Digital Microfluidic (DMF) devices based on electrowetting on dielectric (EWOD) for biological applications

Microfluidic devices have been used in various applications including automated analysis systems, biological applications like DNA sequencing, antigen-antibody reactions, protein studies, chemical applications, single cell studies, etc. Microfluidic devices are primarily categorised into two types. First are continuous microfluidic devices. These devices consist of predefined microchannels, micro-valves, and syringe pumps. Fluid is continuously flowing in these channels. The second type is digital microfluidic platforms. In this type, MXN array of electrodes is patterned on non-conducting substrate. Fluid is discretized to form tiny droplets. These droplets are transported, mixed and split using external electric field. Digital microfluidic devices are configurable as there are no permanently etched channels. Also, they have high throughput. Multiple reactions can be performed on the same platform at the same time. The time taken to complete one reaction is less compared to the continuous devices. Thus they help in faster analysis. These devices are controlled by electrical field and thus unlike continuous devices, digital microfluidic devices are free from mechanically moving parts. Digital microfluidic devices may suffer from charge accumulation due to electrostatic forces. Also, voltage levels applied play an important role. The applied voltage has to be enough to move droplets but should not cause electrolysis of the liquid used. Also voltage switching time between electrodes and frequency applied are important. These parameters can change the mixing quality. In this work, 2D simulations of droplet manipulation due to voltage application, transport and mixing are carried out. Also digital microfluidic device is designed and fabricated to carry out biological mixing experiments

Microfluidic tools for connecting single-cell optical and gene expression phenotype

The single cell is the fundamental unit of biology. Understanding how the identity of individual cells in multicellular organisms contribute to their function remains a key question in Biology. Traditionally, most observations of cells were made through imaging-based techniques. Today, advances in Next Generation Sequencing have led to the widespread adoption of sequencing-based techniques for investigating the genotype and phenotype at single-cell resolution. Microfluidics, including droplet-based microfluidics, have been instrumental in the most successful commercial single-cell genomics platforms.Integrating sequencing and imaging techniques will provide additional information than either of the techniques alone. Both single-cell imaging and genomics techniques measure orthogonal targets, and when combined reveal additional insights into cellular function. However, when performing sequential single-cell assays, there currently exists a tradeoff between throughput and information content. This dissertation will describe progress made towards reducing that gap. I will describe novel microfluidic platforms and techniques and applications involving integrating single-cell sequencing and optical measurements at high throughput. The microfluidics tools that will be discussed in this Dissertation aim to be a platform for performing single-cell multi-parameter and multi-omics techniques that will help further our understanding of cellular identity and how genotype informs phenotype at the single-cell level

Motion Planning for Unlabeled Discs with Optimality Guarantees

We study the problem of path planning for unlabeled (indistinguishable) unit-disc robots in a planar environment cluttered with polygonal obstacles. We introduce an algorithm which minimizes the total path length, i.e., the sum of lengths of the individual paths. Our algorithm is guaranteed to find a solution if one exists, or report that none exists otherwise. It runs in time $\tilde{O}(m^4+m^2n^2)$, where $m$ is the number of robots and $n$ is the total complexity of the workspace. Moreover, the total length of the returned solution is at most $\text{OPT}+4m$, where OPT is the optimal solution cost. To the best of our knowledge this is the first algorithm for the problem that has such guarantees. The algorithm has been implemented in an exact manner and we present experimental results that attest to its efficiency

Silicon-Based Integrated Label-Free Optofluidic Biosensors: Latest Advances and Roadmap

By virtue of the well-developed micro- and nanofabrication technologies and rapidly progressing surface functionalization strategies, silicon-based devices have been widely recognized as a highly promising platform for the next-generation lab-on-a-chip bioanalytical systems with a great potential for point-of-care medical diagnostics. Herein, an overview of the latest advances in silicon-based integrated optofluidic label-free biosensing technologies relying on the efficient interactions between the evanescent light field at the functionalized surface and specifically bound analytes is presented. State-of-the-art technologies demonstrating label-free evanescent wave-based biomarker detection mainly encompass three device configurations, including on-chip waveguide-based interferometers, microring resonators, and photonic-crystal-based cavities. Moreover, up-to-date strategies for elevating the sensitivities and also simplifying the sensing processes are discussed. Emerging laboratory prototypes with advanced integration and packaging schemes incorporating automatic microfluidic components or on-chip optoelectronic devices lead to one significant step forward in real applications of decentralized diagnostics. Besides, particular attention is paid to currently commercialized label-free optical bioanalytical models on the market. Finally, the prospects are elaborated with several research routes toward chip-scale, low-cost, highly sensitive, multi-functional, and user-friendly bioanalytical systems benefiting to global healthcare. © 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Microfluidics: reframing biological enquiry

The underlying physical properties of microfluidic tools have led to new biological insights through the development of microsystems that can manipulate, mimic and measure biology at a resolution that has not been possible with macroscale tools. Microsystems readily handle sub-microlitre volumes, precisely route predictable laminar fluid flows and match both perturbations and measurements to the length scales and timescales of biological systems. The advent of fabrication techniques that do not require highly specialized engineering facilities is fuelling the broad dissemination of microfluidic systems and their adaptation to specific biological questions. We describe how our understanding of molecular and cell biology is being and will continue to be advanced by precision microfluidic approaches and posit that microfluidic tools - in conjunction with advanced imaging, bioinformatics and molecular biology approaches - will transform biology into a precision science

Placement and routing for cross-referencing digital microfluidic biochips.

