Systematic optimization of exhaustive electrokinetic injection combined with micellar sweeping in capillary electrophoresis
Bibliographic record
Abstract
The combination of exhaustive electrokinetic injection and sweeping micellar electrokinetic chromatography (sweeping-MEKC) in capillary electrophoresis often provides a several thousand-fold improvement in concentration detection limit. However, reproducibility of this method has been a major issue that often prevents its use as a quantitative tool for the analysis of ultra-trace analytes in complex matrices. In this paper, we demonstrate that such a technique can be systematically optimized with five key factors: the conductivity of the sample solution, the conductivities of the separation buffers, the fraction of the capillary that is filled with the high conductivity buffer, the electrokinetic injection time, and the surfactant concentration. By controlling the sample conductivity, we were able to achieve highly reproducible results, while still maintaining the sensitivity of field-amplified sample injection. At optimal conditions, we were able to analyze three amine drugs (amphetamine, methamphetamine, and methylenedioxymethamphetamine) with limits of detection of 6 to 8 pg ml(-1) (ppt), which is a several thousand-fold improvement over normal sample injection using CE with a photodiode array detector.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".