Evaluation of a multi‐fiber exchange solid‐phase microextraction system and its application to on‐site sampling
Bibliographic record
Abstract
Until recently, multiple solid-phase microextraction fibers could not be automatically desorbed in a single gas chromatographic sequence without manual intervention from an operator. This drawback had been a critical issue, particularly during the analysis of numerous on-site samples taken with various fiber assemblies. Recently, a Multi-Fiber Exchange system, designed to overcome this flaw found in other commercially available autosamplers, was released. In the current research, a critical evaluation of the Multi-Fiber Exchange system performance in terms of storage stability and long-term operation is presented. It was established in the course of our research that the Multi-Fiber Exchange system can operate continuously and precisely for multiple extraction/injection cycles. However, when the effect of residence time of commercial fibers on the Multi-Fiber Exchange tray was evaluated, results showed that among the evaluated fiber coatings, Carboxen/polydimethylsiloxane was the only coating capable of efficient storage on the tray for up to 24 h after field sampling without suffering significant loss of analytes (≤10% for benzene, toluene, ethylbenzene, o-xylene, decane, and limonene). Additionally, the system capability for high-throughput analysis was demonstrated by the unattended desorption of multiple fibers after on-site sampling of toluene, indoor air levels, in a polymer synthesis lab.
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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 | 0.001 |
| 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 | 0.000 |
| 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 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".