Development and ssage of micro- and nanofluidic devices for nanoparticle trapping, sorting and biosensing

Microfluidics has revolutionized life sciences by introducing the tools to perform complex scientific studies in a simpler yet robust and reliable way. Miniaturization of bench-top processing tools using micro- and nanofluidic devices enables handling biological samples in a physiologically relevant environment to execute complex studies that were not possible before. Organ on a chip, lab on a chip, point-of-care diagnosis, biosensing, miniaturized PCR tools, etc., are some of the previously inconceivable examples in a portable device form. Due to the scale of the device dimensions in such microfluidic devices, small volume handling and processing have become noticeably effortless. Among various applications of micro- and nanofluidic devices, molecular sensing, nanoparticle separation, sorting, trapping, and processing are of significant impact due to their feasibility of implementation in most of the fluidic devices. Single-particle trapping is an effective approach to study the fundamental properties of molecules in their physiological environment. Various active and passive methods exist to execute single-particle studies, such as optical tweezers, magnetic tweezers, dielectrophoretic trapping, hydrodynamic trapping, geometrical trapping, and electrostatic trapping. In the case of active methods, such as optical and magnetic tweezers, precise control of molecular motion is possible at the cost of a complex setup with external force sources. However, high-throughput single-particle trapping and manipulation are not feasible in a way that can be achieved using passive methods such as geometry induced electrostatic (GIE) trapping and geometrical trapping. This thesis focuses on developing integrated micro and nanofluidic devices for 1) high throughput contact free electrostatic trapping of single nanoparticles and 2) size based nanoparticle separation, sorting, and trapping for biosensing applications. The high-throughput single-particle trapping was achieved by developing fluidic devices utilizing the GIE trapping. A GIE trapping fluidic device comprises nanochannels embedded with nanostructures, such as slits, cylinders, and grids. These nanostructures enable the formation of electrostatic potential traps inside the nanoindentations, forcing negatively charged nano objects to attain a position inside them to minimize their self-energy. In conventional GIE trapping devices, negatively charged molecules, such as DNA, viruses, and gold nanoparticles (Au NPs), can be easily trapped in the electrostatic traps. This thesis presents the development and fabrication of GIE trapping devices using 1) glass substrate and 2) polydimethylsiloxane (PDMS) polymer. These substrates attain a net negative surface charge density in an aqueous solution (pH > 2) due to the self-dissociation of terminal silanol groups. Therefore, glass and PDMS based fluidic devices are only usable for the confinement of negatively charged nano objects. In this work, the scope of these fluidic devices was extended to the trapping of positively charged nano objects by using surface modification methods for both glass and PDMS based fluidic devices. The surface modification of glass‑based nanofluidic devices was achieved by modifying the inside of the GIE-trapping device by the adsorption of a single layer of polyelectrolyte (poly(ethyleneimine), PEI). The PEI layer modifies the negatively charged glass surface to a positively charged surface and allows for the trapping of positively charged nanoparticles. However, the surface modifying procedure for the glass based GIE trapping device was demanding and required 4 to 5 days. To have an efficient surface modification process, PDMS based GIE trapping devices were introduced. The introduction of PDMS based fluidic devices for positively charged nano objects has improved the throughput for device fabrication and surface modification. Furthermore, two polyelectrolyte layers (1: poly(ethyleneimine) and 2: poly(styrenesulfonate)) deposition is presented in this work using PDMS based devices to demonstrate the possibility of achieving homogeneously charged surface using multi-polyelectrolyte layers. The efficiency of these devices with surface charge reversal was comparable to native GIE trapping devices, demonstrating the successful and homogeneous surface modification. The trapping efficiency and device performance of a GIE Trapping device rely on the geometry of the device and the interaction between the charged particle and the device surface. Therefore, extensive optimization of the device geometry is essential to achieve efficient GIE trapping in a fluidic device. In this work, two different approaches, 1) charged particle inclusive simulation and 2) point charge approximation simulation, are presented to optimize the geometrical parameters of a GIE trapping device numerically. To compare numerical results with experimental data, a cylindrical nanopocket design was used to represent a nanotrap to confine a charged gold nanoparticle. The charged-particle inclusive simulations are demanding, but provide more accurate results for attainable particle stiffness constant using crucial geometrical parameters of the device, size and charge of the particle of interest, and the salt concentration of the solution. Comparatively, point-charge approximation simulations are faster and give appropriate results of particle trapping stiffness constant, residence time, etc. Here, point-charge approximation simulations are used for efficiently identifying the trends of trapping strength of a device based on critical geometrical parameters, i.e., the height of the nanochannel and the nanopocket and the diameter of the nanopocket. The point charge approximation simulations demonstrated that the trapping strength of a particle inside a nanotrap could be enhanced by increasing the trap height or reducing the channel height. Additionally, the trapping strength of a nanotrap can be modified by changing the diameter of the nanopocket; however, reduction or enlargement of the pocket diameter from the optimum diameter reduces the trapping strength of the nanotrap. For effective GIE trapping, it is important to use a solution with low ionic or salt concentration ( 10-4 pN/nm) in order to avoid screening of the electrostatic field from the charged device surface. A detailed comparison of both approaches, numerical calculations, and experimental results are presented, demonstrating their advantages and disadvantages. While there are many advantages of GIE trapping devices for molecular trapping, one major disadvantage is the reduced functionality of the devices for body fluids that contain high salt concentrations. Due to the high ionic concentration in the body fluids, the electrostatic effect of the charged device surface gets screened, leading to no potential trap for the confinement of charged nano objects. Therefore, a new design of the fluidic device is developed for biosensing applications that can use body fluids to extract the target molecules for molecular sensing. The fluidic device exploited geometrical sieving, deterministic lateral displacement (DLD) arrays, and geometrical trapping for particle separation, sorting, and trapping, respectively. The separation of unwanted macro- and micro-particles was achieved in the separation chamber, followed by the size sorting of target molecule adsorbed nanoparticles and, later, the size based trapping of these nanoparticles in the detection area. The motion of the solution and nanoparticle throughout the device was observed using interferometric scattering detection (iSCAT) microscopy, whereas, for molecular sensing, Raman spectroscopy was used at the detection area to achieve a few pg/ml detection limit. The device has the potential for applications in early multi disease diagnosis for diseases that can be detected using antigen-antibody complex formation on antibody-coated nanoparticles. The presented GIE trapping devices can be used to achieve high-throughput single-nanoparticle trapping, whereas geometrical particle trapping devices can be used to perform size-selective nanoparticle trapping for molecular sensing. Both methods are effective for studies conducted in an aqueous environment and have the potential to be used in molecular studies, disease diagnosis, biological studies, etc., for research and commercial purposes. Demonstrated device fabrication methods and surface modification procedures allow improved productivity and yield of the GIE trapping devices. The device geometry of a GIE trapping device can be optimized further using the presented numerical calculations. Therefore, the work presented here advances the research in the field of GIE trapping and geometrical trapping and opens up new possibilities for both basic and applied research in several fields, such as biophysics, molecular dynamics, diagnostics, and molecular detection

