Two-Dimensional LC × DMS Analysis of 34 PFAS Compounds
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) are ubiquitous environmental pollutants that pose potential risks to ecosystems and human health. Prior to mass spectrometric analysis of environmental samples, it is necessary to separate PFAS from compounds that can cause ion suppression and compromise analyte identification and quantification accuracy. Although liquid chromatography–mass spectrometry (LC–MS) is the gold standard for PFAS trace analysis, some PFAS species still coelute in the LC dimension and could benefit from an orthogonal dimension of separation. Moreover, an additional orthogonal dimension of separation could potentially aid in the identification of unknown fluorinated species (e.g., those identified within a specified mass-defect range). Here, we investigate the sequential use of LC and differential mobility spectrometry (DMS) separation to analyze 34 PFAS species. Upon incorporating DMS in a two-dimensional (2D) separation scheme, we observed baseline resolution of 29 compounds in the 2D LC × DMS space, with partial resolution of the remaining five. In comparison, only five PFAS compounds were baseline-resolved in 1D LC experiments. Because DMS measurements can be acquired in milliseconds, targeted 2D LC × DMS–MS 2 analyses operate on the same time scale as LC–MS 2 analysis. However, the limits of quantitation for PFAS using the 2D LC × DMS–MS 2 method are slightly higher than those achieved by the state-of-the-art LC–MS 2 method owing to ion fragmentation within the energetic DMS environment. Nevertheless, distinct trends observed in the 2D separation space for the various PFAS subclasses will facilitate analyte identification of unknown species in future nontargeted analyses. Finally, we assessed the feasibility of our method for quantifying PFAS in a series of wastewater samples obtained from a Southern Ontario wastewater treatment plant. We were successful in quantifying PFOS, although the concentrations determined were consistently higher than those measured with LC–MS 2 .
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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 | 0.000 | 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 | 0.001 |
| 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".