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Zeng X, Guo X, Jiang S, Yang X, Zhong Z, Liu S, Zhu Z, Song J, Yang C. Digital-scRRBS: A Cost-Effective, Highly Sensitive, and Automated Single-Cell Methylome Analysis Platform via Digital Microfluidics. Anal Chem 2023; 95:13313-13321. [PMID: 37616549 DOI: 10.1021/acs.analchem.3c02484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
Single-cell DNA methylation sequencing is highly effective for identifying cell subpopulations and constructing epigenetic regulatory networks. Existing methylome analyses require extensive starting materials and are costly, complex, and susceptible to contamination, thereby impeding the development of single-cell methylome technology. In this work, we report digital microfluidics-based single-cell reduced representation bisulfite sequencing (digital-scRRBS), the first microfluidics-based single-cell methylome library construction platform, which is an automatic, effective, reproducible, and reagent-efficient technique to dissect the single-cell methylome. Using our digital microfluidic chip, we isolated single cells in 15 s and successfully constructed single-cell methylation sequencing libraries with a unique genome mapping rate of up to 53.6%, covering up to 2.26 million CpG sites. Digital-scRRBS demonstrates a high capacity for distinguishing cell identity and tracking DNA methylation during drug administration. Digital-scRRBS expands the applicability of single-cell methylation methods as a versatile tool for epigenetic analysis of rare cells and populations with high levels of heterogeneity.
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Affiliation(s)
- Xi Zeng
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical of Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering and Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
| | - Xiaoxu Guo
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical of Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering and Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
| | - Shaowei Jiang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical of Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering and Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
| | - Xiaoping Yang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical of Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering and Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
| | - Zhixing Zhong
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical of Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering and Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
| | - Siyu Liu
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical of Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering and Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
| | - Zhi Zhu
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical of Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering and Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
| | - Jia Song
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Chaoyong Yang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical of Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering and Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
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Bugeja N, Oliver C, McGrath N, McGuire J, Yan C, Carlysle-Davies F, Reid M. Teaching old presumptive tests new digital tricks with computer vision for forensic applications. DIGITAL DISCOVERY 2023; 2:1143-1151. [PMID: 38013815 PMCID: PMC10408571 DOI: 10.1039/d3dd00066d] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 06/26/2023] [Indexed: 11/29/2023]
Abstract
Presumptive (or 'spot') tests have served forensic scientists, law enforcement, and legal practitioners for over a hundred years. Yet, the intended design of such tests, enabling quick identification of drugs by-eye, also hides their full potential. Here, we report the development and application of time-resolved imaging methods of reactions attending spot tests for amphetamines, barbiturates, and benzodiazepines. Analysis of the reaction videos helps distinguish drugs within the same structural class that, by-eye, are judged to give the same qualitative spot test result. It is envisaged that application of these results will bridge the existing suite of field and lab-based confirmatory forensic tests, and support a broader range of colorimetric sensing technologies.
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Affiliation(s)
- Nathalie Bugeja
- Department of Pure and Applied Chemistry, University of Strathclyde Glasgow UK
| | - Cameron Oliver
- Department of Pure and Applied Chemistry, University of Strathclyde Glasgow UK
| | - Nicole McGrath
- Department of Pure and Applied Chemistry, University of Strathclyde Glasgow UK
| | - Jake McGuire
- Department of Pure and Applied Chemistry, University of Strathclyde Glasgow UK
| | - Chunhui Yan
- Department of Pure and Applied Chemistry, University of Strathclyde Glasgow UK
| | | | - Marc Reid
- Department of Pure and Applied Chemistry, University of Strathclyde Glasgow UK
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Luo Z, Huang B, Xu J, Wang L, Huang Z, Cao L, Liu S. Machine vision-based driving and feedback scheme for digital microfluidics system. OPEN CHEM 2021. [DOI: 10.1515/chem-2021-0060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
A digital microfluidic system based on electrowetting-on-dielectric is a new technology for controlling microliter-sized droplets on a plane. By applying a voltage signal to an electrode, the droplets can be controlled to move, merge, and split. Due to device design, fabrication, and runtime uncertainties, feedback control schemes are necessary to ensure the reliability and accuracy of a digital microfluidic system for practical application. The premise of feedback is to obtain accurate droplet position information. Therefore, there is a strong need to develop a digital microfluidics system integrated with driving, position, and feedback functions for different areas of study. In this article, we propose a driving and feedback scheme based on machine vision for the digital microfluidics system. A series of experiments including droplet motion, merging, status detection, and self-adaption are performed to evaluate the feasibility and the reliability of the proposed scheme. The experimental results show that the proposed scheme can accurately locate multiple droplets and improve the success rate of different applications. Furthermore, the proposed scheme provides an experimental platform for scientists who focused on the digital microfluidics system.
