Get Multiscale Modeling of Particle Interactions: Applications PDF

By Michael R. King, David J. Gee

Discover how the most recent computational instruments are construction our knowing of particle interactions and resulting in new applications

With this e-book as their advisor, readers will achieve a brand new appreciation of the serious function that particle interactions play in advancing study and constructing new functions within the organic sciences, chemical engineering, toxicology, medication, and production expertise The booklet explores debris ranging in dimension from cations to entire cells to tissues and processed fabrics. a spotlight on recreating advanced, real-world dynamical structures is helping readers achieve a deeper realizing of mobilephone and tissue mechanics, theoretical features of multiscale modeling, and the newest purposes in biology and nanotechnology.

Following an introductory bankruptcy, Multiscale Modeling of Particle Interactions is split into components:

Part I, purposes in Nanotechnology, covers:

  • Multiscale modeling of nanoscale aggregation phenomena: functions in semiconductor fabrics processing

  • Multiscale modeling of infrequent occasions in self-assembled systems

  • Continuum description of atomic sheets

  • Coulombic dragging and mechanical propelling of molecules in nanofluidic systems

  • Molecular dynamics modeling of nanodroplets and nanoparticles

  • Modeling the interactions among compliant microcapsules and patterned surfaces

Part II, purposes in Biology, covers:

  • Coarse-grained and multiscale simulations of lipid bilayers

  • Stochastic method of biochemical kinetics

  • In silico modeling of angiogenesis at a number of scales

  • Large-scale simulation of blood circulation in microvessels

  • Molecular to multicellular deformation in the course of adhesion of immune cells lower than circulation

Each article used to be contributed through a number of major specialists and pioneers within the box. All readers,from chemists and biologists to engineers and scholars, will achieve new insights into how the latesttools in computational technology can increase our realizing of particle interactions and aid the advance of novel purposes around the vast spectrum of disciplines in biology and nanotechnology.

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Extra info for Multiscale Modeling of Particle Interactions: Applications in Biology and Nanotechnology

Sample text

Extended, highly disordered configurations can assume numerous, almost degenerate relaxations, each of which corresponds to a unique local minimum in the potential-energy landscape. In other words, as the MECHANISTICALLY VALIDATED LKMC MODELING OF ATOMIC AGGREGATION 23 number of lattice atoms that are included into the defect structure increases, so does the number of small reconstructions that produce nearlydegenerate local minima. As a result, we find that the density of states for a given cluster increases exponentially with formation energy.

In the following discussion, each coarse cell contains q fine-grid (atomic) sites and is completely characterized by an occupancy number, 0 h q (Fig. 10). A key step in formulating a CGLKMC simulation is the closure rule, which dictates how the single-atom events are averaged within a coarse-grained cell to produce a set of coarse events. 10 Schematic representations of (a) fine-grid (“microscopic”) lattice, and (b) corresponding coarse-grained (“mesoscopic”) lattice (q ¼ 9). ) The major assumptions required for constructing a CGLKMC approximation have been addressed extensively by Vlachos and co-workers, and are addressed here only briefly; see refs.

A 6NN fine-grid interaction potential with constant interaction strength, bJ ¼ 1:5, is applied. This potential is sufficient to lead to strong aggregation of the particles and provides a stringent test of the CGLKMC framework presented in this section. During each simulation, the cluster size distribution is monitored periodically. , the range of the potential). Two moments of the cluster size distribution are used to monitor system evolution. These are the average cluster size, cluster number, M0, where all moments of the size distribution M2/M1, and the total P are defined as Mn ¼ s sn Xs , where Xs is the number of clusters of size s and n is the moment order.

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