Assembly of Multi‐Compartment Cell Mimics by Droplet‐Based Microfluidics
Bibliographic record
Abstract
Abstract In recent years, there has been a growing interest in multi‐compartment systems as a means of developing materials that mimic the structure and function of biological cells. These hierarchical systems, including artificial cells and cell‐like reactors, can efficiently perform biochemical tasks by exploiting compartmentalization inspired by biological systems. However, the bottom‐up design of cell mimics presents significant challenges due to the need for precise and efficient assembly of components. This short review examines recent advances in droplet‐based microfluidics (DBM), which has emerged as a powerful technique for creating cell‐like systems with multi‐compartment architectures, precise composition, and biomimetic functionality. DBM has proven to be a reliable method for generating populations of cell‐mimics with a compartment‐in‐compartment structure, some of which have adaptable properties that resemble the dynamic properties of natural cells. Notable examples will be discussed to illustrate how droplet‐based microfluidics provides a versatile approach to create, manipulate, and study cell‐mimics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".