Meshless Method for Simulation of Compressible Flow

In the present age, rapid development in computing technology and high speed supercomputers has made numerical analysis and computational simulation more practical than ever before for large and complex cases. Numerical simulations have also become an essential means for analyzing the engineering problems and the cases that experimental analysis is not practical. There are so many sophisticated and accurate numerical schemes, which do these simulations. The finite difference method (FDM) has been used to solve differential equation systems for decades. Additional numerical methods based on finite volume and finite element techniques are widely used in solving problems with complex geometry. All of these methods are mesh-based techniques. Mesh generation is an essential preprocessing part to discretize the computation domain for these conventional methods. However, when dealing with mesh-based complex geometries these conventional mesh-based techniques can become troublesome, difficult to implement, and prone to inaccuracies. In this study, a more robust, yet simple numerical approach is used to simulate problems in an easier manner for even complex problem. The meshless, or meshfree, method is one such development that is becoming the focus of much research in the recent years. The biggest advantage of meshfree methods is to circumvent mesh generation. Many algorithms have now been developed to help make this method more popular and understandable for everyone. These algorithms have been employed over a wide range of problems in computational analysis with various levels of success. Since there is no connectivity between the nodes in this method, the challenge was considerable. The most fundamental issue is lack of conservation, which can be a source of unpredictable errors in the solution process. This problem is particularly evident in the presence of steep gradient regions and discontinuities, such as shocks that frequently occur in high speed compressible flow problems. To solve this discontinuity problem, this research study deals with the implementation of a conservative meshless method and its applications in computational fluid dynamics (CFD). One of the most common types of collocating meshless method the RBF-DQ, is used to approximate the spatial derivatives. The issue with meshless methods when dealing with highly convective cases is that they cannot distinguish the influence of fluid flow from upstream or downstream and some methodology is needed to make the scheme stable. Therefore, an upwinding scheme similar to one used in the finite volume method is added to capture steep gradient or shocks. This scheme creates a flexible algorithm within which a wide range of numerical flux schemes, such as those commonly used in the finite volume method, can be employed. In addition, a blended RBF is used to decrease the dissipation ensuing from the use of a low shape parameter. All of these steps are formulated for the Euler equation and a series of test problems used to confirm convergence of the algorithm. The present scheme was first employed on several incompressible benchmarks to validate the framework. The application of this algorithm is illustrated by solving a set of incompressible Navier-Stokes problems. Results from the compressible problem are compared with the exact solution for the flow over a ramp and compared with solutions of finite volume discretization and the discontinuous Galerkin method, both requiring a mesh. The applicability of the algorithm and its robustness are shown to be applied to complex problems

