One-layer microfluidic device for hydrodynamic 3D self-flow-focusing operating in low flow speed
Bibliographic record
Abstract
Hydrodynamic 3D flow-focusing techniques in microfluidics are categorized as (a) sheathless techniques which require high flow rates and long channels, resulting in high operating cost and high flow rates which are inappropriate for applications with flow rate limitations, and (b) sheath-flow based techniques which usually require excessive sheath flow rate to achieve hydrodynamic 3D flow-focusing. Many devices based on these principles use complicated fabrication methods to create multi-layer microchannels. We have developed a sheath-flow based microfluidic device that is capable of hydrodynamic 3D self-flow-focusing. In this device the main flow (black ink) in a low speed, and a sheath flow, enter through two inlets and enter a 180 degree curved channel (300 × 300 μm cross-section). Main flow migrates outwards into the sheath-flow due to centrifugal effects and consequently, vertical focusing is achieved at the end of the curved channel. Then, two other sheath flows horizontally confine the main flow to achieve horizontal focusing. Thus, the core flow is three-dimensionally focused at the center of the channel at the downstream. Using centrifugal force for 3D flow-focusing in a single-layer fabricated microchannel has been previously investigated by few groups. However, their demonstrated designs required high flow speed (>1 m/s) which is not suitable for many applications that live biomedical specie are involved. Here, we introduce a new design which is operational in low flow speed (<0.05 m/s) and is suitable for applications involving live cells. This microfluidic device can be used in detecting, counting and isolating cells in many biomedical applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".