Induced charge electro-osmotic concentration gradient generator
Bibliographic record
Abstract
Biomolecule gradients play an important role in the understanding of various biological processes. Typically, biological cells are exposed to linear and nonlinear concentration gradients and their response is studied for understanding cell growth, cell migration, and cell differentiation mechanisms. Recent studies have demonstrated the use of microfluidic devices for precise and stable concentration gradient generation. However, most of the reported devices are geometrically complex and lack dynamic controllability. In this work, a novel microfluidic gradient generator is presented which utilizes the induced charge electro-osmosis (ICEO) by introducing conducting obstacle in the microchannel. With the ICEO flow component, significant transverse convection can be generated within the microchannel, which can, in turn, be used to create nonlinear as well as asymmetric gradients. The characteristics of the developed concentration gradient are dependent on the interplay between fixed charge electro-osmotic and ICEO flows. It is shown that the proposed device can switch between linear and nonlinear gradients by just altering the applied electric field. Finally, the formation of user-defined concentration profiles (linear, convex, and concave) is demonstrated by varying the conducting obstacle size.
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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".