High-throughput screening of per- and polyfluoroalkyl substances in human plasma using biocompatible solid-phase microextraction coupled with mass spectrometry via microfluidic open interface
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) are of increasing concern due to their environmental persistence, bioaccumulative nature, and association with adverse health outcomes. The growing need for large-scale monitoring and long-term exposure assessment studies necessitates the development of high-throughput, sustainable analytical methodologies. In this work, a solid-phase microextraction-microfluidic open interface-mass spectrometry (SPME-MOI-MS) platform was developed for the rapid screening of 18 PFAS compounds in human plasma. By bypassing the liquid chromatography separation, the method achieves high-throughput performance with an average analysis time of 3.7 min per sample. A novel SPME coating, comprising hydrophilic-lipophilic balanced mixed-mode weak anion exchange sorbent (HLB-WAX) particles embedded in a polyacrylonitrile (PAN) binder, enabled efficient extraction and effective cleanup of complex biological matrices, facilitating direct MS analysis. The method demonstrated excellent linearity (1-100 ng/mL) and low limits of detection (0.11-0.86 ng/mL) across target PFAS compounds. For practical application, PFOA and PFNA were detected in human plasma samples during these initial investigations, demonstrating the potential of the SPME-MOI-MS approach for large-scale PFAS biomonitoring and exposure assessment.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".