Large-Scale Numerical Modeling of Melt and Solution Crystal Growth

We present an overview of mathematical models and their large-scale numerical solution for simulating different phenomena and scales in melt and solution crystal growth. Samples of both classical analyses and state-of-the-art computations are presented. It is argued that the fundamental multi-scale nature of crystal growth precludes any one approach for modeling, rather successful crystal growth modeling relies on an artful blend of rigor and practicality

Integrating discrete stochastic models with single-cell and single-molecule experiments

2019 Summer.Includes bibliographical references.Modern biological experiments can capture the behaviors of single biomolecules within single cells. Much like Robert Brown looking at pollen grains in water, experimentalists have noticed that individual cells that are genetically identical behave seemingly randomly in the way they carry out their most basic functions. The field of stochastic single-cell biology has been focused developing mathematical and computational tools to understand how cells try to buffer or even make use of such fluctuations, and the technologies to measure such fluctuations has vastly improved in recent years. This dissertation is focused on developing new methods to analyze modern single-cell and single-molecule biological data with discrete stochastic models of the underlying processes, such as stochastic gene expression and single-mRNA translation. The methods developed here emphasize a strong link between model and experiment to help understand, design, and eventually control biological systems at the single-cell level

Analytical results on the polymerisation random graph model

The step-growth polymerisation of a mixture of arbitrary-functional monomers is viewed as a time-continuos random graph process with degree bounds that are not necessarily the same for different vertices. The sequence of degree bounds acts as the only input parameter of the model. This parameter entirely defines the timing of the phase transition. Moreover, the size distribution of connected components features a rich temporal dynamics that includes: switching between exponential and algebraic asymptotes and acquiring oscillations. The results regarding the phase transition and the expected size of a connected component are obtained in a closed form. An exact expression for the size distribution is resolved up to the convolution power and is computable in subquadratic time. The theoretical results are illustrated on a few special cases, including a comparison with Monte Carlo simulations.Comment: 19 pages, 7 figure

Design, Analysis, And Computational Methods For Engineering Synthetic Biological Networks

This thesis advances our understanding of three important aspects of biological systems engineering: analysis, design, and computational methods. First, biological circuit design is necessary to engineer biological systems that behave consistently and follow our design specifications. We contribute by formulating and solving novel problems in stochastic biological circuit design. Second, computational methods for solving biological systems are often limited by the nonlinearity and high dimensionality of the system’s dynamics. This problem is particularly extreme for the parameter identification of stochastic, nonlinear systems. Thus, we develop a method for parameter identification that relies on data-driven stochastic model reduction. Finally, biological system analysis encompasses understanding the stability, performance, and robustness of these systems, which is critical for their implementation. We analyze a sequestration feedback motif for implementing biological control. First, we discuss biological circuit design for the stationary and the transient distributional responses of stochastic biochemical systems. Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture their dynamics. The chemical master equation is a commonly used stochastic model that describes how the probability distribution of a chemically reacting system varies with time. Here we design the distributional response of these stochastic models by formulating and solving it as a constrained optimization problem. Second, we analyze the stability and the performance of a biological controller implemented by a sequestration feedback network motif. Sequestration feedback networks have been implemented in synthetic biology using an array of biological parts. However, their properties of stability and performance are poorly understood. We provide insight into the stability and performance of sequestration feedback networks. Additionally, we provide guidelines for the implementation of sequestration feedback networks. Third, we develop computational methods for the parameter identification of stochastic models of biochemical reaction networks. It is often not possible to find analytic solutions to problems where the dynamics of the underlying biological circuit are stochastic, nonlinear or both. Stochastic models are often challenging due to their high dimensionality and their nonlinearity, which further limits the availability of analytical tools. To address these challenges, we develop a computational method for data-driven stochastic model reduction and we use it to perform parameter identification. Last, we provide concluding remarks and future research directions.</p

Suspension flow modeling

Diplomová práce se zabývá prouděním v hydrocyklónu. Hydrocyklón je separační stroj, kterým slouží k oddělování pevných částice z kapaliny (nejčastěji z vody). Díky tangenciálnímu vstupu do zařízení nastává silné vířivé proudění, které způsobuje přisávání vzduchu přes horní a dolní výtok. Toto vzduchové jádro hraje důležitou roli v separaci částic. Výpočty jsou provedeny pomoci CFD.This master thesis deals with the investigation of flow in a hydrocyclone, which is a separator device for separating solid phase from a fluid (mainly water). Due to the tangential inlet to the device high swirling flow is gained, which results the suction of air through the over and underflow, where the air core plays an important role in separating particles. The investigation is carried out with the aid of CFD.