Deformation, speed, and stability of droplet motion in closed electrowetting-based digital microfluidics

2019 ◽  
Vol 31 (6) ◽  
pp. 062002 ◽  
Keyword(s):  
Digital Microfluidics ◽  
Droplet Motion ◽  
Deformation Speed

scholarly journals Electrowetting on Dielectric (EWOD) Device with Dimple Structures for Highly Accurate Droplet Manipulation

2019 ◽  
Vol 9 (12) ◽  
pp. 2406 ◽  
Author(s):  
Katsuo Mogi ◽  
Shungo Adachi ◽  
Naoki Takada ◽  
Tomoya Inoue ◽  
Tohru Natsume
Keyword(s):  
High Speed ◽  
Thin Sheet ◽  
Digital Microfluidics ◽  
Three Dimensional ◽  
Substrate Type ◽  
Droplet Motion ◽  
Electrowetting On Dielectric ◽  
Flat Substrate ◽  
Single Substrate ◽  
Droplet Manipulation

Digital microfluidics based on electrowetting on dielectric (EWOD) devices has potential as a fundamental technology for the accurate preparation of dangerous reagents, the high-speed dispensing of rapidly deteriorating reagents, and the fine adjustment of expensive reagents, such as the preparation of for positron emission tomography (PET). To allow single substrate type EWODs to be practically used in an automatic operation system, we developed a dimple structure as a key technique for a highly accurate droplet manipulation method. The three-dimensional shape of the dimple structure is embossed onto a disposable thin sheet. In this study, we confirmed that the dimple structure can suppress unintended droplet motion caused by unidentified factors. In addition, the stability of the droplets on the dimple structures was evaluated using a sliding experiment. On a flat substrate, the success rate of a droplet motion was lower than 70.8%, but on the dimple structure, the droplets were able to be moved along the dimple structures correctly without unintended motion caused by several environmental conditions. These results indicated that the dimple structure increased the controllability of the droplets. Hence, the dimple structure will contribute to the practical application of digital microfluidics based on single substrate type EWODs.

scholarly journals Dynamics of Electrowetting Droplet Motion in Digital Microfluidics Systems: From Dynamic Saturation to Device Physics

Micromachines ◽  
2015 ◽  
Vol 6 (6) ◽  
pp. 778-789 ◽  
Author(s):  
Weiwei Cui ◽  
Menglun Zhang ◽  
Xuexin Duan ◽  
Wei Pang ◽  
Daihua Zhang ◽  
...  
Keyword(s):  
Digital Microfluidics ◽  
Device Physics ◽  
Droplet Motion

Mechanical-activated digital microfluidics with gradient surface wettability

Lab on a Chip ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 223-232 ◽  
Author(s):  
Lin Qi ◽  
Ye Niu ◽  
Cody Ruck ◽  
Yi Zhao
Keyword(s):  
Digital Microfluidics ◽  
Hydrophobic Surface ◽  
Surface Wettability ◽  
Long Distance ◽  
Droplet Motion ◽  
Surface Patterns ◽  
Droplet Manipulation ◽  
Gradient Surface

Long-distance droplet motion and selective droplet manipulation on repeated hydrophobic surface patterns with gradient wettability by in-plane cyclic vibration.

EFFECT OF A FIRE PLUME ON SUPPRESSION SPRAY DROPLET MOTION

2006 ◽  
Vol 16 (5) ◽  
pp. 563-578 ◽  
Author(s):  
John A. Schwille ◽  
Richard M. Lueptow
Keyword(s):  
Fire Plume ◽  
Spray Droplet ◽  
Droplet Motion

Magnetic Digital Microfluidics for Point-of-Care Testing: Where Are We Now?

2020 ◽  
Vol 27 ◽  
Author(s):  
Yi Zhang
Keyword(s):  
System Integration ◽  
Diagnostic Tests ◽  
Point Of Care ◽  
Digital Microfluidics ◽  
Controlled Environment ◽  
Point Of Care Testing ◽  
Microfluidic Platforms ◽  
Interface System ◽  
Technical Issues ◽  
Sample System

: Point-of-care (POC) testing decentralizes the diagnostic tests to the sites near the patient. Many POC tests rely microfluidic platforms for sample-to-answer analysis. Compared to other microfluidic systems, magnetic digital microfluidics demonstrate compelling advantages for POC diagnostics. In this review, we have examined the capability of magnetic digital microfluidics-based POC diagnostic platforms. More importantly, we have categorized POC settings into three classes based on “where is the point”, “who to care” and “how to test”, and evaluated the suitability of magnetic digital microfluidics in various POC settings. Furthermore, we have addressed other technical issues associated with POC testing such as controlled environment, sample-system interface, system integration and information connectivity. We hope this review would provide a guideline for the future development of magnetic digital microfluidics-based platforms for POC testing.

scholarly journals Machine Learning Model of Dimensionless Numbers to Predict Flow Patterns and Droplet Characteristics for Two-Phase Digital Flows

2021 ◽  
Vol 11 (9) ◽  
pp. 4251
Author(s):  
Jinsong Zhang ◽  
Shuai Zhang ◽  
Jianhua Zhang ◽  
Zhiliang Wang
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Digital Microfluidics ◽  
Flow Patterns ◽  
Machine Learning Algorithms ◽  
Dimensionless Numbers ◽  
Two Phase ◽  
The Difference ◽  
Input Variables ◽  
Digital Microfluidic

In the digital microfluidic experiments, the droplet characteristics and flow patterns are generally identified and predicted by the empirical methods, which are difficult to process a large amount of data mining. In addition, due to the existence of inevitable human invention, the inconsistent judgment standards make the comparison between different experiments cumbersome and almost impossible. In this paper, we tried to use machine learning to build algorithms that could automatically identify, judge, and predict flow patterns and droplet characteristics, so that the empirical judgment was transferred to be an intelligent process. The difference on the usual machine learning algorithms, a generalized variable system was introduced to describe the different geometry configurations of the digital microfluidics. Specifically, Buckingham’s theorem had been adopted to obtain multiple groups of dimensionless numbers as the input variables of machine learning algorithms. Through the verification of the algorithms, the SVM and BPNN algorithms had classified and predicted the different flow patterns and droplet characteristics (the length and frequency) successfully. By comparing with the primitive parameters system, the dimensionless numbers system was superior in the predictive capability. The traditional dimensionless numbers selected for the machine learning algorithms should have physical meanings strongly rather than mathematical meanings. The machine learning algorithms applying the dimensionless numbers had declined the dimensionality of the system and the amount of computation and not lose the information of primitive parameters.

Numerical study of droplet motion on discontinuous wetting gradient surface with rough strip

2021 ◽  
Vol 33 (1) ◽  
pp. 012111
Author(s):  
Wenbin Li ◽  
Jiacai Lu ◽  
Grétar Tryggvason ◽  
Ying Zhang
Keyword(s):  
Numerical Study ◽  
Droplet Motion ◽  
Gradient Surface

scholarly journals Liquid marble based digital microfluidics – fundamentals and applications

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Chin Hong Ooi ◽  
Raja Vadivelu ◽  
Jing Jin ◽  
Sreejith Kamalalayam Rajan ◽  
Pradip Singha ◽  
...  
Keyword(s):  
Low Cost ◽  
Digital Microfluidics ◽  
Ease Of Use ◽  
Liquid Marbles ◽  
Liquid Marble

Liquid marbles are droplets with volume typically on the order of microliters coated with hydrophobic powder. The versatility, ease of use and low cost make liquid marbles an attractive platform...

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