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Dionisios G Vlachos

Publications and source records attributed to Dionisios G Vlachos.

15 recordsLinked to original sources

Silica self-assembly and synthesis of microporous and mesoporous silicates.

The microstructure of silica in basic aqueous solutions containing organic cations and prepared from monomeric precursors is reviewed and interpreted within the context of classical ideas of self-assembly of molecular aggregates. The solution properties can be understood by using the pseudo-phase separation approach coupled to the acid-base chemistry of silanol groups and the Poisson-Boltzmann equation. The silica nanoparticles frequently observed in these systems have a core-shell structure with silica in the core and the organic cations at the shell. Individual particles are observed when the forces between particles are repulsive-as is the case for small cations such as tetramethylammonium or tetrapropylammonium-and extended structures such as M41S materials are formed when the forces are attractive--as is the case for surfactants such as cetyltrimethylammonium. These ideas are useful to understand the evolution of zeolite synthesis gels from nucleation to crystal growth. Although at room temperature the silica and the organic cations are segregated, upon heating the organic cations are embedded within the particles. This transformation signals the onset of structure direction whereby the size and geometry of the organic cation induce changes in the structure of silica that may lead to zeolite nuclei.

Journal Article↗

Dynamics of the dissociation of hydrogen on stepped platinum surfaces using the ReaxFF reactive force field.

The dissociation of hydrogen on eight platinum surfaces, Pt(111), Pt(100), Pt(110), Pt(211), Pt(311), Pt(331), Pt(332), and Pt(533), has been studied using molecular dynamics and the reactive force field, ReaxFF. The force field, which includes the degrees of freedom of the atoms in the platinum substrate, was used unmodified with potential parameters determined from previous calculations performed on a training set exclusive of the surfaces considered in this work. The energetics of the eight surfaces in the absence of hydrogen at 0 K were first compared to previous density functional theory (DFT) calculations and found to underestimate excess surface energy. However, taking Pt(111) as a reference state, we found that the trend between surfaces was adequately predicted to justify a relative comparison between the various stepped surfaces. To assess the strengths and weaknesses of the force field, we performed detailed simulations on two stepped surfaces, Pt(533) and Pt(211), and compared our findings to published experimental and theoretical results. In general, the absolute magnitude of reaction rate predictions was low, a result of the force field's tendency to underpredict surface energy. However, when normalized, the simulations show the correct linear scaling with incident energy and angular dependence at collision energies where a direct dissociation mechanism is observed. Because ReaxFF includes all degrees of freedom in the substrate, we carried out simulations aimed at understanding surface-temperature effects on Pt(533). On the basis of the results on Pt(533)/Pt(211), we studied the reaction of hydrogen at normal incidence on all eight surfaces in a range of energies where we anticipated the force field to give reasonable qualitative trends. These results were subsequently fit to a simple linear model that predicts the enhanced reactivity of surfaces containing 111-type atomic steps versus 100-type atomic steps. This model provides a simple framework for predicting high-energy/high-temperature kinetics of complex surfaces not vicinal to Pt(111).

Journal Article↗

Spatial modeling of dimerization reaction dynamics in the plasma membrane: Monte Carlo vs. continuum differential equations.

Bimolecular reactions in the plasma membrane, such as receptor dimerization, are a key signaling step for many signaling systems. For receptors to dimerize, they must first diffuse until a collision happens, upon which a dimerization reaction may occur. Therefore, study of the dynamics of cell signaling on the membrane may require the use of a spatial modeling framework. Despite the availability of spatial simulation methods, e.g., stochastic spatial Monte Carlo (MC) simulation and partial differential equation (PDE) based approaches, many biological models invoke well-mixed assumptions without completely evaluating the importance of spatial organization. Whether one is to utilize a spatial or non-spatial simulation framework is therefore an important decision. In order to evaluate the importance of spatial effects a priori, i.e., without performing simulations, we have assessed the applicability of a dimensionless number, known as second Damköhler number (Da), defined here as the ratio of time scales of collision and reaction, for 2-dimensional bimolecular reactions. Our study shows that dimerization reactions in the plasma membrane with Da approximately >0.1 (tested in the receptor density range of 10(2)-10(5)/microm(2)) require spatial modeling. We also evaluated the effective reaction rate constants of MC and simple deterministic PDEs. Our simulations show that the effective reaction rate constant decreases with time due to time dependent changes in the spatial distribution of receptors. As a result, the effective reaction rate constant of simple PDEs can differ from that of MC by up to two orders of magnitude. Furthermore, we show that the fluctuations in the number of copies of signaling proteins (noise) may also depend on the diffusion properties of the system. Finally, we used the spatial MC model to explore the effect of plasma membrane heterogeneities, such as receptor localization and reduced diffusivity, on the dimerization rate. Interestingly, our simulations show that localization of epidermal growth factor receptor (EGFR) can cause the diffusion limited dimerization rate to be up to two orders of magnitude higher at higher average receptor densities reported for cancer cells, as compared to a normal cell.

