New Generation of Solid-Phase Microextraction Coatings for Complementary Separation Approaches: A Step toward Comprehensive Metabolomics and Multiresidue Analyses in Complex Matrices
Bibliographic record
Abstract
In this work, a new generation of solid-phase microextraction (SPME) coatings based on polytetrafluoroethylene amorphous fluoroplastics (PTFE AF 2400) as a particle binder is presented. The developed coating was tested for thermal and solvent-assisted desorption, demonstrating its compatibility with both gas- and liquid-chromatographic platforms. The incorporation of hydrophilic-lipophilic balance (HLB) adsorptive particles provided optimal extraction coverage for analytes bearing a broad range of hydrophobicities and molecular weights and of varied chemical diversity. The performance of the newly developed coating was compared to already established coatings based on different polymers such as divinylbenzene/carboxen/polydimethylsiloxane (DVB/Car/PDMS) and octadecyl/benzenesulfonic acid/polyacrylonitrile (C18/SCX/PAN) in order to assess the new prototype versus the existing technology. As this is the first documented instance of PTFE AF being used as a particle immobilizer for SPME, an assessment of the analyte uptake rate and extraction capability of the developed coating was carried out in comparison to other conventionally used polymers. Moreover, the new SPME probes were used to validate an analytical method for determination of banned doping substances, achieving limits of quantitation below the minimum required performance limits (MRPLs) set by the World Anti-Doping Agency (WADA) for most compounds. Considering the broad coverage of the coating in terms of analytes extracted and its suitability for both thermal- and solvent-assisted desorption, these new SPME probes will properly suit various metabolomics applications that involve the use of both gas- and liquid-chromatography.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".