Quantitative Analysis of Systematic Errors Originated from Wall Adsorption and Sample Plug Lengths in Affinity Capillary Electrophoresis Using Two-Dimensional Simulation
Bibliographic record
Abstract
Two-dimensional (2D) simulation of capillary electrophoresis is developed to model affinity interaction and wall adsorption simultaneously. Finite difference schemes are used to evaluate the mass-transfer equation in cylindrical coordinates. A Langmuir second-order kinetic law is applied to regulate the wall adsorption and desorption processes. Contributions from the simulation parameters are investigated extensively, and parameters for accurate and efficient simulation are identified. With the 2D model, capillary zone electrophoresis and affinity capillary electrophoresis (ACE) in the presence of strong or weak wall adsorption are simulated to elucidate peak distortions. Finite sample injection length/amount and wall adsorption that lead to systematic errors in the estimated binding constants in ACE are quantified for the first time with both actual experiments and computer simulation. Methods for correcting the estimated binding constants are proposed to extend the usefulness of ACE.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".