Development and future of droplet microfluidics
Over the past two decades, advances in droplet-based microfluidics have facilitated new approaches to process and analyze samples with unprecedented levels of precision and throughput. A wide variety of applications has been inspired across multiple disciplines ranging from materials science to biol...
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| Published in | Lab on a chip Vol. 24; no. 5; pp. 1135 - 1153 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Royal Society of Chemistry
27.02.2024
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| Subjects | |
| Online Access | Get full text |
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| Summary: | Over the past two decades, advances in droplet-based microfluidics have facilitated new approaches to process and analyze samples with unprecedented levels of precision and throughput. A wide variety of applications has been inspired across multiple disciplines ranging from materials science to biology. Understanding the dynamics of droplets enables optimization of microfluidic operations and design of new techniques tailored to emerging demands. In this review, we discuss the underlying physics behind high-throughput generation and manipulation of droplets. We also summarize the applications in droplet-derived materials and droplet-based lab-on-a-chip biotechnology. In addition, we offer perspectives on future directions to realize wider use of droplet microfluidics in industrial production and biomedical analyses.
This review introduces the development of droplet microfluidics by explaining the physical mechanisms of droplet generation, discussing various approaches in manipulating droplets, and summarizing key applications in material science and biological analyses. |
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| Bibliography: | Huidan Zhang received her B.S. degree from the Department of Clinical Medicine at China Medical University in 2001, and M.S. degree from the Department of Gynecology and Obstetrics at Shengjing Hospital of China Medical University in 2004. After that she joined the Department of Cell Biology at China Medical University, where she received her PhD in Cell Biology in 2009 and become an Associate Professor in 2011. Since then, she has been doing research in Weitz group at Harvard John A. Paulson School of Engineering and Applied Sciences. Her research interests focus on droplet-based lab-on-a-chip applications in biotechnology, such as ddPCR, single-cell & pathogen sequencing and high-throughput enzyme evolution. She is also an entrepreneur to apply these techniques to the fields of tumor-targeted therapy and diagnosis of infectious diseases. Weitz received his PhD in physics from Harvard University and then joined Exxon Research and Engineering Company, where he worked for nearly 18 years. He then became a Professor of physics at the University of Pennsylvania and moved to Harvard at the end of the last millennium as professor of physics and applied physics. He leads a group studying soft matter science with a focus on materials science, biophysics and microfluidics. Several startup companies have come from his lab to commercialize research concepts. Anderson Shum received his PhD in Applied Physics from Harvard University. He is currently a Professor in the Department of Mechanical Engineering at the University of Hong Kong and a core member in the Biomedical Engineering Programme at the University of Hong Kong. He is also the Director of the Advanced Biomedical Instrumentation Centre in Hong Kong. He leads a group studying soft matter and microfluidics. His research interests include liquid-liquid phase separation, emulsions, microfluidics, emulsion-templated materials and soft matter. Lang Nan received his PhD in Mechanical Engineering from the University of Hong Kong in 2021. He currently works as a postdoctoral researcher in Advanced Biomedical Instrumentation Centre in Hong Kong. His research interests include droplet-based microfluidics, point-of-care testing, and biomedical engineering. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 |
| ISSN: | 1473-0197 1473-0189 1473-0189 |
| DOI: | 10.1039/d3lc00729d |