Continuous flow membrane microextraction as a clean method for detecting codeine and papaverine in biological samples using HPLC-UV
Bibliographic record
Abstract
In this study, continuous-flow membrane microextraction was connected online with a high-performance liquid chromatography-UV detector for the pre-concentration and high clean-up of papaverine and codeine in biological samples. The extraction cell was designed with donor and acceptor chambers separated by a sheet membrane. By adjusting the pH of the donor phase (10 mL, pH 11), the analyte molecules were extracted into the supported liquid membrane (SLM) (15 µL of 1-octanol). The donor solution was circulated through the donor chamber using a peristaltic pump and magnetically agitated by a bar stirrer placed near the membrane, enhancing the diffusion and convection flow of the targeted drugs from the donor solution to the SLM. Subsequently, by adjusting the acceptor solution to an acidic pH of 2 (100 µL), the drugs in ionic form were reversely extracted into the acceptor phase. The procedure exhibited desirable relative standard deviation of less than 3.70%, linear ranges of 7–600 ng mL−1 for codeine and 2.0–600 ng mL−1 for papaverine, and limits of detection of 2.0 ng mL−1 for codeine and 0.6 ng mL−1 for papaverine. The design of the extraction cell significantly improved the performance for determining targeted drugs in complex matrices such as plasma and urine.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".