Kinetically-Calibrated Solid-Phase Microextraction Using Label-Free Standards and Its Application for Pharmaceutical Analysis
Bibliographic record
Abstract
Pre-equilibrium solid-phase microextraction (PE-SPME) has attracted considerable research attention due to shorter sampling times and better temporal resolution than afforded by equilibrium SPME (E-SPME). However, the calibration of PE-SPME is often time-consuming and requires deuterated calibrants, which if available, are often expensive. To address these challenges, we propose a simple but versatile kinetic calibration method, in which nonisotopic (label-free) compounds of interest can supplant the use of deuterated analogues, and the need to determine partitioning coefficients inherent to earlier procedures has been eliminated. Using this approach, both free and total concentrations of analytes can be simultaneously measured within complex sample systems with high accuracy and precision. This calibration method was validated against established E-SPME and solid-phase extraction techniques through the measurement of selected pharmaceuticals in progressively complex matrixes including inorganic buffers, fish blood, and municipal wastewater effluents. This calibration approach may significantly improve time and cost-effectiveness, while improving the application of the SPME approach within highly dynamic systems.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".