Control of Laminar Flow and Mass Transport in Crossing Linked Microchannels for Micro Fabrication
Bibliographic record
Abstract
There has been tremendous interest in developing micro technologies towards the integration and automation of Biochips or Lab-on-a-Chip devices due to their wide range of applications in environmental, chemical and biomedical engineering fields. The laminar flow nature in microfluidic devices offers opportunities to microfabricate the desired structures inside microchannels and pattern culturing medium inside microchannels. However, no analysis tools are available to provide optimized configurations for control the flow for microfabrication. Therefore, the goal of this study is to develop a numerical model to study transport phenomena in a cross-linked microchannels aiming to explore an optimized configuration for the microfabrication of specific desired features inside microchannel networks through investigating the effects of controlling parameters on the multistream flow. In this study, electroosmotic flow with induced pressure-driven flow will be employed. This model consists of a set of equations describing the applied potential field, flow field and concentration field in such geometries. The effects of various operational parameters are investigated based on the simultaneous solution to this model, to explore optimized configurations for flow and mass transport control in crossing linked 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".