Accurately describing weak analyte-additive interactions by capillary electrophoresis
Bibliographic record
Abstract
When modeling analyte-additive interactions in capillary electrophoresis (CE), it is necessary to correct for all changes in the apparent electrophoretic mobility of an analyte that are not due to specific binding. Current models based on dynamic complexation have corrected for bulk viscosity changes in the background electrolyte (BGE) when additives are used, while assuming negligible changes in the dielectric constant and other physicochemical properties of the solution. In this report, a study of weak interactions between deoxyribonucleotides and hydroxypropyl-beta-cyclodextrin (HP-beta-CD) revealed significant nonideality in binding isotherms. Changes in the dielectric properties of the solution due to the addition of high concentrations of HP-beta-CD to the BGE was observed to alter the electrophoretic mobility of analytes. A relative dielectric correction factor was required to normalize analyte mobilities to a reference state of zero additive concentration. The use of both a relative dielectric factor and a viscosity correction factor was found to increase the accuracy of the model, reflected by a higher degree of correlation between predicted and measured analyte mobilities. This type of correction is particularly relevant when studying weak analyte binding interactions or when using high concentrations of additive in the BGE. This work is vital for accurate determination of weak binding constants and mobility values, as well as providing a deeper understanding of the fundamental parameters influencing a separation in CE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".