Applications of on-line weak affinity interactions in free solution capillary electrophoresis
Bibliographic record
Abstract
The impressive selectivity offered by capillary electrophoresis can in some cases be further increased when ligands or additives that engage in weak affinity interactions with one or more of the separated analytes are added to the electrophoresis buffer. This on-line affinity capillary electrophoresis approach is feasible when the migration of complexed molecules is different from the migration of free molecules and when separation conditions are nondenaturing. In this review, we focus on applying weak interactions as tools to enhance the separation of closely related molecules, e.g., drug enantiomers and on using capillary electrophoresis to characterize such interactions quantitatively. We describe the equations for binding isotherms, illustrate how selectivity can be manipulated by varying the additive concentrations, and show how the methods may be used to estimate binding constants. On-line affinity capillary electrophoresis methods are especially valuable for enantiomeric separations and for functional characterization of the contents of biological samples that are only available in minute quantities.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".