Continuous High‐Throughput Plasma Separation for Blood Biomarker Sensing Using a Hydrodynamic Microfluidic Device
Bibliographic record
Abstract
Continuous, cost-effective, high-throughput with admissible yield and purity of blood plasma separation is widely needed for biomarker detection in the clinic. The existing gold standard technique (centrifugation) and microfluidic technologies fall short of meeting these criteria. In this study, a microfluidic device design is demonstrated based on passive hydrodynamic principles to achieve admissible yield and purity plasma samples. Through computational and experimental assessments, it is shown that side channels with varying lengths are required to improve the plasma extraction rate. The optimized side channels in this device design use the formed cell-free layer regions in the expanded areas to extract plasma consistently and efficiently. These Hydrodynamic Continuous, High-Throughput Plasma Separator (HCHPS) microfluidic devices achieve a purity in the range of 47% to 64% with whole blood and maintaining a yield of 10% to 18%, with half hemolysis compared to gold standard centrifugation. These devices also separate the plasma from diluted blood with a purity in the range of 62% to 97% with a similar yield range. Additionally, whole human blood spiked with lactate was processed through the HCHPS device, and the separated plasma is collected and analyzed using two biosensing approaches, a bead-based fluorescence, and an electrochemical aptamer biosensing, confirming the quality of plasma for downstream biomarker detection.
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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) | 0.001 | 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 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".