Capture efficiency of dynamic pH junction focusing in capillary electrophoresis
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it