Optimization and validation of a fast ultrahigh‐pressure liquid chromatographic method for simultaneous determination of selected sulphonamides in water samples using a fully porous sub‐2 μm column at elevated temperature
Bibliographic record
Abstract
High temperature in HPLC is considered a valuable tool helping to overcome the increase in the column backpressure when using small packing particles such as sub-2 μm, as it allows reduction in the mobile-phase viscosity. In this study, a fast analytical method based on HPLC-UV was developed using a sub-2 μm column at elevated temperature for the simultaneous determination of nine sulphonamides. Owing to the lower viscosity of the mobile phase, the separation could be achieved in 3 min at 60°C for all analytes. The effect of temperature, the organic modifier percentage and the flow rate on the retention time was studied. The method developed was used for the determination of selected sulphonamides in surface and wastewater samples. Sample preparation was carried out by solid-phase extraction on Oasis HLB cartridges. The method developed was validated based on the linearity, precision, accuracy, detection and quantification limits. The recovery ranged from 70.6 to 96 % with standard deviations not higher than 4.7%, except for sulphanilamide. Limits of detection ranged from 1 to 10 μg/L after optimization of all analytical steps. This method has the highest performance in terms of analytical speed compared with other published HPLC-UV methods for the determination of sulphonamides in water.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".