Green ultra‐fast high‐performance liquid chromatographic method using a short narrow‐bore column packed with fully porous sub‐2 μm particles for the simultaneous determination of selected pharmaceuticals as surface water and wastewater pollutants
Bibliographic record
Abstract
Fast separations are very desirable in laboratories that analyze large numbers of samples per day or those needing short turn-around times. Traditional HPLC methods using conventional stationary phases and standard column dimensions require significant amounts of organic solvents and generate large volumes of waste. With growing awareness about the environment, the development of green technologies has been receiving increasing attention. In this work, a very fast green analytical method based on LC-UV using a short narrow bore column packed with fully porous sub-2 μm particles has been developed for simultaneous determination of nine pharmaceuticals in wastewater and surface water. The chromatographic separation was optimized in order to achieve short analysis time and good resolution for all analytes in a single run. All analytes could be separated in 1 min with good resolution. Sample preparation was executed by solid phase extraction using Oasis HLB cartridges. The method developed was validated based on parameters such as linearity, precision, accuracy, detection, and quantification limits. The recovery ranged from 70.9 to 92.5% with SDs not higher than 5.4%, except for acetaminophen and sulphanilamide. LODs ranged from 0.6-2.5 μg/L, while the LOQs were in the range 2-8 μg/L.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".