Microfluidic curved-channel centrifuge for solution exchange of target microparticles and their simultaneous separation from bacteria
Bibliographic record
Abstract
One of the common operations in sample preparation is to separate specific particles (e.g. target cells, embryos or microparticles) from non-target substances (e.g. bacteria) in a fluid and to wash them into clean buffers for further processing like detection (called solution exchange in this paper). For instance, solution exchange is widely needed in preparing fluidic samples for biosensing at the point-of-care and point-of-use, but still conducted via the use of cumbersome and time-consuming off-chip analyte washing and purification techniques. Existing small-scale and handheld active and passive devices for washing particles are often limited to very low throughputs or require external sources of energy. Here, we integrated Dean flow recirculation of two fluids in curved microchannels with selective inertial focusing of target particles to develop a microfluidic centrifuge device that can isolate specific particles (as surrogates for target analytes) from bacteria and wash them into a clean buffer at high throughput and efficiency. We could process micron-size particles at a flow rate of 1 mL min-1 and achieve throughputs higher than 104 particles per second. Our results reveal that the device is capable of singleplex solution exchange of 11 μm and 19 μm particles with efficiencies of 86 ± 2% and 93 ± 0.7%, respectively. A purity of 96 ± 2% was achieved in the duplex experiments where 11 μm particles were isolated from 4 μm particles. Application of our device in biological assays was shown by performing duplex experiments where 11 μm or 19 μm particles were isolated from an Escherichia coli bacterial suspension with purities of 91-98%. We envision that our technique will have applications in point-of-care devices for simultaneous purification and solution exchange of cells and embryos from smaller substances in high-volume suspensions at high throughput and efficiency.
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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 | 0.000 |
| 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".