Microchannel-based capillary microfluidics: From simple networks to capillaric circuits
Bibliographic record
Abstract
Microfluidics and lab-on-a-chip devices demonstrated the potential advantages for the automation of laboratory workflows and the reduction of sample consumption, reaction time, and assay costs. Capillary microfluidics overcome the limitations of `lab-around-a-chip' by permitting a pre-programmed liquid delivery and flow control to be embedded in the chip without the need for complex peripheral equipment. In this presentation, we chart the progress of microchannel-based capillary microfluidics, from the early stages of the cleanroom environment to the state-of-the-art in rapid prototyping. Following recent progress, we have introduced a new terminology - capillaric circuit (CC) - that both reflects the advances and provides a clear and distinct vocabulary that overcomes the ambiguity due to the multiple usage of the word “capillary”. We briefly describe the governing parameters of self-filling microchannel-based microfluidics CC, and, by analogy with electronic circuits, the deconstruction of CCs into basic fluidic elements and components. The library of basic capillaric elements is expanding. 3D printing enables rapid production of fully functional CCs, establishing CCs as a powerful increasingly complex microfluidic technology that can serve diverse 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".