Dissertations / Theses on the topic 'Brownian dynamics simulations (BDS)'

The thesis reports a contribution to the development of neural-like network- based element-free methods for the numerical simulation of some non-Newtonian fluid flow problems. The numerical approximation of functions and solution of the governing partial differential equations are mainly based on radial basis function networks. The resultant micro-macroscopic approaches do not require any element-based discretisation and only rely on a set of unstructured collocation points and hence are truly meshless or element-free. The development of the present methods begins with the use of the multi-layer perceptron networks (MLPNs) and radial basis function networks (RBFNs) to effectively eliminate the volume integrals in the integral formulation of fluid flow problems. An adaptive velocity gradient domain decomposition (AVGDD) scheme is incorporated into the computational algorithm. As a result, an improved feed forward neural network boundary-element-only method (FFNN- BEM) is created and verified. The present FFNN-BEM successfully simulates the flow of several Generalised Newtonian Fluids (GNFs), including the Carreau, Power-law and Cross models. To the best of the author's knowledge, the present FFNN-BEM is the first to achieve convergence for difficult flow situations when the power-law indices are very small (as small as 0.2). Although some elements are still used to discretise the governing equations, but only on the boundary of the analysis domain, the experience gained in the development of element-free approximation in the domain provides valuable skills for the progress towards an element-free approach. A least squares collocation RBFN-based mesh-free method is then developed for solving the governing PDEs. This method is coupled with the stochastic simulation technique (SST), forming the mesoscopic approach for analyzing viscoelastic flid flows. The velocity field is computed from the RBFN-based mesh-free method (macroscopic component) and the stress is determined by the SST (microscopic component). Thus the SST removes a limitation in traditional macroscopic approaches since closed form constitutive equations are not necessary in the SST. In this mesh-free method, each of the unknowns in the conservation equations is represented by a linear combination of weighted radial basis functions and hence the unknowns are converted from physical variables (e.g. velocity, stresses, etc) into network weights through the application of the general linear least squares principle and point collocation procedure. Depending on the type of RBFs used, a number of parameters will influence the performance of the method. These parameters include the centres in the case of thin plate spline RBFNs (TPS-RBFNs), and the centres and the widths in the case of multi-quadric RBFNs (MQ-RBFNs). A further improvement of the approach is achieved when the Eulerian SST is formulated via Brownian configuration fields (BCF) in place of the Lagrangian SST. The SST is made more efficient with the inclusion of the control variate variance reduction scheme, which allows for a reduction of the number of dumbbells used to model the fluid. A highly parallelised algorithm, at both macro and micro levels, incorporating a domain decomposition technique, is implemented to handle larger problems. The approach is verified and used to simulate the flow of several model dilute polymeric fluids (the Hookean, FENE and FENE-P models) in simple as well as non-trivial geometries, including shear flows (transient Couette, Poiseuille flows)), elongational flows (4:1 and 10:1 abrupt contraction flows) and lid-driven cavity flows.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2005.
Includes bibliographical references (leaves 105-111).
One of the biggest challenges in non-Newtonian fluid mechanics is calculating the polymer contribution to the stress tensor, which is needed to calculate velocity and pressure fields as well as other quantities of interest. In the case of a Newtonian fluid, the stress tensor is linearly proportional to the velocity gradient and is given by the Newton's law of viscosity, but no such unique constitutive equation exists for non-Newtonian fluids. In order to predict accurately a polymer's rheological properties, it is important to have a good understanding of the molecular configurations in various flow situations. To obtain this information about molecular configurations and orientations, a micromechanical representation of a polymer molecule must be proposed. A micromechanical model may be fine scale, such as the Kramers chain model, which accurately predicts a real polymer's heological properties, but at the same time possesses too many degrees of freedom to be used in complex flow simulations, or it may be a coarse-grained model, such as the Hookean or the FENE dumbbell models, which can be used in complex flow analysis, but have too few degrees of freedom to adequately describe the rheology. The Adaptive Length Scale (ALS) model proposed by Ghosh et al. is only marginally more complicated than the FENE dumbbell model, yet it is able to capture the rapid stress growth in the start-up of uniaxial elongational flow, which is not predicted correctly by the simple dumbbell models. The ALS model is optimized in order to have its simulation time as close as possible to that of the FENE dumbbell.
(cont.) Subsequently, the ALS model is simulated in the start-up of the uniaxial elongational and shear flows as well as in steady extensional and shear flows, and the results are compared to those obtained with other competing rheological models such as the Kramers chain, FENE chain, and FENE dumbbell. While a 5-spring FENE chain predicts results that are in very good agreement with the Kramers chain, the required simulation time clearly makes it impossible to use this model in complex flow simulations. The ALS model agrees better with the Kramers chain than does the FENE dumbbell in the start-up of shear and elongational flows. However, the ALS model takes too long to achieve steady state, which is something that needs to be explored further before the model is used in complex flow calculations. Understanding of this phenomena may explain why the stress-birefringence hysteresis loop predicted by the ALS model is unexpectedly small. In general, if polymer stress is to be calculated using Brownian dynamics simulations, a large number of stochastic trajectories must be simulated in order to predict accurately the macroscopic quantities of interest, which makes the problem computationally expensive. However, recent technological advances as well as a new simulation algorithm called Brownian configuration fields make such problems much more tractable. The operation count in order to assess the feasibility of using the ALS model in complex flow situations yields very promising results if parallel computing is used to calculate polymer contribution to stress. In an attempt to capture polydispersity of real polymer solutions, the use of multi-mode models is explored.
(cont.) The model is fit to the linear viscoelastic spectrum to obtain relaxation times and individual modes' contributions to polymer viscosity. Then, data-fitting to the dimensionless extensional viscosity in the startup of the uniaxial elongational flow is performed for the ALS and the FENE dumbbell models to obtain the molecule's contour length, bmax. It is found that the results from the single-mode and the four-mode ALS models agree much better with the experimental data than do the corresponding single-mode and four-mode FENE dumbbell models. However, all four models resulted in a poor fit to the steady shear data, which may be explained by the fact that the zero-shear-rate viscosity obtained via a fit to the dynamic data by Rothstein and McKinley and used in present simulations, tends to be somewhat lower than the steady-state shear viscosity at very low shear rates, which may have caused a mismatch between the value of ... used in the simulation and the true ... of the polymer solution. As a motivation for using the ALS model in complex flow calculations, the results by Phillips, who simulated the closed-form version of the model in the benchmark 4:1:4 contraction- expansion problem are presented and compared to the experimental results by Rothstein and McKinley [49]. While the experimental observations show that there exists a large extra pres- sure drop, which increases monotonically with increasing De above the value observed for a Newtonian fluid subjected to the same flow conditions, the simulation results with a closed-form version of the FENE dumbbell model, called FENE-CR, exhibit the opposite trend.
(cont.) The ALS-C model, on the other hand, is able to predict the trend correctly. The use of the ALS-C model in another benchmark problem, namely the flow around an array of cylinders confined between two parallel plates, also shows very promising results, which are in much better agreement with experimental data by Liu as compared to the Oldroyd-B model. The simulation results for the ALS-C and the Oldroyd-B models are due to Joo, et al. [28] and Smith et al. [50], respectively. Overall, it is concluded that the ALS model is superior to the commonly used FENE dumb- bell model, although more work is needed to understand why it takes significantly longer than the FENE dumbbell to achieve steady state in uniaxial elongational flows, and why the stress birefringence hysteresis loop predicted by the ALS model is much smaller than that of the other rheological models.
by Irina Burmenko.
S.M.

