Modeling and Simulation of Hydrodynamic Interaction of DNA in a Micro-Fluidic Channel
Bibliographic record
Abstract
The motion of DNA (in the bulk solution) and the non-Newtonian effective fluid behavior are considered separately and self-consistently with the fluid motion satisfying the no-slip boundary condition on the surface of the confining geometry in the presence of channel pressure gradients. A different approach has been developed to model DNA in the micro-channel. In this study the DNA is assumed as an elastic chain with its characteristic Young’s modulus, Poisson’s ratio and density. The force which results from the fluid dynamic pressure, viscous forces and electromotive forces is applied to the elastic chain in a coupled manner. The velocity fields in the micro-channel are influenced by the transport properties. Simulations are carried out for the DNAs attached to the micro-fluidic wall. Numerical solutions based on a coupled multiphysics finite element scheme are presented. The modeling scheme is derived based on mass conservation including biomolecular mass, momentum balance including stress due to Coulomb force field and DNA-fluid interaction, and charge transport associated to DNA and other ionic complexes in the fluid. Variation in the velocity field for the non-Newtonian flow and the deformation of the DNA strand which results from the fluid-structure interaction are first studied considering a single DNA strand. Motion of the effective center of mass is analyzed considering various straight and coil geometries. Effects of DNA statistical parameters (geometry and spatial distribution of DNAs along the channel) on the effective flow behavior are analyzed. In particular, the dynamics of different DNA physical properties such as radius of gyration, end-to-end length etc. which are obtained from various different models (Kratky-Porod, Gaussian bead-spring etc.) are correlated to the nature of interaction and physical properties under the same background fluid environment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".