Algorithms↗

Silica nanoparticle formation in the TPAOH-TEOS-H2O system: a population balance model.

A model describing the kinetics of silica nanoparticle formation in the TPAOH-TEOS-H(2)O system is presented. These nanoparticles are an important intermediate in the clear-solution synthesis of silicalite-1, so understanding the mechanisms by which they are formed and stabilized is a key step in determining the crystallization behavior of pure-silica zeolites. The model presented here is based on the mass-conserving form of the Becker-Döring population balance equations, describing growth and fragmentation by addition or removal of monomeric units, and modified to account for rapid equilibration of small silicate species and electrostatic and/or template stabilization of nanoparticles. The model predictions compare favorably with the experimental results. It is found that nanoparticle evolution exhibits distinct time regimes consisting of TEOS hydrolysis, condensation, nanoparticle formation, Ostwald ripening, and a self-sharpening mechanism in particle size distribution toward equilibrium due to stabilization during which no apparent changes in average particle size and pH are observed. Finally, the model provides an alternative, to a recent hypothesis, kinetics point of view to explain the enhanced stability of nanoparticles over extended periods of time.

Journal Article↗

Multiscale spatial Monte Carlo simulations: multigriding, computational singular perturbation, and hierarchical stochastic closures.

Monte Carlo (MC) simulation of most spatially distributed systems is plagued by several problems, namely, execution of one process at a time, large separation of time scales of various processes, and large length scales. Recently, a coarse-grained Monte Carlo (CGMC) method was introduced that can capture large length scales at reasonable computational times. An inherent assumption in this CGMC method revolves around a mean-field closure invoked in each coarse cell that is inaccurate for short-ranged interactions. Two new approaches are explored to improve upon this closure. The first employs the local quasichemical approximation, which is applicable to first nearest-neighbor interactions. The second, termed multiscale CGMC method, employs singular perturbation ideas on multiple grids to capture the entire cluster probability distribution function via short microscopic MC simulations on small, fine-grid lattices by taking advantage of the time scale separation of multiple processes. Computational strategies for coupling the fast process at small length scales (fine grid) with the slow processes at large length scales (coarse grid) are discussed. Finally, the binomial tau-leap method is combined with the multiscale CGMC method to execute multiple processes over the entire lattice and provide additional computational acceleration. Numerical simulations demonstrate that in the presence of fast diffusion and slow adsorption and desorption processes the two new approaches provide more accurate solutions in comparison to the previously introduced CGMC method.

Algorithms↗

Computational modeling reveals molecular details of epidermal growth factor binding.

BACKGROUND: The ErbB family of receptors are dysregulated in a number of cancers, and the signaling pathway of this receptor family is a critical target for several anti-cancer drugs. Therefore a detailed understanding of the mechanisms of receptor activation is critical. However, despite a plethora of biochemical studies and recent single particle tracking experiments, the early molecular mechanisms involving epidermal growth factor (EGF) binding and EGF receptor (EGFR) dimerization are not as well understood. Herein, we describe a spatially distributed Monte Carlo based simulation framework to enable the simulation of in vivo receptor diffusion and dimerization. RESULTS: Our simulation results are in agreement with the data from single particle tracking and biochemical experiments on EGFR. Furthermore, the simulations reveal that the sequence of receptor-receptor and ligand-receptor reaction events depends on the ligand concentration, receptor density and receptor mobility. CONCLUSION: Our computer simulations reveal the mechanism of EGF binding on EGFR. Overall, we show that spatial simulation of receptor dynamics can be used to gain a mechanistic understanding of receptor activation which may in turn enable improved cancer treatments in the future.