The technique of Brownian Dynamics simulation has been used to follow the evolution of model colloidal systems during phase separation in the liquid-vapour and solid-vapour regions of the phase diagram. Systems of monodisperse spherical particles interacting via LJ m:n type potentials were quenched in temperature from the one-phase region into the two phase region. Various structural and rheological properties were followed as the systems evolved, including the radial distribution functions, the small angle scattering peak of the structure factor, the interaction energy and the linear response rheology. The scaling behaviour of these quantities was found to be similar to that observed in light scattering experiments following the phase separation of colloidal systems. The aggregate structure could not be represented well by a single fractal dimension. Some evidence of fractal structure was found early in the phase separation, however the reversibility of the interactions allowed for a high degree of restructuring which led to a collapse of the initially tenuous structure into dense aggregates. The local structure was sensitive to the range of the interaction potential - as the potential became more short-ranged, increasing evidence of crystallisation of the denser phase was apparent from the form of g(f). Particles with 12:6 interactions formed structures displaying the rheological strength associated with an elastic gel. However restructuring was continual, resulting in a dense compact structure. The short-range 36:18 potential retained a tenuous gel-like structure and displayed an arrest of phase separation on long lengthscales. However, the particles did not have the interaction strength necessary to give significant rigidity to the system. This suggests that to form an arrested state with elastic gel-like rheology it would be necessary to have a more permanent form of interaction, in addition to the short-range reversible interactions used in this work.

