Analysis of nitrosamines by capillary electrospray‐high‐field asymmetric waveform ion mobility spectrometry‐MS with programmed compensation voltage
Bibliographic record
Abstract
Emerging disinfection by-products (DBPs) in drinking water are an important public health concern. Certain DBPs, such as nitrosamines, are probable carcinogens, and exposure to halogenated DBPs may lead to birth defects. It is difficult to obtain complete separation of nitrosamines by chromatographic techniques. Thus we explored high-field asymmetric waveform ion mobility spectrometry (FAIMS) as an alternative separation technique for the characterization of individual DBPs in a complex matrix. We first used ESI-FAIMS-MS to separate four nitrosamines: N-nitrosodi-n-butylamine (NDBA), N-nitrosodi-n-propylamine (NDPA), N-nitrosopiperidine (NPip), and N-nitrosodiethylamine (NDEA) in the compensation voltage (CV) spectra. The optimal CVs with a fixed dispersion voltage of -4000 V were found to be -1.2 V (NDBA), 2.7 V (NDPA), 7.5 V (NPip) and 10.1 V (NDEA). In addition, FAIMS-MS effectively reduced the chemical noise and dramatically improved the LODs by as much as tenfold compared to the conventional ESI-MS technique. To further improve sensitivity, an on-line CE system was used in combination with FAIMS-MS to take advantage of the higher ionization efficiency. The calibration curves for the four nitrosamines were linear over a range of 5 ng/mL to 1000 ng/mL with an r(2 )value of 0.9929 to 0.9992. To increase sample throughput, a multiple-injection strategy was developed, in which the CV values were preprogrammed so that the FAIMS device allowed different nitrosamines to pass through at selected time windows. The potential application of the proposed method was demonstrated for the analysis of drinking water samples spiked with nitrosamines.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| 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) | 0.002 | 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".