Univalent salts as modifiers in micellar capillary electrophoresis
Bibliographic record
Abstract
The influence of three univalent salts (LiCl, NaCl and RbCl) on the separation of amino acids labelled with 3-(4-carboxybenzoyl)-quinoline-2-carboxaldehyde (CBQCA) in micellar capillary electrophoresis has been studied. Capacity factors for a series of eight CBQCA-labelled amino acids in a sodium dodecyl sulfate (SDS) micellar system containing different concentrations of salt were measured and were found to be related to both the hydrodynamic radius of the salt counter-ion (Li(+), Na(+), Rb(+)) and the relative hydrophobicity of the amino acid. Affinities of the analytes for the micelles were generally observed to decrease as the salt concentration in the background electrolyte was increased from 10 to 50 mM. This decrease in affinity was greatest in the presence of the salt counter-ion with the smallest hydrodynamic radius and is primarily due to an increased resistance to mass transfer. Furthermore, interaction of hydrophobic analytes with the micelles is greater than that of hydrophilic analytes at all salt concentrations due to the greater strength of the hydrophobic interactions and this effect is also enhanced in the presence of a smaller counter-ion. No negative effects due to Joule heating or electromigrative dispersion were observed for low to moderate concentrations of salt, which suggests that the use of simple univalent salts to modify analyte/micelle affinities can be a practical method for improving the separation of complex mixtures.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".