Investigation of operational fundamentals for vacuum‐assisted headspace high‐capacity solid‐phase microextraction and gas chromatographic analysis of semivolatile compounds from a model solid sample
Bibliographic record
Abstract
Vacuum-assisted headspace solid-phase microextraction (Vac-HS-SPME) is a technique used to enhance SPME sampling of semi-volatile organic compounds. Here, it was combined with a high-capacity SPME Arrow, which features a larger volume of extraction phase and a more rugged configuration than traditional extraction fibers. An in-depth assessment of the critical parameters was conducted to achieve optimal extraction of representative compounds from a model solid sample matrix (Ottawa sand). Operational fundamentals investigated included the types of seals needed to create a leak-free environment under vacuum conditions; the magnitude of the vacuum applied and time needed to activate the Vac kinetics; order of sample vial preparation methods (VPMs); and other standard variables associated with extract analysis by gas chromatography-mass spectrometry. When exploring the limits of sample VPMs, results indicated an ideal workflow requires the solid sample to be spiked before sealing the vial, allow the sample to rest overnight, then apply vacuum at a pressure of -677 mbar (out of -789 mbar maximum possible vacuum with pump and compressor used), exerted on the vial for 90 s. This work provides the necessary workflow for the optimization of Vac-HS-SPME sampling of analytes from solid matrices.
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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 | 0.002 |
| 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 | 0.001 |
| 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 | 0.000 |
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".