Influence of Entrance and Exit Conditions on the Transient Evolution of Streaming Potential in a Finite Length Microchannel
Bibliographic record
Abstract
Microfluidic systems have profoundly transformed chemical analysis, separation and detection techniques over the past decade by enabling rapid manipulation of extremely small volumes of fluid. Electrokinetic (EK) flow, i.e., flow of an electrolyte in narrow capillaries driven by the combined influence of electric field and pressure, is of significant interest in microfluidic devices. Review of literature reveals that most studies on microchannels are either for steady state solution or infinite length microchannels. In this paper, we examine the development of a transient streaming potential for pressure-driven EK flow in a finite length microchannel. A transient numerical simulation of ion transport leading to the development of a streaming potential across a finite length circular cylindrical microchannel connecting two infinite reservoirs is presented. The solution based on finite element analysis shows the transient development of ionic fluxes, currents, and the streaming potential across the channel. The influence of the entrance and exit effects on the evolution of the streaming potential is clearly depicted in this study. Our results will be employed to discuss some of the limitations of literature streaming potential analysis based on infinite length microchannels.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".