Capture efficiency of dynamic pH junction focusing in capillary electrophoresis
Bibliographic record
Abstract
Dynamic pH junction is one of the techniques used to overcome the issue of poor concentration sensitivity in CE. By introducing a long sample plug in the capillary and focusing the target molecules at the pH boundary between the sample plug and background electrolyte, this focusing technique can achieve a detection limit that is one to two orders of magnitude better than conventional CE. For quantification purposes, the capturing efficiency of the injected molecules should be scrutinized. Focusing of all target molecules inside the sample plug is desired to ensure good linearity across the whole dynamic range. To test the theoretical prediction with a real experiment, nicotine is used as the test molecule for two types of dynamic pH junctions. The first one is with acidic background electrolyte, and can accommodate both optical detection methods and positive-ion mode mass spectrometric detection, while the other is suitable for optical detection only due to the use of basic separation background electrolyte. With a theoretical simulation study, it is demonstrated that, for either of these dynamic pH junctions, focusing of at least 95% of target molecule injected into the capillary was easily achievable. More importantly, a longer sample plug could generate a high percentage of molecules captured by dynamic pH junction focusing. Sharp, symmetrical peaks and good linearity for calibration curve can be obtained. Real samples with complex matrixes were also used to demonstrate that nicotine can be selectively focused and quantified using CE-MS.
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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) | 0.000 | 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".