One-Dimensional Approach to Study Kinetics of Reversible Binding of Protein on Capillary Walls
Bibliographic record
Abstract
We introduce a method for kinetic characterization of reversible binding of protein onto the inner capillary wall. In essence, a short plug of the protein solution is propagated through the capillary by pressure, and the protein is detected at the distal capillary end. The signal versus time profile is fitted with a numerical model which uses the rate constants of adsorption, kad, and desorption, kde, as fitting parameters. The values of kad and kde which result in the best fit are considered to be the sought ones. We first used COMSOL multiphysics software to develop a numerical model with two-dimensional (2D) equations of mass transfer. Although 2D models in general can describe experiments more accurately than one-dimensional (1D) models, computing 2D models takes much more time (many hours to find two parameters: kad and kde). We used the fact that the capillary is narrow and long to develop a simplified model with 1D equations of mass transfer. Our comparison of the 1D and 2D models showed that the errors of the 1D approximation were less than 5%, whereas the computation of the 1D model was 100 times faster. We finally used the 1D approach to study kinetics of reversible binding of conalbumin to the uncoated fused-silica capillary walls. We determined kad, kde, and a diffusion coefficient, D. The obtained value of D is in excellent agreement with literature data which suggests that the values of kad and kde (for which there are no literature data) are also calculated correctly. Our approach for finding kad and kde will facilitate quantitative characterization of protein adsorption on capillary walls as well as properties of passivating materials used for capillary coating.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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 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".