High-efficiency electrokinetic micromixing through symmetric sequential injection and expansion
Bibliographic record
Abstract
Rapid electric field switching is an established microfluidic mixing strategy for electrokinetic flows. Many such microfluidic mixers are variations on the T- or Y-form channel geometry. In these configurations, rapid switching of the electric field can greatly improve initial mixing over that achieved with static-field mixing. Due to a fundamental lack of symmetry, however, these strategies produce lingering cross-channel concentration gradients which delay complete mixing of the fluid stream. In this paper, a field switching microfluidic mixing strategy which utilizes a symmetric sequential injection geometry with an expansion chamber to achieve high efficiency microfluidic mixing is demonstrated experimentally. A three-inlet injector sequentially interlaces two dissimilar incoming solutions. Downstream of the injector, the sequence enters an expansion chamber resulting in a dramatic (two orders of magnitude) decrease in Peclet number and rapid axial diffusive mixing. The outlet concentration may be accurately varied over the full spectrum by tuning the duty cycle of the field switching waveform. The chips are designed with input from a previous numerical study, manufactured in poly(dimethylsiloxane) using soft-lithography based microfabrication, and tested using fluorescence microscopy. In the context of on-chip chemical processing for analytical operations, the demonstrated mixing strategy has several features: high mixing efficiency (99%), compact axial length (2.3 mm), steady outflow velocity, and readily variable outlet concentration (0.15 < c* < 0.95).
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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".