Sheath‐assisted versus sheathless dielectrophoretic particle separation
Bibliographic record
Abstract
Lab-on-chip devices are widely being used for binary and ternary cell/particle separation applications. Among the lab-on-chip methods, dielectrophoresis (DEP) is a cost-effective and label-free method, with great capabilities for size-based separation of cells and particles, which is mostly performed in sheath-assisted forms. However, the elimination of the sheath flows offers advantages such as ease of operation and higher sample throughput. In this work, we present a comparison of sheath-assisted and sheathless DEP separation of three sizes of microparticles using tilted electrodes. The sheath-assisted design was capable of separating the 5, 10, and 15 μm particles with a separation efficiency as high as 98.0% for 15 μm particles. By adding a DEP focusing region, a sheathless DEP separator was proposed, which offered higher throughputs (up to 10 times) at the cost of lowering the separation efficiency (a reduction up to 10.3% for 15 μm) compared to the sheath-assisted design. To enhance the separation efficiency, a combination of the DEP focusing accompanied by weak sheath flows from both sides was proposed. This design achieved the highest sample separation yield in the outlets (as high as 98.7% for 15 μm) with a sample throughput of more than 4.2 μL/min. This study provides insights into the choice of an appropriate platform for any application in which the yield, purity, throughput, and portability must be considered.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".