In this thesis I have used computer simulations to study the structure and dynamics of grafted polymers during confinement. These systems are of importance for understanding e.g. colloidal stability and surface coatings. We have used Brownian dynamics simulations with the polymers modeled as discrete wormlike chains allowing for a variable persistence length as well as different non-bonded interactions. The size and shape of the chains are characterized by the radius of gyration and the degree of oblateness/prolateness, and the entanglement is followed by calculating the mean overcrossing number. Starting in the dilute regime with a single polymer mushroom we have investigated how the rate of compression and solvent quality effects the behaviour of a compressed chain. In the brush regime, we investigated how the surface coverage effects the behaviour during compression. For low coverages the chains have the possibilty to increase their lateral extension during confinement but in general, the chains have a low inter-entanglement, as they strive to keep their integrity during the confinement process. To go from a polymer brush to the construction of a connected network, we have developed a method to construct a closed network without using periodic boundary conditions by building the network on a sphere in R4. In this way we avoid the restrictions of periodicity at the cell boundaries. We finally also show how to develop the idea of using spherical boundary conditions, by presenting a novel algorithm for simulating diffusion on a spherical surface. The method is more stable and allows for larger time steps, compared to commonly used methods in computer simulations.

Understanding dynamics of colloidal dispersions is important for several applications ranging from coatings such as paints to growing colloidal crystals for photonic bandgap materials. The research outlined in this dissertation describes the use of Monte Carlo and Stokesian Dynamic simulations to model colloidal dispersions, and the development of theoretical expressions to quantify and predict dynamics of colloidal dispersions. The emphasis is on accurately modeling conservative, Brownian, and hydrodynamic forces to model dynamics of colloidal dispersions. In addition, we develop theoretical expressions for quantifying self-diffusion in colloids interacting via different particle-particle and particle-wall potentials. Specifically, we have used simulations to quantitatively explain the observation of anomalous attraction between like-charged colloids, develop a new criterion for percolation in attractive colloidal fluids, and validate the use of analytical expressions for quantifying diffusion in interfacial colloidal fluids. The results of this work contribute to understanding dynamics in interfacial and bulk colloidal fluids.

