Mohammad Naraghi

Bio: Mohammad Naraghi is an academic researcher from Texas A&M University. The author has contributed to research in topics: Nanofiber & Carbon nanofiber. The author has an hindex of 27, co-authored 90 publications receiving 2518 citations. Previous affiliations of Mohammad Naraghi include University of Illinois at Urbana–Champaign & Texas A&M University System.

Optimal length scales emerging from shear load transfer in natural materials: application to carbon-based nanocomposite design.

Abstract: Numerous theoretical and experimental studies on various species of natural composites, such as nacre in abalone shells, collagen fibrils in tendon, and spider silk fibers, have been pursued to provide insight into the synthesis of novel bioinspired high-performance composites. However, a direct link between the mechanical properties of the constituents and the various geometric features and hierarchies remains to be fully established. In this paper, we explore a common denominator leading to the outstanding balance between strength and toughness in natural composite materials. We present an analytical model to link the mechanical properties of constituents, their geometric arrangement, and the chemistries used in their lateral interactions. Key critical overlap length scales between adjacent reinforcement constituents, which directly control strength and toughness of composite materials, emerge from the analysis. When these length scales are computed for three natural materials-nacre, collagen molecules, and spider silk fibers-very good agreement is found as compared with experimental measurements. The model was then used to interpret load transfer capabilities in synthetic carbon-based materials through parametrization of in situ SEM shear experiments on overlapping multiwall carbon nanotubes.

Novel method for mechanical characterization of polymeric nanofibers.

Abstract: A novel method to perform nanoscale mechanical characterization of highly deformable nanofibers has been developed. A microelectromechanical system (MEMS) test platform with an on-chip leaf-spring load cell that was tuned with the aid of a focused ion beam was built for fiber gripping and force measurement and it was actuated with an external piezoelectric transducer. Submicron scale tensile tests were performed in ambient conditions under an optical microscope. Engineering stresses and strains were obtained directly from images of the MEMS platform, by extracting the relative rigid body displacements of the device components by digital image correlation. The accuracy in determining displacements by this optical method was shown to be better than 50nm. In the application of this method, the mechanical behavior of electrospun polyacrylonitrite nanofibers with diameters ranging from 300to600nm was investigated. The stress-strain curves demonstrated an apparent elastic-perfectly plastic behavior with elastic modulus of 7.6±1.5GPa and large irreversible strains that exceeded 220%. The large fiber stretch ratios were the result of a cascade of periodic necks that formed during cold drawing of the nanofibers.

Fast parallel algorithms for short-range molecular dynamics

Abstract: 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.