Effect of Binding Components in Complex Sample Matrices on Recovery in Direct Immersion Solid-Phase Microextraction: Friends or Foe?
Bibliographic record
Abstract
The development of matrix compatible coatings for solid-phase microextraction (SPME) has enabled direct extraction of analytes from complex sample matrices. The direct immersion (DI) mode of SPME when utilized in conjunction with such extraction phases facilitates extraction of a wide range of analytes from complex matrices without the incurrence of fouling or coating saturation. In this work, mathematical models and computational simulations were employed to investigate the effect of binding components present in complex samples on the recovery of small molecules varying in logP for extractions carried out using the direct immersion approach. The presented findings corroborate that the studied approach indeed enables the extraction of both polar and nonpolar analytes from complex matrices, provided a suitable sorbent is employed. Further results indicated that, in certain cases, the kinetics of extraction of a given analyte in its free form might be dependent on the desorption kinetics of their bound form from matrix components, which might lower total recoveries of analytes with high affinity for the matrix. However, the binding of analytes to matrix components also enables SPME to extract a balanced quantity of different logP analytes, facilitated by multiphase equilibria, with a single extraction device.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".