Electrokinetic preconcentration of particles and cells in microfluidic reservoirs
Bibliographic record
Abstract
Preconcentrating samples of dilute particles or cells to a detectable level is required in many chemical, environmental and biomedical applications. A variety of force fields have thus far been demonstrated to capture and accumulate particles and cells in microfluidic devices, which, however, all take place within the region of microchannels and may potentially cause channel clogging. This work presents a new method for the electrokinetic preconcentration of 1 μm-diameter polystyrene particles and E. coli cells in a very-low-conductivity medium inside a microfluidic reservoir. The entire microchannel can hence be saved for a post-concentration analysis. This method exploits the strong recirculating flows of induced-charge electroosmosis to concentrate particles and cells near the corners of the reservoir-microchannel interface. Positive dielectrophoresis is found to also play a role when small microchannels are used at high electric fields. Such an in-reservoir electrokinetic preconcentration method can be easily implemented in a parallel mode to increase the flow throughput, which may potentially be used to preconcentrate bacterial pathogens in water.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".