scholarly journals Magnetic digital microfluidics – a review

Lab on a Chip ◽  
2017 ◽  
Vol 17 (6) ◽  
pp. 994-1008 ◽  
Author(s):  
Yi Zhang ◽  
Nam-Trung Nguyen
Keyword(s):  
Digital Microfluidics ◽  
Open Surface ◽  
Microfluidic Platform ◽  
Digital Microfluidic

A magnetic digital microfluidic platform manipulates droplets on an open surface.

Digital microfluidics using a differentially polarized interface (DPI) to enhance translational force

Lab on a Chip ◽  
2018 ◽  
Vol 18 (21) ◽  
pp. 3293-3302 ◽  
Author(s):  
Md Enayet Razu ◽  
Jungkyu Kim
Keyword(s):  
Low Voltage ◽  
Digital Microfluidics ◽  
Microfluidic Platform ◽  
Digital Microfluidic

A low-voltage and differentially polarized digital microfluidic platform is developed by enhancing the electromechanical force for droplet translation.

Digital microfluidics for spheroid-based invasion assays

Lab on a Chip ◽  
2016 ◽  
Vol 16 (8) ◽  
pp. 1505-1513 ◽  
Author(s):  
Brian F. Bender ◽  
Andrew. P. Aijian ◽  
Robin. L. Garrell
Keyword(s):  
Drug Discovery ◽  
Cell Invasion ◽  
Cell Biology ◽  
Digital Microfluidics ◽  
Three Dimensional ◽  
Multicellular Spheroids ◽  
Microfluidic Platform ◽  
Digital Microfluidic ◽  
Biology Research ◽  
Invasion Assays

A digital microfluidic platform that enables the formation, gel encapsulation, and assaying of three-dimensional multicellular spheroids is described. Such a platform can facilitate automation of cell invasion assays for cell biology research and drug discovery.

A droplet-to-digital (D2D) microfluidic device for single cell assays

Lab on a Chip ◽  
2015 ◽  
Vol 15 (1) ◽  
pp. 225-236 ◽  
Author(s):  
Steve C. C. Shih ◽  
Philip C. Gach ◽  
Jess Sustarich ◽  
Blake A. Simmons ◽  
Paul D. Adams ◽  
...  
Keyword(s):  
Single Cell ◽  
Microfluidic Device ◽  
Digital Microfluidics ◽  
Microfluidic Platform ◽  
Cell Assays ◽  
Digital Microfluidic

We have developed a new hybrid droplet-to-digital microfluidic platform (D2D) that integrates droplet-in-channel microfluidics with digital microfluidics for performing multi-step single cell assays.

scholarly journals Digital Microfluidic Platform for Multiplexing Enzyme Assays: Implications for Lysosomal Storage Disease Screening in Newborns

2011 ◽  
Vol 57 (10) ◽  
pp. 1444-1451 ◽  
Author(s):  
Ramakrishna S Sista ◽  
Allen E Eckhardt ◽  
Tong Wang ◽  
Carrie Graham ◽  
Jeremy L Rouse ◽  
...  
Keyword(s):  
Newborn Screening ◽  
Incubation Time ◽  
Digital Microfluidics ◽  
Dried Blood Spots ◽  
Reference Laboratory ◽  
Enzymatic Analysis ◽  
Lysosomal Storage ◽  
Microfluidic Platform ◽  
Enzyme Replacement ◽  
Digital Microfluidic

BACKGROUND Newborn screening for lysosomal storage diseases (LSDs) has been gaining considerable interest owing to the availability of enzyme replacement therapies. We present a digital microfluidic platform to perform rapid, multiplexed enzymatic analysis of acid α-glucosidase (GAA) and acid α-galactosidase to screen for Pompe and Fabry disorders. The results were compared with those obtained using standard fluorometric methods. METHODS We performed bench-based, fluorometric enzymatic analysis on 60 deidentified newborn dried blood spots (DBSs), plus 10 Pompe-affected and 11 Fabry-affected samples, at Duke Biochemical Genetics Laboratory using a 3-mm punch for each assay and an incubation time of 20 h. We used a digital microfluidic platform to automate fluorometric enzymatic assays at Advanced Liquid Logic Inc. using extract from a single punch for both assays, with an incubation time of 6 h. Assays were also performed with an incubation time of 1 h. RESULTS Assay results were generally comparable, although mean enzymatic activity for GAA using microfluidics was approximately 3 times higher than that obtained using bench-based methods, which could be attributed to higher substrate concentration. Clear separation was observed between the normal and affected samples at both 6- and 1-h incubation times using digital microfluidics. CONCLUSIONS A digital microfluidic platform compared favorably with a clinical reference laboratory to perform enzymatic analysis in DBSs for Pompe and Fabry disorders. This platform presents a new technology for a newborn screening laboratory to screen LSDs by fully automating all the liquid-handling operations in an inexpensive system, providing rapid results.

An integrated droplet-digital microfluidic system for on-demand droplet creation, mixing, incubation, and sorting

Lab on a Chip ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 524-535 ◽  
Author(s):  
Fatemeh Ahmadi ◽  
Kenza Samlali ◽  
Philippe Q. N. Vo ◽  
Steve C. C. Shih
Keyword(s):  
Ionic Liquid ◽  
Digital Microfluidics ◽  
Microfluidic System ◽  
Yeast Mutant ◽  
Microfluidic Platform ◽  
On Demand ◽  
Digital Microfluidic ◽  
Mutant Cells

A new microfluidic platform that integrates droplet and digital microfluidics to automate a variety of fluidic operations. The platform was applied to culturing and to selecting yeast mutant cells in ionic liquid.

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.

Integrated Optical Sensor in a Digital Microfluidic Platform

2008 ◽  
Vol 8 (5) ◽  
pp. 628-635 ◽  
Author(s):  
Lin Luan ◽  
Randall D. Evans ◽  
Nan M. Jokerst ◽  
Richard B. Fair
Keyword(s):  
Optical Sensor ◽  
Microfluidic Platform ◽  
Integrated Optical ◽  
Digital Microfluidic ◽  
Integrated Optical Sensor

A digital microfluidic platform for the automation of quantitative biomolecular assays

Lab on a Chip ◽  
2010 ◽  
Vol 10 (6) ◽  
pp. 685-691 ◽  
Author(s):  
Erik C. Jensen ◽  
Bharath P. Bhat ◽  
Richard A. Mathies
Keyword(s):  
Microfluidic Platform ◽  
Biomolecular Assays ◽  
Digital Microfluidic

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.

scholarly journals Driving and sorting of the fluorescent droplets on digital microfluidic platform

2018 ◽  
Vol 22 (11) ◽  
Author(s):  
Xingbo Wang ◽  
Yuhao Piao ◽  
Yan Su ◽  
Weiqiang Wang
Keyword(s):  
Microfluidic Platform ◽  
Digital Microfluidic
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