New pressure‐assisted sweeping on‐line preconcentration for polar environmentally relevant nitrosamines: <scp>P</scp>art 1. Sweeping for polar compounds and application of auxiliary pressure
Bibliographic record
Abstract
Typically sweeping reversed migration EKC (RM-EKC) is used for online enrichment and separation of neutral compounds in CE, however sweeping is not usually suitable for highly polar neutral compounds due to the lack of strong interaction with micellar phase. Since acidic BGE or coated capillaries (BGE pH 2-8) are used to virtually eliminate the EOF, migration of neutral analytes is only through association with the micelles with relatively slow electrophoretic mobility. To decrease the long analysis times that result, an auxiliary pressure can be applied, which also serves to avoid the associated band broadening. In this study, we have modified a commercially available CE instrument to perform pressure-assisted sweeping. The apparatus described can be used to precisely control the application of pressure, and therefore direction and magnitude of bulk flow in the capillary. This modification allows us to employ longer capillaries and capillaries with larger internal diameter to increase the sensitivity. An optimized method was used for the analysis of a group of seven N-nitrosamines that have been widely reported in environmental samples and good concentration factors of up to 34 were achieved. When a coated capillary is employed, this method is effective even at neutral pH, making it broadly applicable.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".