Bibliographic record
Abstract
We present an on-chip liquid routing technique intended for application in well-based microfluidic systems that require long-term active pumping at low to medium flowrates. Our technique requires only one fluidic feature layer, one pneumatic control line and does not rely on flexible membranes and mechanical or moving parts. The presented bubble pump is therefore compatible with both elastomeric and rigid substrate materials and the associated scalable manufacturing processes. Directed liquid flow was achieved in a microchannel by an in-series configuration of two previously described "bubble gates", i.e., by gas-bubble enabled miniature gate valves. Only one time-dependent pressure signal is required and initiates at the upstream (active) bubble gate a reciprocating bubble motion. Applied at the downstream (passive) gate a time-constant gas pressure level is applied. In its rest state, the passive gate remains closed and only temporarily opens while the liquid pressure rises due to the active gate's reciprocating bubble motion. We have designed, fabricated and consistently operated our bubble pump with a variety of working liquids for >72 hours. Flow rates of 0-5.5 μl min(-1), were obtained and depended on the selected geometric dimensions, working fluids and actuation frequencies. The maximum operational pressure was 2.9 kPa-9.1 kPa and depended on the interfacial tension of the working fluids. Attainable flow rates compared favorably with those of available micropumps. We achieved flow rate enhancements of 30-100% by operating two bubble pumps in tandem and demonstrated scalability of the concept in a multi-well format with 12 individually and uniformly perfused microchannels (variation in flow rate <7%). We envision the demonstrated concept to allow for the consistent on-chip delivery of a wide range of different liquids that may even include highly reactive or moisture sensitive solutions. The presented bubble pump may provide active flow control for analytical and point-of-care diagnostic devices, as well as for microfluidic cells culture and organ-on-chip platforms.
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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".