A Novel Micro Pressurized Liquid Extraction Method for Rapid Sample Preparation of Polycyclic Aromatic Hydrocarbons in Various Solids
Bibliographic record
Abstract
The use of a novel micro pressurized liquid extraction (µPLE) method for the isolation of 16 priority pollutant polycyclic aromatic hydrocarbons (PAHs) from various solid samples is explored. The technique employs rapid heating in a single static extraction mode to remove analytes in a matter of seconds from 5–10 mg samples using only 125 µL of solvent. For example, results show that 30 s extractions with toluene at 200 °C produce respective PAH recovery ranges of 90%–130% and 88%–114% from samples of soil and smoked chicken. Comparatively, solids containing significant amounts of biochar were more challenging to extract from. For instance, when using a pure biochar sample matrix, recoveries for the 16 PAHs range from only 33%–66% after 60 s of extraction with toluene at 200 °C. Overall, these extraction results agree very well with those reported when using conventional methods on similar samples. Therefore, the findings indicate that µPLE can potentially provide an alternative sample preparation method for PAHs that is both very rapid and requires little solvent.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".