Microfluidic Gasketless Interconnects Sealed by Superhydrophobic Surfaces
Bibliographic record
Abstract
Existing methods for sealing chip-to-chip (or module-to-motherboard) microfluidic interconnects commonly use additional interconnect components (O-rings, gaskets, and tubing), and manual handling expertise for assembly. Novel gasketless superhydrophobic fluidic interconnects (GSFIs) sealed by transparent superhydrophobic surfaces, forming liquid bridges between the fluidic ports for fluidic passages were demonstrated. Two test platforms were designed, fabricated, and evaluated, a multi-port chip system (ten interconnects) and a modules-on-amotherboard system (four interconnects). System assembly in less than 3 sec was done by embedded magnets and pin-in-V-groove structures. Flow tests with deionized (DI) water, ethanol/water mixture, and plasma confirmed no leakage through the gasketless interconnects up to a maximum flow rate of 100 μL/min for the multi-port chip system. The modules-on-a-motherboard system showed no leakage of water at a flow rate of 20 μL/min and a pressure drop of 3.71 psi. Characterization of the leakage pressure as a function of the surface tension of the sample liquid in the multi-port chip system revealed that lower surface tension of the liquid led to lower static water contact angles on the superhydrophobic-coated substrate and lower leakage pressures. The high-density, rapidly assembled, gasketless interconnect technology will open up new avenues for chip-to-chip fluid transport in complex microfluidic modular systems. [2020-0168].
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".