The use of computational simulation to study the dynamics and interactions of macromolecules has become an important tool in the field of biochemistry. A common method to perform these simulations is to use all-atom explicit-solvent molecular dynamics (MD). However, due to the limitations in computational power currently available, this method is not practical for simulating large-scale biomolecular systems on long timescales. An alternative is to perform implicit-solvent Brownian dynamics (BD) simulations using a coarse grained (CG) model that allows for increased computational efficiency. However, if simulations using the CG model are not realistic, then the gain in computational efficiency from using a CG model is not worthwhile. This thesis describes the derivation of a set of bonded and nonbonded CG potential functions for use in implicit-solvent BD simulations of proteins derived from all-atom explicit-solvent MD simulations of amino acids. To determine which force field and water model to use in the MD simulations, Chapter II describes 1 Μs all-atom explicit-solvent MD simulations of glycine, asparagine, phenylalanine, and valine solutions at 50, 100, 200 and 300 mg/ml concentrations performed using eight different force field and water model combinations. To evaluate the accuracy of the force fields at high solute concentrations, the density, viscosity, and dielectric increments of the four amino acids were calculated from the simulations and compared to experimental results. Additionally, the change in the strength of hydrophobic and electrostatic interactions with increasing solute concentration was calculated for each force field and water model combination. As a result of this study, the Amber ff99SB-ILDN force field and TIP4P-Ew explicit-solvent water model were chosen for all subsequent MD simulations. Chapter III describes the derivation of CG bonded potential functions from 1 Μs all-atom explicit-solvent MD simulations of each of the twenty amino acids, including a separate simulation for protonated histidine. The angle and dihedral probability distributions sampled during the MD simulations were used to optimize the bonded potential functions using the iterative Boltzmann inversion (IBI) method. Chapter IV describes the derivation of CG nonbonded potential functions from 1 Μs all-atom explicit-solvent MD simulations of every possible pairing of the amino acids (231 different systems). The radial distribution functions calculated from these MD simulations were used to optimize a set of nonbonded CG potential functions using the IBI method. The optimized set of bonded and nonbonded potential functions, which is termed COFFDROP (COarse-grained Force Field for Dynamic Representation Of Proteins), quantitatively reproduced all of the calculated MD distributions. To determine if COFFDROP would be useful for simulations of bimolecular systems, Chapter V describes the testing of the transferability of the force field. First, COFFDROP was used to simulate concentrated amino acid solutions. The clustering of the solutes in these simulations was directly compared with results from corresponding all-atom explicit-solvent MD simulations and found to be in excellent agreement. Next, BD simulations of 9.2 mM solutions of the small protein villin headpiece were performed. The proteins aggregated during these simulations, which is in agreement with results from MD simulation but in disagreement with experiment. After scaling the strength of COFFDROP's nonbonded potential functions by a factor of 0.8 and rerunning the BD simulations, the amount of aggregation was comparable to experimental observations. Based on these results, COFFDROP is likely to be applicable in CG BD simulations of large, highly concentrated, biomolecular systems.

This thesis presents the results of computer simulation studies of the structure, dynamics, and deformation of cross-linked polymer gels. Obtaining a fundamental understanding of the interrelation between the detailed structure and the properties of polymer gels is a challenge and a key issue towards designing materials for specific purposes. A new off-lattice method for constructing a closed network is presented that is free from defects, such as looping chains and dangling ends. Using these model networks in Brownian dynamics simulations, I show results for the structure and dynamics of bulk gels and describe a novel approach using spherical boundary conditions as an alternative to the periodic boundary conditions commonly used in simulations. This algorithm was also applied for simulating the diffusion of tracer particles within a static and dynamic network, to illustrate the quantitative difference and importance of including network mobility for large particles, as dynamic chains facilitate the escape of particles that become entrapped. I further investigate two technologically relevant properties of polymer gels: their stimuli-responsive behaviour and their mechanical properties. The collapse of core-shell nanogels was studied for a range of parameters, including the cross-linking degree and shell thickness. Two distinct regimes of gel collapse could be observed, with a rapid formation of small clusters followed by a coarsening stage. It is shown that in some cases, a collapsing shell may lead to an inversion of the core-shell particle which exposes the core polymer chains to the environment. This thesis also explores the deformation of bimodal gels consisting of both short and long chains, subject to uniaxial elongation, with the aim to understand the role of both network composition as well as structural heterogeneity on the mechanical response and the reinforcement mechanism of these materials. It is shown that a bimodal molecular weight distribution alone is sufficient to strongly alter the mechanical properties of networks compared to the corresponding unimodal networks with the same number-average chain length. Furthermore, it is shown that heterogeneities in the form of high-density short-chain clusters affect the mechanical properties relative to a homogeneous network, primarily by providing extensibility.

