Parallelization of Curved Inertial Microfluidic Channels to Increase the Throughput of Simultaneous Microparticle Separation and Washing
Bibliographic record
Abstract
The rising global need for clean water highlights the importance of efficient sample preparation methods to separate and wash various contaminants such as microparticles. Microfluidic methods for these purposes have emerged but they mostly deliver either separation or washing, with very low throughputs. Here, we investigate parallelization of a curved-channel particle separation and washing device in order to increase its throughput for sample preparation. A curved microchannel applies inertial forces to focus larger 10 µm microparticles at the inner wall of the channel and separate them from smaller 5 µm microparticles at the outer wall. At the same time, Dean flow recirculation is used to exchange the carrier solution of the large microparticles to a clean buffer (washing). We increased the number of curved channels in a stepwise manner from two to four to eight channels in two different arraying designs, i.e., rectangular and polar arrays. We examined efficient separation of target 10 µm particles from 5 µm particles, while transferring the larger microparticles into a clean buffer. Dean flow recirculation studies demonstrated that the rectangular arrayed device performs better, providing solution exchange efficiencies of more than 96% on average as compared to 89% for the polar array device. Our 8-curve rectangular array device provided a particle separation efficiency of 98.93 ± 0.91%, while maintaining a sample purity of 92.83 ± 1.47% at a high working flow rate of 12.8 mL/min. Moreover, the target particles were transferred into a clean buffer with a solution exchange efficiency of 96.81 ± 0.54% in our 8-curve device. Compared to the literature, our in-plane parallelization design of curved microchannels resulted in a 13-fold increase in the working flow rate of the setup while maintaining a very high performance in particle separation and washing. Our microfluidic device offers the potential to enhance the throughput and the separation and washing efficiencies in applications for biological and environmental samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".