Effect of Solid-Fluid Energy Parameters on Streaming Potential and Flow Rate in Pressure-Driven Flow in Microchannels
Bibliographic record
Abstract
Electrokinetic phenomena play an important role in microfluidic transport behavior. Review of literature suggests that surface energetic can also be an important factor, but rarely explored. Typically, surface energetic is taken into account by consideration as an arbitrarily selected slip boundary condition in the modified Navier-Stokes equation. In this paper, instead of selecting this arbitrary slip condition, we examine how solid-liquid energy parameters influence the transport of microfluidics in terms of streaming potential. The simultaneous effects of surface energetics and electrokinetics will be conducted by means of a mean-field free energy lattice boltzmann approach recently proposed. Rather than using the conventional Navier-Stokes equation with a slip condition, the description solid-liquid energetic is manifested by the more physical energy parameters in the mean-field description of the method. As a result, the magnitude of liquid slip can be related directly to the solid-liquid interfacial slip. These results will be employed in conjunction with the description of electrokinetic transport phenomena for streaming potential. The variation of streaming potential as a function of the energy parameters (solid-liquid interaction) is clearly demonstrated. In pressure-driven liquid microfluidics, the flow rate may be decreased due to the counter-effect between the electrokinetic and slip.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".