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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".