Numerical Investigation of the Effect of Geometric and Physiochemical Parameters on Biomolecule Capture Efficiency
Bibliographic record
Abstract
This paper presents and compares three different designs including open channel, circular pillar and screen-plate microreactors for capturing and detection of biomolecules in a buffer liquid. In general, these capturing/detection devices consist of a flow cell containing one or several reactive surfaces loaded with ligand molecules. The critical issue in the design of an efficient device is the proximity of the biomolecules to the ligands in the capturing stage since the latter is immobilized on the reactive surface and the former is freely moving in the flow. The flow pattern and the geometry of the device are the key factors in this regard. The presented designs are numerically modeled and compared in terms of capture efficiency. Immersed biomolecules are assumed to behave like a continuum medium. The Navier-Stokes and advection-diffusion equations are solved in two dimensions and the concentration profile is found after a certain sampling period. The chemical reaction between the ligand and the biomolecule is included in the model through solving the first order kinetic equation at the boundaries. The average surface concentrations of the adsorbed molecules are plotted and compared for all the geometries to determine the most efficient one. Considering the performance, ease of fabrication, and detection, the screen plates are found to be the best option for the purpose of biomolecule removal. The effects of the change in the geometric parameters (e.g., the flow path width in the microchannels) and physicochemical parameters (e.g., the diffusion constant, ligand surface density, and forward and backward reaction rates) involved in the problem on the adsorbed concentration are thoroughly inspected and the corresponding results are plotted.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".