This thesis describes water proton and deuterium relaxation processes, as seen by Nuclear Magnetic Resonance (NMR) spectroscopy, using Brownian Dynamics (BD) or Molecular Dynamics (MD) simulations. The MD simulations reveal new detailed information about the dynamics and order of water molecules outside of a lipid bilayer. This is very important information in order to fully understand deuterium NMR measurements in lipid bilayer systems, which require an advanced analysis, because of the complicated water motion (such as tumbling and self-diffusion). The BD simulation methods are combined with the powerful Stochastic Liouville Equation (SLE) in its Langevin form (SLEL) to give new insight into both ¹H₂O and ²H₂O relaxation. The new simulation techniques which combine BD and SLEL can give important new information in cases where other methods do not apply. The deuterium relaxation is described in the context of a water/lipid interface and is in a very elegant way combined with the simulation of diffusion on curved surfaces developed by our research group. ¹H₂O spin-lattice relaxation is described for paramagneticsystems. With this we mean systems with paramagnetic transition metal ions or complexes, that are dissolved into a water solvent. The theoretical description of such systems are quite well investigated but such systems are not yet fully understood. An important consequence of the Paramagnetic Relaxation Enhancement (PRE) calculations when using the SLEL approach combined with BD simulations is that we obtain the electron correlation functions, which describe the relaxation of the paramagnetic electron spins. This means for example that it is also straight forward to generate Electron Spin Resonance (ESR) lineshapes.

Electronic energy transfer has been investigated in pure donor systems by means of computer simulations. Calculated properties were the probability that the initially excited donor is excited at a time t after the excitation, Gs(t), the mean square displacement of the excitation and different fluorescence observables. For three dimensional systems the results obtained by Monte Carlo simulations were compared to the so-called GAF-theory {Gouchanour,C. R., Andersen, H. C. and Fayer, M. D., J. Chem. Phys. 81, 4380 (1984)}, and the agreement was found to be good. Anisotropic systems, i.e. mono-, bi- and multilayer systems, were compared to the two-particle model {Baumann,J. and Fayer, M. D., J. Chem. Phys. 85, 4087 (1986)}. The agreement between the Gs(t) calculated from the tp- model and the Monte Carlo simulations were good for all systems investigated. However, the agreement between the fluorescence observables obtained by MC and the tp-model were in general poor. A much better agreement was found when a phenomenological approach was used for calculating the fluorescence depolarization ratios. Three dimensional systems where the donors are rotating on the same time scale as the energy transfer takes place have also been studied and compared to analytical theories. The Molecular Dynamics simulations of decapeptide H142 shows that simulations in a continuum with a relative permeability do not provide a reliable alternative to simulations with explicit solvent molecules.

Diss. (sammanfattning) Umeå : Umeå universitet, 1991, härtill 5 uppsatser

digitalisering@umu

In order to associate with the cytoplasmic leaflet of the plasma membrane, many cytosolic signalling proteins possess a distinct lipid binding domain as part of their overall fold. Here, a multiscale simulation approach has been used to investigate three membrane-binding proteins involved in cellular processes such as growth and proliferation. The pleckstrin homology (PH) domain from the general receptor for phosphoinositides 1 (GRP1-PH) binds phosphatidylinositol (3,4,5)-trisphosphate (PI(3,4,5)P₃) with high affinity and specificity. To investigate how this peripheral protein is able to locate its target lipid in the complex membrane environment, Brownian dynamics (BD) simulations were employed to explore association pathways for GRP1-PH binding to PI(3,4,5)P₃ embedded in membranes with different surface charge densities and distributions. The results indicated that non-PI(3,4,5)P₃ lipids can act as decoys to disrupt PI(3,4,5)P₃ binding, but that at approximately physiological anionic lipid concentrations steering towards PI(3,4,5)P₃ is actually enhanced. Atomistic molecular dynamics (MD) simulations revealed substantial membrane penetration of membrane-bound GRP1-PH, evident when non-equilibrium, steered MD simulations were used to forcibly dissociate the protein from the membrane surface. Atomistic and coarse grained (CG) MD simulations of the phosphatase and tensin homologue deleted on chromosome ten (PTEN) tumour suppressor, which also binds PI(3,4,5)P₃, detected numerous non-specific protein-lipid contacts and anionic lipid clustering around PTEN that can be modulated by selective in silico mutagenesis. These results suggested a dual recognition model of membrane binding, with non-specific membrane interactions complementing the protein-ligand interaction. Molecular docking and MD simulations were used to characterise the lipid binding properties of kindlin-1 PH. Simulations demonstrated that a dynamic salt bridge was responsible for controlling the accessibility of the binding site. Electrostatics calculations applied to a variety of PH domains suggested that their molecular dipole moments are typically aligned with their ligand binding sites, which has implications for steering and ligand electrostatic funnelling.

