Design of an interface to allow microfluidic electrophoresis chips to drink from the fire hose of the external environment
Bibliographic record
Abstract
An interface design is presented that facilitates automated sample introduction into an electrokinetic microchip, without perturbing the liquids within the microfluidic device. The design utilizes an interface flow channel with a volume flow resistance that is 0.54-4.1 x 10(6) times lower than the volume flow resistance of the electrokinetic fluid manifold used for mixing, reaction, separation, and analysis. A channel, 300 microm deep, 1 mm wide and 15-20 mm long, was etched in glass substrates to create the sample introduction channel (SIC) for a manifold of electrokinetic flow channels in the range of 10-13 microm depth and 36-275 microm width. Volume flow rates of up to 1 mL/min were pumped through the SIC without perturbing the solutions within the electrokinetic channel manifold. Calculations support this observation, suggesting a leakage flow to electroosmotic flow ratio of 0.1:1% in the electrokinetic channels, arising from 66-700 microL/min pressure-driven flow rates in the SIC. Peak heights for capillary electrophoresis separations in the electrokinetic flow manifold showed no dependence on whether the SIC pump was on or off. On-chip mixing, reaction and separation of anti-ovalbumin and ovalbumin could be performed with good quantitative results, independent of the SIC pump operation. Reproducibility of injection performance, estimated from peak height variations, ranged from 1.5-4%, depending upon the device design and the sample composition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".