Enhancing conjugation rate of antibodies to carboxylates: Numerical modeling of conjugation kinetics in microfluidic channels and characterization of chemical over-exposure in conventional protocols by quartz crystal microbalance
Bibliographic record
Abstract
This research reports an improved conjugation process for immobilization of antibodies on carboxyl ended self-assembled monolayers (SAMs). The kinetics of antibody/SAM binding in microfluidic heterogeneous immunoassays has been studied through numerical simulation and experiments. Through numerical simulations, the mass transport of reacting species, namely, antibodies and crosslinking reagent, is related to the available surface concentration of carboxyl ended SAMs in a microchannel. In the bulk flow, the mass transport equation (diffusion and convection) is coupled to the surface reaction between the antibodies and SAM. The model developed is employed to study the effect of the flow rate, conjugating reagents concentration, and height of the microchannel. Dimensionless groups, such as the Damköhler number, are used to compare the reaction and fluidic phenomena present and justify the kinetic trends observed. Based on the model predictions, the conventional conjugation protocol is modified to increase the yield of conjugation reaction. A quartz crystal microbalance device is implemented to examine the resulting surface density of antibodies. As a result, an increase in surface density from 321 ng/cm(2), in the conventional protocol, to 617 ng/cm(2) in the modified protocol is observed, which is quite promising for (bio-) sensing applications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".