Exploring Negative Chemical Ionization of Per- and Polyfluoroalkyl Substances via a Liquid Electron Ionization LC-MS Interface
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFASs) are a class of aliphatic manufactured compounds comprising fluoro-chemicals with varied functional groups and stable carbon-fluorine bonds. They are defined as "forever chemicals" due to their persistent and bioaccumulative character. These substances have been detected in various environmental samples, including water, air, soil, and human blood, posing significant health hazards. High-performance liquid chromatography coupled with electrospray ionization mass spectrometry (HPLC-ESI-MS) is typically employed for the analysis of PFASs. Negative chemical ionization (NCI) is generally coupled to gas chromatography (GC) and offers high selectivity and sensitivity for compounds containing electronegative atoms, such as PFASs. The liquid electron ionization (LEI) interface is an efficient mechanism developed to robustly couple a liquid flow rate from an LC system to an EI or a CI source. This interface has been successfully utilized for pesticide determination in UHPLC-LEI-CI in negative ion mode (NCI). This work aims to evaluate different parameters involved in the ionization of PFASs analyzed in LC-LEI-NCI and subsequently develop a method for their detection in real samples. The parameters considered for this study include (i) a comparison of different CI reagent gases (methane, isobutane, and argon); (ii) the use of acetonitrile as both the chromatographic solvent and CI reagent gas; (iii) the presence of water and formic acid as chromatographic mobile phase components; and (iv) the mobile phase flow rate. The optimal combination of these parameters led to promising results. Tentative fragmentation pathways of PFASs in NCI mode are proposed based on the dissociative electron capture mechanism.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".