Structural Identification of Highly Polar Nontarget Contaminants in Drinking Water by ESI-FAIMS-Q-TOF-MS
Bibliographic record
Abstract
Drinking water is a complex mixture that contains thousands of naturally occurring and anthropogenic contaminants. Liquid chromatography-mass spectrometry (LC-MS) methods have gained a tremendous popularity in monitoring nonvolatile, highly polar, and thermally labile components in drinking water. It is well recognized, however, that there are difficulties or limitations of LC-MS methods associated with (1) significant resources (time and effort) involved in sample preparation (preconcentration, fractionation, separation), (2) low screening capacity for target contaminants, and (3) insufficient capabilities for structural identification (elucidation) of nontarget contaminants. Consequently, LC-MS methods are mainly used for the detection of target contaminants (compounds identified in drinking water before), seldom for the structural identification of abundant nontarget pollutants (unidentified pollutants in drinking water), and almost never for the structural identification of nontarget components at a trace level. The paper presents a new method of electrospray ionization high field asymmetric waveform ion mobility spectrometry mass spectrometry (ESI-FAIMS-MS), which can detect a large number of water pollutants in a quick and convenient fashion without preconcentration, fractionation, derivatization, or column separation. Most importantly, the method provides structural identification of nontarget contaminants including species present in drinking water at a sub-parts-per-billion concentration level. The identification of previously unknown contaminants was based on mass measurements of investigated ions and their fragments in mass and tandem mass spectrometry. Elemental compositions of these ions, determined by mass measurements, were used to link dissociation patterns of investigated species with their chemical structures. Characterization of nontarget contaminants of chlorine-treated drinking water by ESI-FAIMS-MS has revealed many previously unknown disinfection byproducts. The most intriguing compound, from a group of highly polar hydroxycarboxylic acids discovered in the study, was the most abundant component of drinking water, glycolic acid. Glycolic acid (toxic to kidneys and associated with a moderate maternal toxicity) has never been considered as a drinking water contaminant, despite the fact that it is present in drinking water at a higher concentration (high ppm) than concentrations of highly polar water pollutants that had attracted most attention in the past. The process of structural elucidation of discovered pollutants, including ultratrace contaminants representing a variety of carboxylic acids, will be presented in detail. The structural identification of highly polar contaminants in drinking water presented in the paper is rarely reported in the literature. The key experimental feature of the ESI-FAIMS-MS method is FAIMS separation, which significantly improves the identification capabilities of mass spectrometry.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".