Optimization of the Coating Procedure for a High-Throughput 96-Blade Solid Phase Microextraction System Coupled with LC–MS/MS for Analysis of Complex Samples
Bibliographic record
Abstract
Biocompatible C18-polyacrylonitrile (PAN) coating was used as the extraction phase for an automated 96-blade solid phase microextraction (SPME) system with thin-film geometry. Three different methods of coating preparation (dipping, brush painting, and spraying) were evaluated; the spraying method was optimum in terms of its stability and reusability. The high-throughput sample preparation was achieved by using a robotic autosampler that enabled simultaneous preparation of 96 samples in 96-well-plate format. The increased volume of the extraction phase of the C18-PAN thin film coating resulted in significant enhancement in the extraction recovery when compared with that of the C18-PAN rod fibers. Various factors, such as reusability, reproducibility, pH stability, and reliability of the coating were evaluated. The results showed that the C18-PAN 96-blade SPME coating presented good extraction recovery, long-term reusability, good reproducibility, and biocompatibility. The limits of detection and quantitation were in the ranges of 0.1-0.3 and 0.5-1 ng/mL for all four 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".