In this project, self-assembly behaviour of colloidal particles was investigated by a combination of both computer simulations and experimental approach. In particular, a Brownian Dynamics algorithm was used to simulate either steep-repulsive spheres or spherocylinders in a shrinking spherical confinement. In accordance with literature, in the former case packed spheres were shown to crystallize into a distinctive icosahedral structure. In the latter study, spherocylinders clearly revealed a local tendency to form smectic layers. After the synthesis of micro-sized fluorescent-labelled silica spheres and rods, particle self-assembly in a spherical confinement was experimentally explored. While our selected method widely produced well-defined spherical supraparticles, it generally failed in inducing crystalline or liquid-crystalline ordering. This outcome was supposed to emerge due to fast compression of particles inside the confinement. In the last part of the project, Brownian Dynamics simulations of mixtures of rods and spheres in a spherical confinement were performed. Our preliminary investigation unveiled a modest tendency for rod-rich mixtures to form a binary smectic configuration. However, same-shape phase separation prominently occurred for increasing fractions of spheres. Notably, a quantitative analysis on the simulated configurations was accomplished by introduction of a novel binary smectic local order parameter.

Die Assoziation kleiner Moleküle (Liganden) in hydrophobe Bindungstaschen spielt eine fundamentale Rolle in der Biomolekularerkennung und den Selbstassemblierungsprozessen der physikalischen Chemie wässriger Lösungen. Während der Einfluss des Wassers auf die freie Energie der Bindung (die Bindungsaffinität) im thermischen Gleichgewicht in den letzten Jahren auf immer stärkere Aufmerksamkeit stößt, ist die Rolle des Wassers in der Kinetik und der Bestimmung der Bindungsraten noch weitestgehend unverstanden. Welche nanoskaligen Effekte des Wassers beeinflussen die Dynamik des Liganden in der Nähe der Bindungstasche, und wie lassen sie sich durch die chemischen Eigenschaften der Tasche steuern? Neuste Forschungen haben mithilfe von molekularen Computersimulationen eines einfachen Modells gezeigt, dass Hydrationsfluktuationen in der hydrophoben Bindungstasche an die Dynamik des Liganden koppeln und damit seine Bindungsrate beeinflussen. Da die Wasserfluktuationen wiederum durch die Geometrie und Hydrophobizität der Bindungstasche beeinflusst werden, entsteht die Möglichkeit, kontrollierte Fluktuation zu kreieren, um die Bindungsraten des Liganden zu steuern. In dieser Arbeit wird diese Perspektive mithilfe eines theoretischen Multiskalenansatzes für prototypische Schlüssel-Schloss-Systeme aufgegriffen. Wir untersuchen den Einfluss der physikochemischen Eigenschaften der Bindungstasche auf die Diffusivität und die Bindungsraten des Liganden, und wie die Orientierung eines anisotropen Liganden an die Hydrationsfluktuationen der Tasche koppelt. Damit stellen wir fest, dass kleine Änderungen der Taschentiefe eine extreme Beschleunigung der Bindungsraten bewirken kann und, dass gleichzeitig die Bindung in konkave Taschen vorteilhaft für die Reorientierungsdynamik des Liganden ist. Die Resultate dieses Projekts sollen somit helfen, maßgeschneiderte Lösungen für funktionale „Host-Guest“-Systeme sowie pharmazeutische Moleküle in biomedizinischen Anwendungen zu entwickeln.
The association of small molecules (ligands) to hydrophobic binding pockets plays an integral role in biochemical molecular recognition and function, as well as in various self-assembly processes in the physical chemistry of aqueous solutions. While the investigation of water contributions to the binding free energy (affinity) in equilibrium has attracted a great deal of attention in the last decade, little is known about the role of water in determining the rates of binding and kinetic mechanisms. For instance, what are the nanoscale water effects on ligand diffusion close to the hydrophobic docking site, and how can they be steered by the chemical composition of the pocket? Recent studies used molecular simulations of a simple prototypical pocket-ligand model to show that hydration fluctuations within the binding pocket can couple to the ligand dynamics and influence its binding rates. Since the hydration fluctuations, in turn, can be modified by the pocket’s geometry and hydrophobicity, the possibility exists to create well-controlled solvent fluctuations to steer the ligand’s binding rates. In this work, we pick up this appealing notion employing a theoretical multi-scale approach of a generic key-lock system in aqueous solution. We explore the influence of the physicochemical properties of the pocket on local ligand diffusivities and binding rates and demonstrate how the orientation of a (non-spherical) ligand couples to a pocket’s hydration fluctuations. We find that minor modulation in pocket depth can drastically speed up the binding rate and that, concurrently, binding to molded binding sites is advantageous for the rotational dynamics of the ligand. The results and discussion of this work shall, therefore, imply generic design principles for tailored solutions of functional host-guest systems as well as optimized drugs in biomedical applications.