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Affiliation(s)
- Zhijie Luo
- College of Information Science and Technology, Zhongkai University of Agriculture and Engineering , Guangzhou 510225 , China
- Smart Agriculture Engineering Research Center of Guangdong Higher Education Institutes, Zhongkai University of Agriculture and Engineering , Guangzhou 510225 , China
- Guangzhou Key Laboratory of Agricultural Products Quality & Safety Traceability Information Technology, Zhongkai University of Agriculture and Engineering , Guangzhou 510225 , China
| | - Bangrui Huang
- College of Information Science and Technology, Zhongkai University of Agriculture and Engineering , Guangzhou 510225 , China
| | - Jiazhi Xu
- College of Information Science and Technology, Zhongkai University of Agriculture and Engineering , Guangzhou 510225 , China
| | - Lu Wang
- College of Information Science and Technology, Zhongkai University of Agriculture and Engineering , Guangzhou 510225 , China
| | - Zitao Huang
- College of Information Science and Technology, Zhongkai University of Agriculture and Engineering , Guangzhou 510225 , China
| | - Liang Cao
- College of Information Science and Technology, Zhongkai University of Agriculture and Engineering , Guangzhou 510225 , China
| | - Shuangyin Liu
- College of Information Science and Technology, Zhongkai University of Agriculture and Engineering , Guangzhou 510225 , China
- Smart Agriculture Engineering Research Center of Guangdong Higher Education Institutes, Zhongkai University of Agriculture and Engineering , Guangzhou 510225 , China
- Guangzhou Key Laboratory of Agricultural Products Quality & Safety Traceability Information Technology, Zhongkai University of Agriculture and Engineering , Guangzhou 510225 , China
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Wang Y, Zhao H, Liu X, Lin W, Jiang Y, Li J, Zhang Q, Zheng G. An integrated digital microfluidic bioreactor for fully automatic screening of microalgal growth and stress-induced lipid accumulation. Biotechnol Bioeng 2020; 118:294-304. [PMID: 32946108 DOI: 10.1002/bit.27570] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 09/06/2020] [Accepted: 09/15/2020] [Indexed: 01/25/2023]
Abstract
Algae are the promising feedstock of biofuel. The screening of competent species and proper fertilizer supply is of the most important tasks. To accelerate this rather slow and laborious step, we developed an integrated high-throughput digital microfluidic (DMF) system that uses a discrete droplet to serve as a microbioreactor, encapsulating microalgal cells. On the basis of fundamental understanding of various droplet hydrodynamics induced by the existence of different sorts of ions and biological species, incorporation of capacitance-based position estimator, electrode-saving-based compensation, and deterministic splitting approach, was performed to optimize the DMF bioreactor. Thus, it enables all processes (e.g., nutrient gradient generation, algae culturing, and analyzing of growth and lipid accumulation) occurring automatically on-chip especially in a high-fidelity way. The ability of the system to compare different microalgal strains on-chip was investigated. Also, the Chlorella sp. were stressed by various conditions and then growth and oil accumulation were analyzed and compared, which demonstrated its potential as a powerful tool to investigate microalgal lipid accumulation at significantly lower laborites and reduced time.
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Affiliation(s)
- Yunhua Wang
- Institute of Environmental and Chemical Engineering, Dalian University, Dalian, China
| | - Hongyu Zhao
- Institute of Environmental and Chemical Engineering, Dalian University, Dalian, China
| | - Xianming Liu
- Department of Biotechnology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Wang Lin
- Institute of Environmental and Chemical Engineering, Dalian University, Dalian, China
| | - Youwei Jiang
- Department of Materials Science and Engineering, South University of Science and Technology, Shenzhen, China
| | - Jianfeng Li
- Department of R&D, Jiangsu Celyee Cell Technology Research Institute, Nanjing, China
| | - Qian Zhang
- Institute of Environmental and Chemical Engineering, Dalian University, Dalian, China
| | - Guoxia Zheng
- Institute of Environmental and Chemical Engineering, Dalian University, Dalian, China
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6
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Multi-purpose machine vision platform for different microfluidics applications. Biomed Microdevices 2019; 21:68. [DOI: 10.1007/s10544-019-0401-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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In Situ Image Acquisition and Measurement of Microdroplets Based on Delay Triggering. MICROMACHINES 2019; 10:mi10020148. [PMID: 30813297 PMCID: PMC6412821 DOI: 10.3390/mi10020148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2019] [Revised: 02/14/2019] [Accepted: 02/20/2019] [Indexed: 11/16/2022]
Abstract
An in situ image acquisition apparatus based on delay triggering for visualizing microdroplets formation is described. The imaging system includes a charge-coupled device camera, a motion control card, a driving circuit, a time delay triggering circuit, and a light source. By adjusting the varying trigger delay time which is synchronized with respect to the signal for jetting, the steady sequential images of the droplet flying in free space can be captured real-time by the system. Several image processing steps are taken to measure the diameters and coordinates of the droplets. Also, the jetting speeds can be calculated according to the delay time interval. For glycerin/water (60:40, mass ratio), under the given conditions of the self-made pneumatically diaphragm-driven drop-on-demand inkjet apparatus, the average of diameter and volume are measured as 266.8 μm and 9944 pL, respectively, and the maximum average velocity of the microdroplets is 0.689 m/s. Finally, the imaging system is applied to measure the volume of 200 microsolder balls generated from the inkjet apparatus. The average diameter is 87.96 μm, and the relative standard deviation is 0.83%. The results show good reproducibility. Unlike previous stroboscopic techniques, the present in situ imaging system which is absence of instantaneous high intensity light employs two control signals to stimulate the microdroplet generator and the charge-coupled device (CCD) camera. Hence, the system can avoid the desynchronization problem of signals which control the strobe light-emitting diode (LED) light source and the camera in previous equipment. This technology is a reliable and cost-effective approach for capturing and measuring microdroplets.
