Analytical method for determining polycyclic aromatic hydrocarbon pollutants using ultrafast liquid chromatography with fluorescence detection and the recent column packed with the new 5 μm Kinetex-C18 core-shell particles
Bibliographic record
Abstract
An ultrafast liquid chromatography method with fluorescence detection has been optimized for the determination of 15 polycyclic aromatic hydrocarbons (PAHs) using a recent Kinetex-C18 column (250 mm × 4.6 mm). This column has been recently packed with a new brand of porous shell particles with an average particle size of 5 μm to separate various compounds by liquid chromatography, operating at very low pressure. After optimization of the analytical procedure, the separation of the 15 PAHs in spiked tap water samples was achieved without coeluted products in 21.5 min at 16 °C using an aqueous/acetonitrile mobile phase under gradient concentrations with a very low flow rate (0.7–1.0 mL min−1) and low pressure values (870–1590 psi = 60–110 bar), all of these conditions being interesting from an economic point of view. The synchronization of wavelength time changing and the elution time of each compound was performed to avoid baseline deviation. The validation of the whole of the experimental procedure was conducted taking into consideration the following parameters: calibration curve, linearity, limits of detection and quantification, accuracy, sensitivity, precision, and repeatability of the retention time for each PAH. The proposed analytical procedure presented adequate linearity over a concentration range from 0.025 to 10 μg L−1 with a correlation coefficient better than 0.9980. The repeatability (relative standard deviation in percentage, n = 5) of the retention time for the different PAHs investigated ranged from 0.03% to 0.34% and the limit of detection was under 0.6 μg L−1 for most PAHs (excepted for indeno[1,2,3-c,d]pyrene, limit of detection = 1.71 μg L−1). The intraday and interday precisions were below 4%. The recovery of PAH in spiked tap water samples was variable, ranging from 96% to 109%, with relative standard deviation between 0.2% and 4.8%, depending on PAHs and their concentration levels.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".