Calibration for On-Site Analysis of Hydrocarbons in Aqueous and Gaseous Samples Using Solid-Phase Microextraction
Bibliographic record
Abstract
Rapid sampling and sample preparation methodology was investigated using adsorptive poly(dimethylsiloxane)/divinylbenzene and Carboxen/poly(dimethylsiloxane) solid-phase microextraction (SPME) fiber coatings and volatile aromatic hydrocarbons (BTEX: benzene, toluene, ethylbenzene, and o-xylene). A flow-through system was used to generate a standard aqueous solution of BTEX as model sample with known linear velocity. Parameters that affect the extraction process, including sampling time, concentration, water velocity, and temperature, were investigated. Very short sampling times from 10 s and sorbents with strong affinity and large capacity were used to ensure the effect of '"zero sink" and to calibrate the extraction process in the initial linear extraction region. Several different concentrations were investigated, and it was found that mass uptake changes with concentration linearly. The increase of water velocity increases mass uptake, though the increase is not linear. Temperature does not affect mass uptake significantly under typical field sampling conditions. To further accurately describe rapid SPME analysis of aqueous samples, a new model translated from heat transfer to a circular cylinder in cross-flow was used. An empirical correlation to this model was used to predict the mass-transfer coefficient. Findings indicate that predicted mass uptake compares well with experimental mass uptake. The new model was tested for rapid air sampling, and it was found that this new model also predicted rapid air sampling accurately. Findings presented in this study extend the existing fundamental knowledge related to rapid sampling/sample preparation with SPME.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".