Computer Simulation↗

Physical basis for the formation and stability of silica nanoparticles in basic solutions of monovalent cations.

The colloidal stability, phase behavior, and solubility of silica nanoparticles (3-10 nm) that are formed in basic solutions of monovalent cations (primarily tetrapropylammonium) are investigated using a combination of chemical equilibria and electrostatic models. The free-energy gain associated with the formation of an electric double layer surrounding the nanoparticle was obtained by solving the Poisson-Boltzmann equation. This free energy is an important contribution to the total free energy of the particle and is second only to the formation of Si-O-Si bonds. The free energy of formation of the nanoparticles becomes increasingly negative with an increase in particle size and density, which explains the lower solubility of nanoparticles compared to that of amorphous silica. There is a minimum in the free energy of condensation as a function of size that qualitatively explains why the formation of small particles with a uniform size (<5 nm) is energetically favorable. The electrostatic models provide an estimate for the nanoparticle surface potential, which is significantly higher (-120 to -170 mV) than that of zeolite silicalite-1 (-60 to -80 mV) prepared in similar solutions. This result explains the stability of such small particles in solution. It is also shown that a condensation model that is based on silica solubility can describe the phase diagram for nanoparticle formation reported by Fedeyko et al. (J. Phys. Chem. B 2004, 108, 12271) over a wide range of pH and, in conjunction with a complexation model, provides an approximate equilibrium constant (pKa = 8.4) for the dissociation of nanoparticle silanol groups.

Journal Article↗

Evolution of self-assembled silica-tetrapropylammonium nanoparticles at elevated temperatures.

The time evolution of silica nanoparticles in solutions of tetrapropylammonium (TPA) has been studied using a combination of small-angle scattering, conductivity, and pH measurements to provide the first comprehensive analysis of nanoparticle structural and compositional changes at elevated temperatures. We have found that silica-TPA nanoparticles subjected to hydrothermal treatment (70-90 degrees C) grow via an Ostwald ripening mechanism with growth rates that depend on both pH and temperature. Small-angle X-ray (SAXS) and neutron (SANS) scattering confirm that the core-shell structure of the particles, initially present at room temperature, is maintained during heating, but an evolution toward sphericity is evidenced especially at high values of pH. SAXS absolute intensity calculations were utilized to calculate the changes in nanoparticle composition and concentration over time. These changes along with the conductivity and pH measurements and SANS contrast matching studies indicate that, upon heating, TPA becomes embedded in the core of nanoparticles giving rise to more zeolitic-looking nanomaterials.

Journal Article↗

Heterogeneities in EGF receptor density at the cell surface can lead to concave up scatchard plot of EGF binding.

The mechanism responsible for the concave up nature of the Scatchard plot of epidermal growth factor (EGF) binding on EGF receptor (EGFR) has been a controversial issue for more than a decade. Past efforts to mechanistically simulate the concave up nature of the Scatchard plot of EGF binding have shown that negative cooperativity in EGF binding on an EGFR dimer or inclusion of some external site or binding event can describe this behavior. However, herein we show that heterogeneity in the density of EGFR due to localization in certain regions of the plasma membrane, which has been experimentally reported, can also lead to concave up shape of the Scatchard plot of the EGF binding on EGFR.

Cell Line, Tumor↗

Formation and structure of self-assembled silica nanoparticles in basic solutions of organic and inorganic cations.

The phase behavior of silica solutions containing organic and inorganic cations was studied at room temperature using conductivity, pH, and small-angle scattering experiments. A critical aggregation concentration (cac) was observed at approximately 1:1 ratio of SiO(2)/OH(-) for all cation solutions from conductivity and pH studies. From this cac, a phase diagram of the system was developed with three distinct phase regions in pseudoequilibrium: a monomer/oligomer region (I), a monomer/oligomer/nanoparticle region (II), and a gel region (III). Small-angle X-ray and neutron scattering (SAXS and SANS) on solutions of region II formed with tetrapropylammonium hydroxide (TPAOH) revealed that the nanoparticles have a core-shell structure. Structure analysis of the SAXS and SANS data was best fit by a core-shell oblate ellipsoid model. A polydisperse set of core-shell spheres also fit the data well although with lower agreement factors. Similar nanoparticle morphologies were found in solutions of TMAOH, CsOH, and NaOH.

Journal Article↗

Time accelerated Monte Carlo simulations of biological networks using the binomial tau-leap method.

