Optimization of dynamic pH barrage junction focusing for weakly alkaline or zwitterionic analytes in capillary electrophoresis
Bibliographic record
Abstract
Dynamic pH junction focusing prior to electrophoretic separation has been widely used for online pre-concentration of biologically important analytes, which are mostly weakly alkaline/acidic or zwitterionic species such as neurotransmitters, peptides, and proteins. A pH junction is formed when background electrolytes with different pH values are injected sequentially into the separation column of a capillary electrophoresis (CE) system. Unlike the traditional dynamic pH junction configuration with analyte molecules located in a different chemical environment to the separation background electrolyte (BGE), the pH barrage junction has a separate high pH (or low pH) region containing no analyte. Based on Simul 5 Complex simulations and experimental verification with three series of electrolyte combinations, four basic principles for pH barrage junction focusing were identified for its optimization. First, the peak shape after focusing is slightly asymmetric, but this has negligible influence on the analysis result. Second, longer length of the barrage segment is needed for complete focusing with lower concentration of the buffering species. Third, this technique is more advantageous for analytes with relatively high electrophoretic mobility in a capillary without electroosmotic flow. Fourth, provided the analyte region and pH junction buffering species are separated, this quantitative technique is compatible with both optical and mass spectrometric detection.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".