Multilayer Hybrid Microfluidics: A Digital-to-Channel Interface for Sample Processing and Separations
Bibliographic record
Abstract
Microchannels can separate analytes faster with higher resolution, higher efficiency and with lower reagent consumption than typical column techniques. Unfortunately, an impediment in the path toward fully integrated microchannel-based laboratories-on-a-chip is the integration of preseparation sample processing. In contrast, the alternative format of digital microfluidics (DMF), in which discrete droplets are manipulated on an array of electrodes, is well-suited for carrying out sequential chemical reactions such as those commonly employed in proteomic sample preparation. We recently reported a new paradigm of "hybrid microfluidics," integrating DMF with microchannels for in-line sample processing and separations. Here, we build on our initial efforts, introducing a second-generation hybrid microfluidic device architecture. In the new multilayer design, droplets are manipulated by DMF in the two-plate format, an improvement that facilitates dispensing samples from reservoirs, as well as droplet splitting and storage for subsequent analysis. To demonstrate the capabilities of the new method, we implemented an on-chip serial dilution experiment, as well as multistep enzymatic digestion. Given the myriad applications requiring preprocessing and chemical separations, the hybrid digital-channel format has the potential to become a powerful new tool for micro total analysis systems.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".