Identification of Omitted Pollutants in Environmental Water via In Situ Thin-Film Microextraction
Bibliographic record
Abstract
Sample preparation is an inevitable step in the screening workflow for the identification of unknown pollutants in the aquatic environment. However, the possible loss of pollutants during sample preparation has aroused serious concern but remains not effectively resolved. This study shows that high-risk pollutants omitted in solid-phase extraction (SPE) can be identified via in situ thin-film microextraction (TFME) coupled with high-resolution mass spectrometry. It was observed that a total of 541 features showed higher mass spectrometry signal intensity by using in situ TFME in comparison with SPE. Subsequently, 28 compounds were identified from the features with higher intensity by comparing the recorded tandem mass spectra with the online database and validating with standards. Notably, six out of these compounds were completely omitted using SPE, including a dye, drug, and industrial product. It was confirmed that the lower extraction efficiencies of SPE were attributed to the limited sample volumes, the losses of compounds during sample transportation and storage, and the entrapment of compounds in SPE columns. This study demonstrates that in situ TFME should be used as a supplementary technique to SPE for extending the coverage of pollutants in the screening workflows.
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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 | 0.001 |
| 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 | 0.000 |
| 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".