scholarly journals Digital Microfluidics for Automated Proteomic Processing

10.3791/1603 ◽  
2009 ◽  
Author(s):  
Mais J. Jebrail ◽  
Vivienne N. Luk ◽  
Steve C. C. Shih ◽  
Ryan Fobel ◽  
Alphonsus H. C. Ng ◽  
...  
Keyword(s):  
Digital Microfluidics

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.

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...

Additive-Free Digital Microfluidics

Langmuir ◽  
2013 ◽  
Vol 29 (28) ◽  
pp. 9024-9030 ◽  
Author(s):  
Sergio L. S. Freire ◽  
Brendan Tanner
Keyword(s):  
Digital Microfluidics

A Framework for Validation of Synthesized MicroElectrode Dot Array Actuations for Digital Microfluidic Biochips

2021 ◽  
Vol 26 (6) ◽  
pp. 1-36
Author(s):  
Pushpita Roy ◽  
Ansuman Banerjee
Keyword(s):  
Formal Analysis ◽  
Digital Microfluidics ◽  
Analysis Method ◽  
Correctness Proofs ◽  
Synthesis Tool ◽  
Laboratory Procedures ◽  
Automated Tools ◽  
Digital Microfluidic ◽  
High Level ◽  
Smooth Transformation

Digital Microfluidics is an emerging technology for automating laboratory procedures in biochemistry. With more and more complex biochemical protocols getting mapped to biochip devices and microfluidics receiving a wide adoption, it is becoming indispensable to develop automated tools and synthesis platforms that can enable a smooth transformation from complex cumbersome benchtop laboratory procedures to biochip execution. Given an informal/semi-formal assay description and a target microfluidic grid architecture on which the assay has to be implemented, a synthesis tool typically translates the high-level assay operations to low-level actuation sequences that can drive the assay realization on the grid. With more and more complex biochemical assay protocols being taken up for synthesis and biochips supporting a wider variety of operations (e.g., MicroElectrode Dot Arrays (MEDAs)), the task of assay synthesis is getting intricately complex. Errors in the synthesized assay descriptions may have undesirable consequences in assay operations, leading to unacceptable outcomes after execution on the biochips. In this work, we focus on the challenge of examining the correctness of synthesized protocol descriptions, before they are taken up for realization on a microfluidic biochip. In particular, we take up a protocol description synthesized for a MEDA biochip and adopt a formal analysis method to derive correctness proofs or a violation thereof, pointing to the exact operation in the erroneous translation. We present experimental results on a few bioassay protocols and show the utility of our framework for verifiable protocol synthesis.

Image-based feedback and analysis system for digital microfluidics

Lab on a Chip ◽  
2017 ◽  
Vol 17 (20) ◽  
pp. 3437-3446 ◽  
Author(s):  
Philippe Q. N. Vo ◽  
Mathieu C. Husser ◽  
Fatemeh Ahmadi ◽  
Hugo Sinha ◽  
Steve C. C. Shih
Keyword(s):  
Digital Microfluidics ◽  
Biological Analysis ◽  
Analysis System

A new feedback and biological analysis system for digital microfluidics that uses an imaging based setup.

Digital Microfluidics

2015 ◽  
pp. 588-595
Author(s):  
Cheuk-Wing Li ◽  
Mengsu Yang
Keyword(s):  
Digital Microfluidics
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