Bibliographic record
Abstract
We introduce a miniature gate valve as a readily implementable strategy for actively controlling the flow of liquids on-chip, within a footprint of less than one square millimetre. Bubble gates provide for simple, consistent and scalable control of liquid flow in microchannel networks, are compatible with different bulk microfabrication processes and substrate materials, and require neither electrodes nor moving parts. A bubble gate consists of two microchannel sections: a liquid-filled channel and a gas channel that intercepts the liquid channel to form a T-junction. The open or closed state of a bubble gate is determined by selecting between two distinct gas pressure levels: the lower level corresponds to the "open" state while the higher level corresponds to the "closed" state. During closure, a gas bubble penetrates from the gas channel into the liquid, flanked by a column of equidistantly spaced micropillars on each side, until the flow of liquid is completely obstructed. We fabricated bubble gates using single-layer soft lithographic and bulk silicon micromachining procedures and evaluated their performance with a combination of theory and experimentation. We assessed the dynamic behaviour during more than 300 open-and-close cycles and report the operating pressure envelope for different bubble gate configurations and for the working fluids: de-ionized water, ethanol and a biological buffer. We obtained excellent agreement between the experimentally determined bubble gate operational envelope and a theoretical prediction based on static wetting behaviour. We report case studies that serve to illustrate the utility of bubble gates for liquid sampling in single and multi-layer microfluidic devices. Scalability of our strategy was demonstrated by simultaneously addressing 128 bubble gates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".