Extending the Solid-Phase Microextraction Technique To High Analyte Concentrations: Measurements and Thermodynamic Analysis
Bibliographic record
Abstract
The solid-phase microextraction (SPME) technique has been used historically to quantify analytes present at the parts per million level. However, the nonintrusive nature of SPME lends itself to other applications involving analytes at higher concentration. In the current work, the possibility of using the SPME technique to measure concentrated gaseous samples was examined. Pentane concentrations between 0 and 100% saturation were studied, over a temperature range of 20-45 degrees C. The results showed that, up to a critical mole fraction in the solid phase, the concentrations of pentane in the polymeric extracting solid and vapor phases were related by a constant, equal to Henry's constant. The temperature dependence of Henry's constant was shown to follow the predicted trend with temperature, as determined from rigorous thermodynamic calculations. Above the pentane concentration in the polymeric phase, the response deviated from linearity. The nonideality was captured in an activity coefficient. An activity coefficient model developed to describe the nonideality was found to be a function of the swollen volume of the SPME polymer phase. The results indicate that the SPME technique can be applied to high analyte concentrations, although difficulties may be encountered when multiple analytes are absorbed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".