UNLABELLED: Developing a quantitative understanding of intracellular networks requires simulations and computational analyses. However, traditional differential equation modeling tools are often inadequate due to the stochasticity of intracellular reaction networks that can potentially influence the phenotypic characteristics. Unfortunately, stochastic simulations are computationally too intense for most biological systems. Herein, we have utilized the recently developed binomial tau-leap method to carry out stochastic simulations of the epidermal growth factor receptor induced mitogen activated protein kinase cascade. Results indicate that the binomial tau-leap method is computationally 100-1000 times more efficient than the exact stochastic simulation algorithm of Gillespie. Furthermore, the binomial tau-leap method avoids negative populations and accurately captures the species populations along with their fluctuations despite the large difference in their size. AVAILABILITY: http://www.dion.che.udel.edu/multiscale/Introduction.html. Fortran 90 code available for academic use by email. SUPPLEMENTARY INFORMATION: Details about the binomial tau-leap algorithm, software and a manual are available at the above website.

Algorithms↗

Binomial distribution based tau-leap accelerated stochastic simulation.

Recently, Gillespie introduced the tau-leap approximate, accelerated stochastic Monte Carlo method for well-mixed reacting systems [J. Chem. Phys. 115, 1716 (2001)]. In each time increment of that method, one executes a number of reaction events, selected randomly from a Poisson distribution, to enable simulation of long times. Here we introduce a binomial distribution tau-leap algorithm (abbreviated as BD-tau method). This method combines the bounded nature of the binomial distribution variable with the limiting reactant and constrained firing concepts to avoid negative populations encountered in the original tau-leap method of Gillespie for large time increments, and thus conserve mass. Simulations using prototype reaction networks show that the BD-tau method is more accurate than the original method for comparable coarse-graining in time.

Journal Article↗

Spatially adaptive lattice coarse-grained Monte Carlo simulations for diffusion of interacting molecules.

While lattice kinetic Monte Carlo (KMC) methods provide insight into numerous complex physical systems governed by interatomic interactions, they are limited to relatively short length and time scales. Recently introduced coarse-grained Monte Carlo (CGMC) simulations can reach much larger length and time scales at considerably lower computational cost. In this paper we extend the CGMC methods to spatially adaptive meshes for the case of surface diffusion (canonical ensemble). We introduce a systematic methodology to derive the transition probabilities for the coarse-grained diffusion process that ensure the correct dynamics and noise, give the correct continuum mesoscopic equations, and satisfy detailed balance. Substantial savings in CPU time are demonstrated compared to microscopic KMC while retaining high accuracy.

Journal Article↗

Microstructural optimization of a zeolite membrane for organic vapor separation.

A seeded growth method for the fabrication of high-permeance, high-separation-factor zeolite (siliceous ZSM-5, [Si96O192]-MFI) membranes is reported. The method consists of growing the crystals of an oriented seed layer to a well-intergrown film by avoiding events that lead to a loss of preferred orientation, such as twin overgrowths and random nucleation. Organic polycations are used as zeolite crystal shape modifiers to enhance relative growth rates along the desirable out-of-plane direction. The polycrystalline films are thin (approximately 1 micrometer) with single grains extending along the film thickness and with large in-plane grain size (approximately 1 micrometer). The preferred orientation is such that straight channels with an open diameter of approximately 5.5 angstroms run down the membrane thickness. Comparison with previously reported membranes shows that these microstructurally optimized films have superior performance for the separation of organic mixtures with components that have small differences in size and shape, such as xylene isomers.

Journal Article↗

Coarse-grained stochastic processes for microscopic lattice systems.

Diverse scientific disciplines ranging from materials science to catalysis to biomolecular dynamics to climate modeling involve nonlinear interactions across a large range of physically significant length scales. Here a class of coarse-grained stochastic processes and corresponding Monte Carlo simulation methods, describing computationally feasible mesoscopic length scales, are derived directly from microscopic lattice systems. It is demonstrated below that the coarse-grained stochastic models can capture large-scale structures while retaining significant microscopic information. The requirement of detailed balance is used as a systematic design principle to guarantee correct noise fluctuations for the coarse-grained model. The coarse-grained stochastic algorithms provide large computational savings without increasing programming complexity or computer time per executive event compared to microscopic Monte Carlo simulations.

Algorithms↗