Investigation of the Effect of the Extraction Phase Geometry on the Performance of Automated Solid-Phase Microextraction
Bibliographic record
Abstract
A new configuration of C(18) thin film extraction phase designed for high sample throughput has been developed and applied to the analysis of benzodiazepines in spiked urine samples using high performance liquid chromatography coupled with tandem mass spectrometry. The high throughput analysis was achieved with the use of a robotic autosampler which enabled parallel analyte extraction in a 96-well plate format. Factors affecting data reproducibility, extraction kinetics, sample throughput, and reliability of the system were investigated and optimized. The intrawell reproducibility was 4.5-7.3%, while interwell reproducibility was 7.0-11% in urine and PBS samples. The limits of detection and quantitation were 0.05-0.15 ng/mL and 0.2-2.0 ng/mL for all analytes, respectively. By comparison with optimized automated multifiber SPME relying on rod geometry, the C(18) thin films showed higher extraction rates (approximate 2-fold increase) and hence higher sample throughput because of the improved configuration and more effective agitation/mass transfer. In addition, this new configuration provided an extraction phase with greater surface area to volume ratio and greater extraction phase volume, which resulted in approximately 2-fold increase in the extraction capacity for diazepam compared with the extractions with automated multifiber SPME rod geometry. The results of this investigation demonstrated the advantages of using thin films to improve extraction kinetics and sensitivity of automated SPME methods for high performance liquid chromatography.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".