Sequential Solid-Phase Microextraction with Biocompatible Coating Materials to Address Displacement Effects in the Quantitative Analysis
Bibliographic record
Abstract
Solid-phase microextraction (SPME) is a simple and effective sample-preparation technique for the analysis of complex samples. However, sample matrices containing high concentrations of nonpolar substances or spiked analytes in free form can cause swelling, saturation, and/or competition phenomena in the coating material. This results in a displacement effect wherein polar analytes with low affinities for the solid coating material are displaced by nonpolar substances in the matrix or spiked analytes with a high affinity. Therefore, the quantitative analysis of polar analytes can be challenging, as the displacement effect causes non-linearity in the calibration curves. This paper presents a comprehensive investigation of the conditions under which the displacement effect occurs and how it influences the quantitative analysis of polar analytes. To remedy this issue, a sequential SPME strategy using two SPME blades with different selectivities is applied. SPME blades offer a large surface area and coating volume─and thus, greater extraction capacity─which may mitigate the displacement effect. In addition, the biocompatible coatings on the SPME blades are comprised of small amounts of sorbent particles embedded by a polyacrylonitrile (PAN) binder, which allows them to be directly immersed into complex matrixes such as biological and food samples, as the PAN acts as a barrier that prevents the adsorption of large macromolecules (e.g., cells and proteins). As such, a C18/PAN-coated blade was applied for the first extraction step, which significantly decreased the concentrations of nonpolar compounds in the sample. In the second step, a hydrophilic–lipophilic balanced (HLB)/PAN-coated blade was employed to extract the polar analytes and any remaining nonpolar analytes. The proposed sequential SPME strategy successfully enabled the quantitative determination of polar and nonpolar drugs of abuse with log P values ranging from 0.16 to 4.98 in biological matrices while also providing good linearities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".