal NMR solvent differs for each fluorinated analyte. Cr(acac)3 was shown to have pronounced effects on the limits of detection of the analyte. Generally, the optimal sensitivity condition appears to be methanol-d4/2M DBU in the presence of 4 mg/mL of Cr-(acac)3. The method was validated through spike and recovery for five fluoro acids from environmentally relevant waters. Results are presented for the analysis of TFA in Toronto rainwater, which ranged from < 16 to 850 ng/L. The NMR results were confirmed by GC-MS selected-ion monitoring of the fluoroanalide derivative.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".