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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it