Disposable silicon-glass microfluidic devices: precise, robust and cheap
Bibliographic record
Abstract
Si-glass microfluidics have long provided unprecedented precision, robustness and optical clarity. However, chip fabrication is costly (∼500 USD per chip) and in practice, devices are not heavily reused. We present a method to reduce the cost-per-chip by two orders of magnitude (∼5 USD per chip), rendering Si-glass microfluidics disposable for many applications. The strategy is based on reducing the area of the chip and a whole-chip manifolding strategy that achieves reliable high-pressure high-temperature fluid connectivity. The resulting system was validated at 130 bar and 95 °C and demonstrated in both energy and carbon capture applications. We studied heavy oil flooding with brine, polymer, and surfactant polymer solutions and found the surfactant polymer as the most effective solution which recovered ∼80% of the oil with the least amount of injection while maintaining a relatively uniform displacement front. In a carbon capture application, we measured the dilation of an emerging ionic liquid analog, choline chloride with urea, in gaseous and supercritical CO2. Previously restricted to niche microfluidic applications, the approach here brings the established benefits of Si-glass microfluidics to a broad range of applications.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".