Detecting<i>N</i>-Nitrosamines in Drinking Water at Nanogram per Liter Levels Using Ammonia Positive Chemical Ionization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Detection of N-nitrosamines in water supplies is an environmental and public health issue because many N-nitrosamines are classified as probable human carcinogens. Some analytical methods are inadequate for detecting N-nitrosodimethylamine (NDMA) at low ng/L concentrations in water due to poor extraction efficiencies and nonselective and nondistinctive GC/MS electron ionization techniques. Development of a selective, sensitive, and affordable benchtop analytical method for eight N-nitrosamines, at relevant drinking water concentrations was the primary objective of this project. A solid-phase extraction method using Ambersorb 572 and LiChrolut EN was developed in conjunction with GC/MS ammonia positive chemical ionization (PCI). Ammonia PCI shows excellent sensitivity and selectivity for N-nitrosamines, which were quantified using both isotope dilution/surrogate standard and internal standard procedures. Method detection limits for all investigated N-nitrosamines ranged from 0.4 to 1.6 ng/L. Applying our extraction method to authentic drinking water samples with dissolved organic carbon concentrations of 9 mg/L, we were able to detect N-nitrosodimethylamine (2-180 ng/L) as well as N-nitrosopyrrolidine (2-4 ng/L) and N-nitrosomorpholine (1 ng/L), two N-nitrosamines that have not been reported in drinking water to date. With high recoveries of standards and analytes, the described internal standard method offers a valuable new approach for investigating several N-nitroso compounds at ultratrace levels in drinking water.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it