pH-Independent large-volume sample stacking of positive or negative analytes in capillary electrophoresis
Bibliographic record
Abstract
In capillary electrophoresis, the short optical path length associated with on-column UV detection imposes an inherent detection problem. Detection limits can be improved using sample stacking. Recently, large-volume sample stacking (LVSS) without polarity switching was demonstrated to improve detection limits of charged analytes by more than 100-fold. However, this technique requires suppression of the electroosmotic flow (EOF) during the run. This necessitates working at a low pH, which limits using pH to optimize selectivity. We demonstrate that LVSS can be performed at any buffer pH (4.0-10.0) if the zwitterionic surfactant Rewoteric AM CAS U is used to suppress the EOF. Sensitivity enhancements of up to 85-fold are achieved with migration time, corrected area, and peak height reproducibility of 0.8-1.6%, 1.3-3.7%, and 0.8-4.9%, respectively. Further, it is possible to stack either positively or negatively charged analytes using zwitterionic surfactants to suppress the EOF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".