Novel induced charge electrokinetic based microfluidic design for trapping of micro and nanoparticles: Numerical simulation approach
Bibliographic record
Abstract
Abstract ICEK phenomena have recently been used for separating particles. The most critical issue in separating the nanoparticles (e.g., exosome, viruses, or bacteria) in complex biofluids is implementing a two‐step procedure (I) trapping the larger particles (e.g., red blood cells) from the blood and (II) trapping the nanoparticles. The purpose of this paper is to propose a design framework for the separation of considered particles in one chip. The model considered evaluating the feasibility of two‐step micro and nanoparticle separations, for instance, exosome (30–120 nm) from red blood cells (5–7 μm) or other cells in biological samples. A low voltage direct current (DC) electric field is used to generate vortices around the obstacles to trap microparticles (e.g., red blood cells) and nanoparticles (e.g., exosome) before the first and second obstacles, respectively. The achieved results demonstrated that the generated vortices are adequately strong to trap both micro and nanoparticles. This chip has several advantages, consisting of low voltage requirement and easy to manufacture design.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".