Enumeration Algorithm for Determination of Binding Constants in Capillary Electrophoresis
Bibliographic record
Abstract
With more accurate simulation models and more efficient algorithms becoming available, the binding constants of an affinity interaction can be obtained from much simpler experiments using capillary electrophoresis. With the enumeration algorithm, all possible combinations of the binding constant and the complex mobility in certain ranges that could result in the experimental migration time of an injected analyte are extracted from a 3-D surface, which depicts the migration times resulting from different values of the binding constant and the mobility of the complex formed between the interacting pair, to form a 2-D curve. When the experimental conditions are changed, the analyte migration time will also change. A new 2-D curve can be constructed from another 3-D surface on the basis of the pairs of binding constants and complex mobility values that could result in the new migration time. Because the true binding constant and complex mobility values have to be the same for both experimental conditions under the same temperature, there has to be a point where both 2-D curves will converge. The coordinates of the converging point give the values for a binding constant and a complex mobility that will fit all 2-D curves generated under certain experimental conditions. p-Nitrophenol is used as the analyte, beta-cyclodextrin is used as the additive, and a one-cell model is used to simulate affinity CE. The experimental conditions that can improve the accuracy of the binding constants are discussed.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".