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Yafia M, Emran BJ, Najjaran H. Digital Microfluidic Systems: Fundamentals, Configurations, Techniques, and Applications. MICROFLUIDICS: FUNDAMENTAL, DEVICES AND APPLICATIONS 2018:175-209. [DOI: 10.1002/9783527800643.ch5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Ali SS, Ibrahim M, Sinanoglu O, Chakrabarty K, Karri R. Security Assessment of Cyberphysical Digital Microfluidic Biochips. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016; 13:445-458. [PMID: 26701892 DOI: 10.1109/tcbb.2015.2509991] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
A digital microfluidic biochip (DMFB) is an emerging technology that enables miniaturized analysis systems for point-of-care clinical diagnostics, DNA sequencing, and environmental monitoring. A DMFB reduces the rate of sample and reagent consumption, and automates the analysis of assays. In this paper, we provide the first assessment of the security vulnerabilities of DMFBs. We identify result-manipulation attacks on a DMFB that maliciously alter the assay outcomes. Two practical result-manipulation attacks are shown on a DMFB platform performing enzymatic glucose assay on serum. In the first attack, the attacker adjusts the concentration of the glucose sample and thereby modifies the final result. In the second attack, the attacker tampers with the calibration curve of the assay operation. We then identify denial-of-service attacks, where the attacker can disrupt the assay operation by tampering either with the droplet-routing algorithm or with the actuation sequence. We demonstrate these attacks using a digital microfluidic synthesis simulator. The results show that the attacks are easy to implement and hard to detect. Therefore, this work highlights the need for effective protections against malicious modifications in DMFBs.
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Ibrahim M, Chakrabarty K. Efficient Error Recovery in Cyberphysical Digital-Microfluidic Biochips. ACTA ACUST UNITED AC 2015. [DOI: 10.1109/tmscs.2015.2478457] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Sotiropoulou CL, Voudouris L, Gentsos C, Demiris AM, Vassiliadis N, Nikolaidis S. Real-time machine vision FPGA implementation for microfluidic monitoring on Lab-on-Chips. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2014; 8:268-77. [PMID: 24875286 DOI: 10.1109/tbcas.2013.2260338] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
A machine vision implementation on a field-programmable gate array (FPGA) device for real-time microfluidic monitoring on Lab-On-Chips is presented in this paper. The machine vision system is designed to follow continuous or plug flows, for which the menisci of the fluids are always visible. The system discriminates between the front or "head" of the flow and the back or "tail" and is able to follow flows with a maximum speed of 20 mm/sec in circular channels of a diameter of 200 μm (corresponding to approx. 60 μl/sec ). It is designed to be part of a complete Point-of-Care system, which will be portable and operate in non-ideal laboratory conditions. Thus, it is able to cope with noise due to lighting conditions and small LoC displacements during the experiment execution. The machine vision system can be used for a variety of LoC devices, without the need for fiducial markers (such as redundancy patterns) for its operation. The underlying application requirements called for a complete hardware implementation. The architecture uses a variety of techniques to improve performance and minimize memory access requirements. The system input is 8 bit grayscale uncompressed video of up to 1 Mpixel resolution. The system uses an operating frequency of 170 Mhz and achieves a computational time of 13.97 ms (worst case), which leads to a throughput of 71.6 fps for 1 Mpixel video resolution.
