Diffusion-Based Calibration for SPME Analysis of Aqueous Samples
Bibliographic record
Abstract
When an SPME fiber is exposed for a short period of time to a flowing fluid sample, the amount of extracted analyte depends on its diffusion coefficient in the matrix medium, and it can be correlated to its concentration using a simple mathematical model. This work discusses the extension of this approach, already validated for gaseous samples and SPME fibers coated with strong adsorbent coatings, to the diffusion-based quantification of analytes present in aqueous samples. Dilute aqueous solutions of aromatic hydrocarbons were used as model samples and vials were modified to use conventional magnetic agitation with controlled tangential flow of the test solution around the fiber. It was demonstrated that, with proper selection of the stirring speed and sampling time, the same diffusion-based quantitative model used for gas samples could be employed. Under optimal conditions, the concentrations of the evaluated aromatic hydrocarbons were estimated with relative standard deviations between 0.8 and 3.6% and without deviation from the expected values within this precision range. Considering the extraction times involved, between 30 and 60 s, the approach here presented is the fastest possible technique for direct extraction of analytes from liquid samples.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".