Single-Use Poly(etheretherketone) Solid-Phase Microextraction–Transmission Mode Devices for Rapid Screening and Quantitation of Drugs of Abuse in Oral Fluid and Urine via Direct Analysis in Real-Time Tandem Mass Spectrometry
Bibliographic record
Abstract
The analysis of oral fluid (OF) and urine samples to detect drug consumption has garnered considerable attention as alternative biomatrices. Efficient implementation of microextraction and ambient ionization technologies for rapid detection of target compounds in such biomatrices creates a need for biocompatible devices which can be implemented for in vivo sampling and easily interfaced with mass spectrometry (MS) analyzers. This study introduces a novel solid-phase microextraction–transmission mode (SPME-TM) device made of poly(etheretherketone) (PEEK) mesh that can rapidly detect prohibited substances in biofluids via direct analysis in real-time tandem MS (DART-MS/MS). PEEK mesh was selected due to its biocompatibility, excellent resistance to various organic solvents, and its ability to withstand relatively high temperatures (≤350 °C). The meshes were coated with hydrophilic–lipophilic-balance particle-poly(acrylonitrile) (HLB-PAN) slurry. The robustness of the coated meshes was tested by performing rapid vortex agitation (≥3200 rpm) in LC/MS-grade solvents and by exposing them to the DART source jet stream at typical operational temperatures (∼250–350 °C). PEEK SPME-TM devices proved to be robust and were therefore used to perform ex vivo analysis of drugs of abuse spiked in urine and OF samples. Excellent results were obtained for all analytes under study; furthermore, the tests yielded satisfactory limits of quantitation (median, ∼0.5 ng mL –1 ), linearity (≥0.99), and accuracy (80–120%) over the evaluated range (0.5–200 ng mL –1 ). This research highlights plastic SPME-TM’s potential usefulness as a method for rapidly screening for prohibited substances in on-site/in vivo scenarios, such as roadside or workplace drug testing, antidoping controls, and pain management programs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".