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Sinha A, Jebrail MJ, Kim H, Patel KD, Branda SS. A versatile automated platform for micro-scale cell stimulation experiments. J Vis Exp 2013. [PMID: 23962881 DOI: 10.3791/50597] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Study of cells in culture (in vitro analysis) has provided important insight into complex biological systems. Conventional methods and equipment for in vitro analysis are well suited to study of large numbers of cells (≥ 10(5)) in milliliter-scale volumes (≥ 0.1 ml). However, there are many instances in which it is necessary or desirable to scale down culture size to reduce consumption of the cells of interest and/or reagents required for their culture, stimulation, or processing. Unfortunately, conventional approaches do not support precise and reproducible manipulation of micro-scale cultures, and the microfluidics-based automated systems currently available are too complex and specialized for routine use by most laboratories. To address this problem, we have developed a simple and versatile technology platform for automated culture, stimulation, and recovery of small populations of cells (100-2,000 cells) in micro-scale volumes (1-20 μl). The platform consists of a set of fibronectin-coated microcapillaries ("cell perfusion chambers"), within which micro-scale cultures are established, maintained, and stimulated; a digital microfluidics (DMF) device outfitted with "transfer" microcapillaries ("central hub"), which routes cells and reagents to and from the perfusion chambers; a high-precision syringe pump, which powers transport of materials between the perfusion chambers and the central hub; and an electronic interface that provides control over transport of materials, which is coordinated and automated via pre-determined scripts. As an example, we used the platform to facilitate study of transcriptional responses elicited in immune cells upon challenge with bacteria. Use of the platform enabled us to reduce consumption of cells and reagents, minimize experiment-to-experiment variability, and re-direct hands-on labor. Given the advantages that it confers, as well as its accessibility and versatility, our platform should find use in a wide variety of laboratories and applications, and prove especially useful in facilitating analysis of cells and stimuli that are available in only limited quantities.
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Affiliation(s)
- Anupama Sinha
- Department of Systems Biology, Sandia National Laboratories, USA
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Murran MA, Najjaran H. Capacitance-based droplet position estimator for digital microfluidic devices. LAB ON A CHIP 2012; 12:2053-2059. [PMID: 22510981 DOI: 10.1039/c2lc21241b] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Digital microfluidic (DMF) devices manipulate minuscule droplets through basic fluidic operations including droplet transport, mixing and splitting commonly known as the building blocks for complete laboratory analyses on a single device. A DMF device can house various chemical species and confine chemical reactions within the volume of a droplet much like a micro-reactor. The automation of fluidic protocols requires a feedback controller whose sensor is capable of locating droplets independent of liquid composition (or previous knowledge of liquid composition). In this research, we present an estimator that tracks the continuous displacement of a droplet between electrodes of a DMF device. The estimator uses a dimensionless ratio of two electrode capacitances to approximate the position of a droplet, even, in the domain between two adjacent electrodes. This droplet position estimator significantly enhances the control precision of liquid handling in DMF devices compared to that of the techniques reported in the literature. It captures the continuous displacement of a droplet; valuable information for a feedback controller to execute intricate fluidic protocols including droplet positioning between electrodes, droplet velocity and acceleration control. We propose a state estimator for tracking the continuous droplet displacement between two adjacent electrodes. The dimensionless nature of this estimator means that any droplet composition can be sensed. Thus, no calibration for each chemical species within a single DMF device is required. We present theoretical and experimental results that demonstrate the efficacy of the position estimator in approximating the position of the droplet in the interval between two electrodes.
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Affiliation(s)
- Miguel Angel Murran
- School of Engineering, University of British Columbia, Kelowna, BC, V1V 1V7, Canada
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Pollack MG, Pamula VK, Srinivasan V, Eckhardt AE. Applications of electrowetting-based digital microfluidics in clinical diagnostics. Expert Rev Mol Diagn 2011; 11:393-407. [PMID: 21545257 DOI: 10.1586/erm.11.22] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Digital microfluidics based on electrowetting is a type of microfluidic platform in which liquids are processed as individual unit-sized droplets that are dispensed from a source, merged together, split apart or transported between locations on demand. These devices are implemented using arrays of surface electrodes to control the shape and position of droplets through the electrowetting effect. A major thrust of digital microfluidics research has been the development of integrated lab-on-a-chip devices to perform clinical in vitro diagnostic assays. A variety of preparatory and analytical processes have been implemented and feasibility has been demonstrated for test types ranging from clinical chemistries to immunoassays, nucleic acid tests and cell-based assays. In this article, the current state and future potential of digital microfluidics for clinical diagnostic testing is reviewed and evaluated.
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Affiliation(s)
- Michael G Pollack
- Advanced Liquid Logic, Inc., PO Box 14025, Research Triangle Park, NC 27709, USA.
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