Sheathless and high throughput sorting of paramagnetic microparticles in a magneto-hydrodynamic microfluidic device
Bibliographic record
Abstract
Sorting of microorganisms and particles from a mixture is critical for numerous biotechnological and medical applications. Several sorting methods such as pinched flow fractionation (PFF), optical sorting, dielectrophoresis, acoustic separation, magnetophoresis and deterministic lateral displacement (DLD) have been reported in literature. But most of these methods lack ideal characteristics of a sorter such as ability to process at high throughput, simple design, non-complicated fabrication method, sheathless operation and high purity in separation. In this paper, we have introduced a novel sorting technique by integrating focusing of magnetic particles in a narrow microchannel with their hydrodynamic separation at a downstream expansion channel which meets majority of the aforementioned characteristics. To achieve this, the sheathless focusing of paramagnetic microparticles in the narrow microchannel and their deflection at the expansion channel were first studied at various flow rates (0.5-5 ml h-1). Then, a mixture of 5 and 11 μm paramagnetic particles was introduced into the device and their separation was examined quantitatively. It was found that the magnetic particles were focused along the wall of channel, however their centers were positioned on two distinct streamlines owing to difference in their sizes. Hence, these two particles were found separated from each other as they flew into the expansion region. This technique of size based separation of paramagnetic particles works at a high throughput of 107 particles per hour and offers more than 98% purity in sorting.
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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) | 0.000 | 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".