Xiao, Zigang."October 2010."Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (leaves 62-66).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.viChapter 1 --- Introduction --- p.1Chapter 1.1 --- Microfluidic Technology --- p.2Chapter 1.1.1 --- Continuous Flow Microfluidic System --- p.2Chapter 1.1.2 --- Digital Microfluidic System --- p.2Chapter 1.2 --- Pin-Constrained Biochips --- p.4Chapter 1.2.1 --- Droplet-Trace-Based Array Partitioning Method --- p.5Chapter 1.2.2 --- Broadcast-addressing Method --- p.5Chapter 1.2.3 --- Cross-Referencing Method --- p.6Chapter 1.2.3.1 --- Electrode Interference in Cross-Referencing Biochips --- p.7Chapter 1.3 --- Computer-Aided Design Techniques for Biochip --- p.8Chapter 1.4 --- Placement Problem in Biochips --- p.8Chapter 1.5 --- Droplet Routing Problem in Cross-Referencing Biochips --- p.11Chapter 1.6 --- Our Contributions --- p.14Chapter 1.7 --- Thesis Organization --- p.15Chapter 2 --- Literature Review --- p.16Chapter 2.1 --- Introduction --- p.16Chapter 2.2 --- Previous Works on Placement --- p.17Chapter 2.2.1 --- Basic Simulated Annealing --- p.17Chapter 2.2.2 --- Unified Synthesis Approach --- p.18Chapter 2.2.3 --- Droplet-Routing-Aware Unified Synthesis Approach --- p.19Chapter 2.2.4 --- Simulated Annealing Using T-tree Representation --- p.20Chapter 2.3 --- Previous Works on Routing --- p.21Chapter 2.3.1 --- Direct-Addressing Droplet Routing --- p.22Chapter 2.3.1.1 --- A* Search Method --- p.22Chapter 2.3.1.2 --- Open Shortest Path First Method --- p.23Chapter 2.3.1.3 --- A Two Phase Algorithm --- p.24Chapter 2.3.1.4 --- Network-Flow Based Method --- p.25Chapter 2.3.1.5 --- Bypassibility and Concession Method --- p.26Chapter 2.3.2 --- Cross-Referencing Droplet Routing --- p.28Chapter 2.3.2.1 --- Graph Coloring Method --- p.28Chapter 2.3.2.2 --- Clique Partitioning Method --- p.30Chapter 2.3.2.3 --- Progressive-ILP Method --- p.31Chapter 2.4 --- Conclusion --- p.32Chapter 3 --- CrossRouter for Cross-Referencing Biochip --- p.33Chapter 3.1 --- Introduction --- p.33Chapter 3.2 --- Problem Formulation --- p.34Chapter 3.3 --- Overview of Our Method --- p.35Chapter 3.4 --- Net Order Computation --- p.35Chapter 3.5 --- Propagation Stage --- p.36Chapter 3.5.1 --- Fluidic Constraint Check --- p.38Chapter 3.5.2 --- Electrode Constraint Check --- p.38Chapter 3.5.3 --- Handling 3-pin net --- p.44Chapter 3.5.4 --- Waste Reservoir --- p.45Chapter 3.6 --- Backtracking Stage --- p.45Chapter 3.7 --- Rip-up and Re-route Nets --- p.45Chapter 3.8 --- Experimental Results --- p.46Chapter 3.9 --- Conclusion --- p.47Chapter 4 --- Placement in Cross-Referencing Biochip --- p.49Chapter 4.1 --- Introduction --- p.49Chapter 4.2 --- Problem Formulation --- p.50Chapter 4.3 --- Overview of the method --- p.50Chapter 4.4 --- Dispenser and Reservoir Location Generation --- p.51Chapter 4.5 --- Solving Placement Problem Using ILP --- p.51Chapter 4.5.1 --- Constraints --- p.53Chapter 4.5.1.1 --- Validity of modules --- p.53Chapter 4.5.1.2 --- Non-overlapping and separation of Modules --- p.53Chapter 4.5.1.3 --- Droplet-Routing length constraint --- p.54Chapter 4.5.1.4 --- Optical detector resource constraint --- p.55Chapter 4.5.2 --- Objective --- p.55Chapter 4.5.3 --- Problem Partition --- p.56Chapter 4.6 --- Pin Assignment --- p.56Chapter 4.7 --- Experimental Results --- p.57Chapter 4.8 --- Conclusion --- p.59Chapter 5 --- Conclusion --- p.60Bibliography --- p.6

An Outlook on Design Technologies for Future Integrated Systems

The economic and social demand for ubiquitous and multifaceted electronic systems-in combination with the unprecedented opportunities provided by the integration of various manufacturing technologies-is paving the way to a new class of heterogeneous integrated systems, with increased performance and connectedness and providing us with gateways to the living world. This paper surveys design requirements and solutions for heterogeneous systems and addresses design technologies for realizing them

Single Cell Analysis

Cells are the most fundamental building block of all living organisms. The investigation of any type of disease mechanism and its progression still remains challenging due to cellular heterogeneity characteristics and physiological state of cells in a given population. The bulk measurement of millions of cells together can provide some general information on cells, but it cannot evolve the cellular heterogeneity and molecular dynamics in a certain cell population. Compared to this bulk or the average measurement of a large number of cells together, single-cell analysis can provide detailed information on each cell, which could assist in developing an understanding of the specific biological context of cells, such as tumor progression or issues around stem cells. Single-cell omics can provide valuable information about functional mutation and a copy number of variations of cells. Information from single-cell investigations can help to produce a better understanding of intracellular interactions and environmental responses of cellular organelles, which can be beneficial for therapeutics development and diagnostics purposes. This Special Issue is inviting articles related to single-cell analysis and its advantages, limitations, and future prospects regarding health benefits