Cap‐Drop: A Pre‐Programmed, Self‐Powered Capillary Microfluidic System for Passive Droplet Generation and 3D Cell Culture Modeling
Bibliographic record
Abstract
3D cell culture models and precision diagnostics have advanced significantly through microfluidic systems, yet their broad implementation remains limited by challenges in scalability, integration, and portability. Effective 3D cell culture models require systems that maintain sample integrity, minimize evaporation, and avoid crosstalk while handling various biofluids. However, current platforms often depend on active pumping, bulky components, and complex controls, which hinder portability, usability, and affordability. To address these challenges, the Capillary Droplet microfluidic (Cap-Drop) is presented, a novel capillary-driven platform that generates and immobilizes droplets with precision, eliminating the need for external pumps or intricate setups. Unlike conventional system, where moving droplets complicate tracking and identification, Cap-Drop ensures fixed droplet positioning, allowing seamless tracking and analysis. By integrating hydrophilic and hydrophobic materials with several innovative capillary elements -including passive vents (PV), pressure reducer (PR), stop valves (SV), delay channels, and bubble trap (BT)-Cap-Drop enables robust droplet formation (40 to 500 nL) for biofluids of varying properties. The pre-programmed design of PV in corporation with other capillary elements autonomously seals microwells (MWs), ensuring consistent sample digitization and supress risk of evaporation. Cap-Drop is optimized and offers a transformative platform for microfluidic technologies in mechanistic cellular studies, preclinical drug screening, and clinical diagnostics.
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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 | 0.001 | 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 | 0.001 |
| 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".