3D free-assembly modular microfluidics inspired by movable type printing
Bibliographic record
Abstract
Reconfigurable modular microfluidics presents an opportunity for flexibly constructing prototypes of advanced microfluidic systems. Nevertheless, the strategy of directly integrating modules cannot easily fulfill the requirements of common applications, e.g., the incorporation of materials with biochemical compatibility and optical transparency and the execution of small batch production of disposable chips for laboratory trials and initial tests. Here, we propose a manufacturing scheme inspired by the movable type printing technique to realize 3D free-assembly modular microfluidics. Double-layer 3D microfluidic structures can be produced by replicating the assembled molds. A library of modularized molds is presented for flow control, droplet generation and manipulation and cell trapping and coculture. In addition, a variety of modularized attachments, including valves, light sources and microscopic cameras, have been developed with the capability to be mounted onto chips on demand. Microfluidic systems, including those for concentration gradient generation, droplet-based microfluidics, cell trapping and drug screening, are demonstrated. This scheme enables rapid prototyping of microfluidic systems and construction of on-chip research platforms, with the intent of achieving high efficiency of proof-of-concept tests and small batch manufacturing.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".