Selective Focusing of Catecholamines and Weakly Acidic Compounds by Capillary Electrophoresis Using a Dynamic pH Junction
Bibliographic record
Abstract
A systematic study of selective analyte focusing in a multisection electrolyte system by capillary electrophoresis (CE) is presented. It was found that a dynamic pH junction between sample and background electrolyte zones can be used to focus zwitterionic catecholamines and weakly acidic compounds without the use of special ampholytes. Differences in pH and concentration of complexing agents, such as borate, in the sample and background electrolyte zones were determined to cause focusing through changes in the local velocity of the analyte in two different segments of the capillary. Velocity-difference induced focusing (V-DIF) of analytes using a dynamic pH junction allowed the injection of large sample volumes and significantly improved the concentration sensitivity of CE. Under optimized conditions, the limit of detection for epinephrine was determined to be about 4 x 10(-8) M (the original sample) with conventional UV absorbance detection. Moreover, separation efficiencies greater than a million theoretical plates can be achieved by focusing such large sample volumes into narrow zones. Multisection electrolyte systems, which lead to the formation of a dynamic pH junction, can be tuned toward improving the concentration sensitivity of specific analytes if their chemical properties are known.
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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) | 0.001 | 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 | 0.001 |
| 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".