The Study of Combined Electroosmotic and Pressure Driven Flow in Wavy Nanochannels
Bibliographic record
Abstract
Solid surfaces of micro/nanochannels exhibit a certain degree of roughness that is incurred during fabrication and/or adsorption of macromolecules. The presence of such roughness changes the flow pattern in electroosmotic flows (EOF). The present study investigates the effect of surface waviness on combined EOF and pressure driven flow (PDF) of an electrolyte solution, in a nanochannel having charged walls. The surface profile of the top and bottom walls vary either in a varicose or in a sinuous mode. The problem is solved by using the Perturbation model, a modified linearized disturbance Navier-Stokes equations, by assuming two-dimensional combined EOF and PDF between two parallel plates as base flow. By discretizing the linearized disturbance equations using the Chebyshev collocation method in the wall normal direction and Fourier transformation in the flow direction, the perturbed velocity components are calculated. The effects of electric double layer (EDL) and amplitude of wavy surface on the flow pattern are studied. The effects of overlapped EDL are also studied as one of the limiting case. The formation of circulation regions is observed in the varicose mode channel when the EOF and PDF are flowing in the opposite direction. The decrease in the number of circulation regions is ob served for the decrease in the value of average half height of the channel to debye length ratio (κ). Serpentine or triangular type waviness in the streamline velocity is observed in sinuous mode type channel when the EOF and PDF are in opposite directions. The increase in the waviness of the streamline velocity is observed for decrease in the value of κ and increase in the amplitude a when both EOF and PDF are flowing in the same direction.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".