Three-Dimensional Microfluidic Focusing With Surface Charge Patterning
Bibliographic record
Abstract
Electrokinetically-driven flow circulations resulting from heterogeneous surface patches have previously been employed to improve mixing in microchannels. Here, numerical simulations demonstrate local in-channel hydrodynamic focusing through the use of strategically-patterned surface charge. Presented first is the case of a single straight channel with an axially-localized cross-sectional surface patch (ring). The surface patch exhibits a zeta potential equal in magnitude to the native microchannel surface but opposite in sign. The unsteady species transport in the presence of the electrokinetically-induced circulations is modelled, and a mean residence time is quantified. In general, residence times indicate the potential application of these circulations to microfluidic-based memory storage. Next, an improved focusing process for pinched-injection is demonstrated that exploits non-uniform surface patches. Lastly, surface patches are applied to enhance stream focusing in the microfluidic cross geometry. It is demonstrated that with this technique three-dimensional hydrodynamic focusing can be achieved in a single planar microfluidic structure. In one case, the microfluidic fluid stream was constrained to the centre of the channel and focused to 12% of its original cross-sectional area. Extensions of this work are discussed, as are the microfabrication and surface modification processes required for lab-on-chip implementation of these numerically simulated processes.
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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) | 0.001 | 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".