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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".