Evaluation of performance and matrix compatibility of mixed mode C18-SCX SPME fibers for compounds with different physicochemical properties
Bibliographic record
Abstract
Solid-phase microextraction (SPME) has evolved significantly since its inception, yet challenges remain in developing coatings fully compatible with LC-MS that combine broad polarity coverage with biocompatibility for complex matrices. This study evaluates mixed-mode C 18 -SCX (strong cation exchange) SPME fibers designed to extract analytes of a wide range of physicochemical properties, addressing limitations in current methodologies. The fibers were tested for extraction efficiency, reproducibility, and matrix compatibility using a group of model compounds with different physicochemical properties, namely, codeine (logP=1.19), carbamazepine (logP=2.45), diazepam (logP=2.82), and propranolol (logP=3.47). Furthermore, the biocompatibility was tested in diverse matrices, such as PBS, blood, plasma, urine, and grape juice. Results demonstrated exceptional inter-fiber reproducibility (RSD ≤ 15%, n =96 fibers) and robust performance in biomatrices, with relative matrix effects primarily governed by analyte binding affinities to matrix macromolecules rather than coating fouling. Absolute matrix effects were negligible (93–111%), underscoring the fibers’ ability to deliver clean extracts for LC-MS analysis. Fiber reusability was validated over three consecutive extractions (RSD ≤ 10%), and morphological integrity was preserved post-extraction, even in challenging matrices like whole blood. This work represents the versatility of mixed-mode SPME fibers for high-throughput bioanalysis, offering a significant advancement for in vivo and in vitro targeted and untargeted applications.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".