Machine Learning-Driven Innovations in Microfluidics
Jinseok Park, Yang Woo Kim, Hee-Jae Jeon · 2024
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Description
Jinseok Park, Yang Woo Kim, and Hee-Jae Jeon review how machine learning is reshaping microfluidics, the craft of steering tiny fluid volumes on chips. Their 2024 review starts from the field's promise: such devices made biosensing precise and small. The flood of design choices and data, though, strains human handling.
Learning algorithms now sharpen each stage. They improve design accuracy before a chip is built, run experiments on their own, read results in real time, and sort the heavy diagnostic data sets the chips produce. The review tours recent gains in health diagnostics, environmental monitoring, and synthetic biology. The authors frame the pairing as the path to intelligent lab systems that largely run themselves.
Catalog record
| CREATOR | Jinseok Park, Yang Woo Kim, Hee-Jae Jeon |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 39 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |