Behavior of Interacting Species in Capillary Electrophoresis Described by Mass Transfer Equation
Bibliographic record
Abstract
Affinity capillary electrophoresis (ACE) has been used to estimate thermodynamic constants of binding interactions with linear or nonlinear regression methods. The accuracy of this approach relies heavily on the binding interaction mechanism, which is controlled by both the nature of the interaction and the experimental conditions. The development of a highly efficient computer-simulated ACE system makes it possible to demonstrate the detailed behavior of any interacting species of a given interaction under any conditions. The order of the mobilities of the complex and the two binding species in their free forms is a key factor to determine what molecules in what locations of the column are involved in the interaction, and the peak shape resulting from such interactions, of a given ACE experiment. In this paper and the supporting materials, 18 scenarios in 6 different combinations of migration orders of the free analyte, free additive, and complex formed are studied by a computer simulation program based on the mass transfer equation. From the study of these situations, we conclude high additive concentration (ensuring high capacity factor) and low analyte concentration (ensuring fast fill-in of the free additive in the analyte plug) are crucial for obtaining accurate results when using the regression methods. On the other hand, the approach to estimate binding constants with computer simulation can be much more accurate as long as accurate and efficient simulation models can be developed, especially when the ratio of the additive and analyte concentrations is not large enough.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".