Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

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Numerical Heat Transfer And Fluid Flow

Large-Scale Fabrication of Boron Nitride Nanosheets and Their Utilization in Polymeric Composites with Improved Thermal and Mechanical Properties

Thanks to their remarkable mechanical, electrical, thermal, and barrier properties, graphene-based nanocomposites have been a hot area of research in the past decade. Because of their simple top-down synthesis, graphene oxide (GO) and reduced graphene oxide (rGO) have opened new possibilities for gas barrier, membrane separation, and stimuli-response characteristics in nanocomposites. Herein, we review the synthesis techniques most commonly used to produce these graphene derivatives, discuss how synthesis affects their key material properties, and highlight some examples of nanocomposites with unique and impressive properties. We specifically highlight their performances in separation applications, stimuli-responsive materials, anti-corrosion coatings, and energy storage. Finally, we discuss the outlook and remaining challenges in the field of practical industrial-scale production and use of graphene-derivative-based polymer nanocomposites.

Synthesis, properties, and applications of graphene oxide/reduced graphene oxide and their nanocomposites

Gas barrier performance of graphene/polymer nanocomposites

Due to its exceptionally outstanding electrical, mechanical and thermal properties, graphene is being explored for a wide array of applications and has attracted enormous academic and industrial interest. Graphene and its derivatives have also been considered as promising nanoscale fillers in gas barrier application of polymer nanocomposites (PNCs). In this review, recent research and development of the utilization of graphene and its derivatives in the fabrication of nanocomposites with different polymer matrices for barrier application are explored. Most synthesis methods of graphene-based PNCs are covered, including solution and melt mixing, in situ polymerization and layer-by-layer process. Graphene layers in polymer matrix are able to produce a tortuous path which works as a barrier structure for gases. A high tortuosity leads to higher barrier properties and lower permeability of PNCs. The influence of the intrinsic properties of these fillers (graphene and its derivatives) and their state of dispersion in polymer matrix on the gas barrier properties of graphene/PNCs are discussed. Analytical modeling aspects of barrier performance of graphene/PNCs are also reviewed in detail. We also discuss and address some of the work on mixed matrix membranes for gas separation.

Gas Barrier Performance of Graphene/Polymer Nanocomposites

Multiscale modeling of carbon nanotube epoxy composites

Strain gradient polarization in graphene

This paper deals with the investigation of the effect of carbon nanotube (CNT) waviness on the effective coefficient of thermal expansion (CTE) of a novel continuous fuzzy fiber reinforced composite (FFRC). This novel FFRC is composed of carbon fibers, sinusoidally wavy CNTs and epoxy matrix. The sinusoidally wavy CNTs are radially grown on the circumferential surfaces of the carbon fibers. Analytical micromechanics model based on the method of cells (MOC) approach is derived to investigate the influence of the waviness of CNTs on the effective CTEs of the FFRC. The present study reveals that if the amplitudes of the radially grown sinusoidally wavy CNTs are parallel to the axis of the carbon fiber then the thermoelastic properties of the FFRC are significantly improved over those of the FFRC being composed of straight CNTs.

Shailesh I. Kundalwal

Papers

Gas barrier performance of graphene/polymer nanocomposites

Gas Barrier Performance of Graphene/Polymer Nanocomposites

Multiscale modeling of carbon nanotube epoxy composites

Strain gradient polarization in graphene

Effect of carbon nanotube waviness on the effective thermoelastic properties of a novel continuous fuzzy fiber reinforced composite