In-Tube Molecularly Imprinted Polymer Solid-Phase Microextraction for the Selective Determination of Propranolol
Bibliographic record
Abstract
A molecularly imprinted polymer (MIP) material was synthesized for use as an in-tube solid-phase microextraction (SPME) adsorbent. The inherent selectivity and chemical and physical robustness of the MIP material was demonstrated as an effective stationary-phase material for in-tube SPME. An automated and on-line MIP SPME extraction method was developed for propranolol determination in biological fluids. This simplified the sample preparation process and the chromatographic separation of several beta-blocker compounds. The method developed for propranolol showed improved selectivity in comparison to alternative in-tube stationary-phase materials, overcoming the limitations of existing SPME coating materials. Preconcentration of the sample by the MIP adsorbent increased the sensitivity, yielding a limit of detection of 0.32 microg/mL by UV detection. Excellent method reproducibility (RSD < 5.0%) and column reusability (> 500 injections) were observed over a fairly wide linear dynamic range (0.5-100 microg/mL) in serum samples. To our knowledge, this is the first report on the automated application of a MIP material for in-tube SPME. The method was inexpensive, simple to set up, and simplified the choice of SPME adsorbent for in-tube extraction. The approach can potentially be extended to other MIPs for the determination of a wide range of chemically significant analytes.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".