碩士
國立臺灣大學
化學工程學研究所
99
We use Brownian dynamics-finite element method (BD-FEM) to design microfluidic devices that are capable to efficiently and uniformly stretch DNA for the application of gene mapping. Our design is based on the devices proposed by Kim and Doyle[1] that stretches DNA electrophoretically with the electric field gradient generated in a hyperbolic contraction. To enhance DNA stretching, we propose two strategies that pre-condition DNA before they enter the contraction. For the first approach, we pre-stretch DNA in the direction perpendicular to the funnel axis with a expansion geometry. The partially stretched chains are then turned to align with the axial of the funnel, and experience the second stretching. As a result, DNA chains adapt more extended configurations before going into the funnel, and therefore achieve a higher degree of extension. For the second approach, we pre-condition DNA conformation using an oscillating extensional electric field that has been shown to effectively reducing the population of folded DNA at an ideal condition. However, this approach shows negligible effect in our design, and we find that the original prediction was actually wrong due to the erroneous choice of flow filed. We further examine the efficiency of our design for stretching longer DNA. It is found the performance of the pre-conditioning strategy deteriorates with increasing DNA molecular weight. By analyzing the probability distribution of DNA extension in the device, we propose a new design that utilizes the excluded volume effect of the device boundary to prevent the formation of folded DNA. Our simulation results indeed show that the design with both tricks can provide very uniform, highly stretched DNA even under relatively low field gradient.

碩士
國立中央大學
物理學系
106
The parametrized density functional tight-binding (DFTB) theory is used to calculate the force field of a neutral gold cluster and it is then combined with the Brownian-type molecular dynamics (MD) algorithm [S. K. Lai, W. D. Lin, K. L. Wu, W. H. Li, and K. C. Lee, J. Chem. Phys. 121, 1487 (2004)] to perform simulation studies for this system within the Nosé-Hoover thermostat scheme. We analyze the simulation data for four selected Au clusters which were prepared at T=300 K, and deduce from their thermally evolved behaviors the (a) fluxional character, (b) chiral behavior, and (c) traits of the bi- to tridimensional transition. In all of these MD simulations studies, the initial input position coordinates of ions in these clusters were the lowest energy configurations which we obtained separately from an optimization algorithm [T.W. Yen, T.L. Lim, T.L. Yoon and S.K. Lai, Comput. Phys. Commun. 220, 143 (2017)] where the individual cluster’s energy function employed is within the same DFTB theory. Using these same initial structures, we carried out also independent metadynamics MD (MMD) simulations at 300 K and generated biased trajectories for two of these selected clusters in a collective-variable space instead of the conventional configurational space in terms of the position coordinates of ions. The MMD simulations serve to explore the temporal change of Au clusters in the collective-variable space. It is hoped that an analysis of clusters whose energy functions are calculated in the DFTB theory in the latter and the comparison of simulation results with similar MMD simulations conducted with an empirical potential whose potential parameters are determined from bulk solid-state data would shed light on the subtlety and importance of s-d hybridization which is known to play an important role in both the electronic and structural properties of Au clusters. In this work, we delve into the effects of this covalent-like behavior of these selected clusters, examining them in parallel the features (a)-(c) mentioned above in Brownian-type MD and MMD simulations.