Rapid and Concomitant Analysis of Pharmaceuticals in Treated Wastewater by Coated Blade Spray Mass Spectrometry
Bibliographic record
Abstract
The widespread use of pharmaceuticals in both human and animal populations, and the resultant contamination of surface waters from the outflow of water treatment facilities is an issue of growing concern. This has raised the need for analytical methods that can both perform rapid sample analysis and overcome the limitations of conventional analysis procedures, such as multistep workflows and tedious procedures. Coated blade spray (CBS) is a solid-phase microextraction based technique that enables the direct-to-mass-spectrometry analysis of extracted compounds via the use of limited organic solvent to desorb analytes and perform electrospray ionization. This paper documents how CBS can be applied for the concomitant tandem mass spectrometric (MS/MS) analysis of nine pharmaceuticals in treated wastewater. The total analysis times of less than 11 min provided limits of detection lower than 50 ng L –1 for all target compounds in river water. The CBS methodology was then compared to a conventional solid-phase extraction technique for the analysis of the final effluent of six wastewater treatment facilities. The experimental results strongly suggest that CBS offers scientists a viable alternative method for analyzing water samples that is both rapid and relatively solvent-free.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".