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Editorial for the Special Issue on Droplet Microfluidics

Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA
Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
Cancer Center, Stony Brook School of Medicine, Stony Brook, NY 11794, USA
Institute for Engineering Driven Medicine, Stony Brook University, Stony Brook, NY 11794, USA
Cold Spring Harbor Laboratories, Cold Spring Harbor, NY 11724, USA
Authors to whom correspondence should be addressed.
Micromachines 2020, 11(12), 1086;
Received: 1 December 2020 / Accepted: 2 December 2020 / Published: 8 December 2020
(This article belongs to the Special Issue Droplet Microfluidics)
Emulsions, which are collections of immiscible droplets, have elicited scientific and commercial interests for decades. However, droplet microfluidics has only recently emerged due to advances in microfluidics and physical chemistry. Droplet microfluidics uniquely allows encapsulation into monodisperse and stable droplets. Those properties represent the two pillars of the technology because they enable manipulating droplets as independent micro-reactors both on-chip and off-chip. Droplets are generated and processed with high precision in microfluidic circuits that can include different modules in series.
Since its inception, droplet microfluidics has inspired numerous research topics, ranging from fluid hydrodynamics to biology, chemistry, and material sciences. The field is indeed highly interdisciplinary as it combines fluid dynamics, microfabrication, physical-chemistry, chemistry, and biology. Droplet microfluidics has also garnered commercial success through applications such as digital PCR or single-cell genomics.
This special issue is a collection of eight articles that perfectly illustrate the breadth of this vibrant field. It represents a testimony to the creativity involved in the development of droplet microfluidics. Six articles cover automation and templating capabilities of droplet microfluidics for biological, chemical, and material sciences applications; the last two articles present novel modules that proposes a solution to the limited mixing in single phase microfluidics and that provides new sorting abilities for biological applications.
Droplet microfluidics promises workflow automation with improved throughput and reduced reagent consumption. The paper by Lindong Weng et al. [1] reviews the advantages of droplet microfluidics to screen large libraries for directed evolution. Directed evolution is arguably the initial impetus behind the development of droplet microfluidics because droplets readily link the genotype (coding sequence) to the genotype (protein) via encapsulation. The paper also provides a thorough review of the microfluidic modules used to manipulate and process droplets at high throughput and thus serves as a perfect introduction to the technology. Mark Davies et al. [2] present a droplet-based commercial system’s theory and practice to generate combinatorial drug libraries for high throughput screening. The combinatorial power of droplets combined with reagent consumption reduction surpasses conventional technologies such as robotics for large and complex screening strategies. Hoon Suk Rho et al. [3] introduce a system that combines droplet microfluidics with on-chip valves, two technologies that have been recently combined together. Their low throughput platform enables fine droplet control and the design of complex chemical and biological reactions. Finally, Nan Shi et al. [4] demonstrate a screening platform with automated calibration that provides real-time correction for fluorescence drift. The repeated calibration is essential to improve the quality of analytical assays, especially those that rely on expensive reagents.
Microfluidics droplets can be used as templates for manufacturing original microscale objects. For instance, Jianhua Guo et al. [5] harness osmotic pressure to precisely tune the thickness of the ultrathin shell of microparticles manufactured at high throughput. Employing unusual fluids, Qingming Hu et al. [6] demonstrate the generation of liquid metal droplets numerically and experimentally. These two papers exemplify the ability to use droplet microfluidics to create new materials with novel properties and applications in very diverse fields.
The approach of Xiaoyu Jia et al. [7] contrasts with the typical use of droplets as microreactors by utilizing them as actuators. They overcome the limited mixing of single-phase microfluidics by creating chaotic convective turbulent flow with air bubbles. Finally, Chandler Dobso et al. [8] use a photo-reactive surfactant to tag droplets of interest that can then be passively sorted out due to the induced change in interfacial energy. Unlike conventional sorting methods, where the sorting decision is made just before sorting, this functionality temporaly uncouples observation, decision algorithm, and sorting. This uncoupling opens up the ability of long term observation of droplets before sorting.
We are grateful to all the authors who submitted their papers to this special issue dedicated to droplet microfluidics. We are also indebted to all the reviewers who dedicated their time and helped improve the quality of the submitted papers.

Conflicts of Interest

The author declares no conflict of interest.


  1. Weng, L.; Spoonamore, J.E. Droplet Microfluidics-Enabled High-Throughput Screening for Protein Engineering. Micromachines 2019, 10, 734. [Google Scholar] [CrossRef] [PubMed][Green Version]
  2. Davies, M.; Abubaker, M.; Bible, L. A Flexible, Microfluidic, Dispensing System for Screening Drug Combinations. Micromachines 2020, 11, 943. [Google Scholar] [CrossRef] [PubMed]
  3. Rho, H.S.; Gardeniers, H. Microfluidic Droplet-Storage Array. Micromachines 2020, 11, 608. [Google Scholar] [CrossRef] [PubMed]
  4. Shi, N.; Easley, C.J. Programmable µChopper Device with On-Chip Droplet Mergers for Continuous Assay Calibration. Micromachines 2020, 11, 620. [Google Scholar] [CrossRef] [PubMed]
  5. Guo, J.; Hou, L.; Hou, J.; Yu, J.; Hu, Q. Generation of Ultra-Thin-Shell Microcapsules Using Osmolarity-Controlled Swelling Method. Micromachines 2020, 11, 444. [Google Scholar] [CrossRef] [PubMed][Green Version]
  6. Hu, Q.; Jiang, T.; Jiang, H. Numerical Simulation and Experimental Validation of Liquid Metal Droplet Formation in a Co-Flowing Capillary Microfluidic Device. Micromachines 2020, 11, 169. [Google Scholar] [CrossRef] [PubMed][Green Version]
  7. Jia, X.; Che, B.; Jing, G.; Zhang, C. Air-Bubble Induced Mixing: A Fluidic Mixer Chip. Micromachines 2020, 11, 195. [Google Scholar] [CrossRef] [PubMed][Green Version]
  8. Dobson, C.; Zielke, C.; Pan, C.W.; Feit, C.; Abbyad, P. Method for Passive Droplet Sorting after Photo-Tagging. Micromachines 2020, 11, 964. [Google Scholar] [CrossRef] [PubMed]
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Brouzes, E.; Li, S. Editorial for the Special Issue on Droplet Microfluidics. Micromachines 2020, 11, 1086.

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Brouzes E, Li S. Editorial for the Special Issue on Droplet Microfluidics. Micromachines. 2020; 11(12):1086.

Chicago/Turabian Style

Brouzes, Eric, and Siran Li. 2020. "Editorial for the Special Issue on Droplet Microfluidics" Micromachines 11, no. 12: 1086.

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