Pre-Equilibrium Solid-Phase Microextraction of Free Analyte in Complex Samples: Correction for Mass Transfer Variation from Protein Binding and Matrix Tortuosity
Bibliographic record
Abstract
The accurate measurement of free analyte concentrations within complex sample matrixes by pre-equilibrium solid-phase microextraction (SPME) has proven challenging due to variations in mass uptake kinetics. For the first time, the effects of the sample binding matrix and tortuosity on the kinetics of analyte extraction (from the sample to the SPME fiber) are demonstrated to be quantitatively symmetrical with those of the desorption of preloaded deuterated standards (from the fiber to the sample matrix). Consequently, kinetic calibration methods can be employed to correct for variation in SPME sampling kinetics, facilitating the application of pre-equilibrium SPME within complex sample systems. This approach was applied ex vivo to measure pharmaceuticals in fish muscle tissues, with results consistent with those obtained from equilibrium SPME and microdialysis. The developed method has the inherent advantages of being more accurate, precise, and reproducible, thus providing the framework for applications where rapid measurement of free analyte concentrations (within complicated sample matrixes such as biological tissues, sediment, and surface water) are required.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".