Cheng Yan

Bio: Cheng Yan is an academic researcher from Queensland University of Technology. The author has contributed to research in topics: Graphene & Materials science. The author has an hindex of 48, co-authored 383 publications receiving 10453 citations. Previous affiliations of Cheng Yan include Dalian University of Technology & Harbin Institute of Technology.

Tunneling effect in a polymer/carbon nanotube nanocompositestrain sensor

Abstract: A strain sensor has been fabricated from a polymer nanocomposite with multiwalled carbon nanotube (MWNT) fillers. The piezoresistivity of this nanocomposite strain sensor has been investigated based on an improved three-dimensional (3D) statistical resistor network model incorporating the tunneling effect between the neighboring carbon nanotubes (CNTs), and a fiber reorientation model. The numerical results agree very well with the experimental measurements. As compared with traditional strain gauges, much higher sensitivity can be obtained in the nanocomposite sensors when the volume fraction of CNT is close to the percolation threshold. For a small CNT volume fraction, weak nonlinear piezoresistivity is observed both experimentally and from numerical simulation. The tunneling effect is considered to be the principal mechanism of the sensor under small strains.

The electrical properties of polymer nanocomposites with carbon nanotube fillers

Abstract: The electrical properties of polymer nanocomposites containing a small amount of carbon nanotube (CNT) are remarkably superior to those of conventional electronic composites. Based on three-dimensional (3D) statistical percolation and 3D resistor network modeling, the electrical properties of CNT nanocomposites, at and after percolation, were successfully predicted in this work. The numerical analysis was also extended to investigate the effects of the aspect ratio, the electrical conductivity, the aggregation and the shape of CNTs on the electrical properties of the nanocomposites. A simple empirical model was also established based on present numerical simulations to predict the electrical conductivity in several electronic composites with various fillers. This investigation further highlighted the importance of theoretical and numerical analyses in the exploration of basic physical phenomena, such as percolation and conductivity in novel nanocomposites.

The electrical properties of polymer nanocomposites with carbon nanotube fillers

Abstract: The electrical properties of polymer nanocomposites containing a small amount of carbon nanotube (CNT) are remarkably superior to those of conventional electronic composites. Based on three-dimensional (3D) statistical percolation and 3D resistor network modeling, the electrical properties of CNT nanocomposites, at and after percolation, were successfully predicted in this work. The numerical analysis was also extended to investigate the effects of the aspect ratio, the electrical conductivity, the aggregation and the shape of CNTs on the electrical properties of the nanocomposites. A simple empirical model was also established based on present numerical simulations to predict the electrical conductivity in several electronic composites with various fillers. This investigation further highlighted the importance of theoretical and numerical analyses in the exploration of basic physical phenomena, such as percolation and conductivity in novel nanocomposites.

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.

A stretchable carbon nanotube strain sensor for human-motion detection

Abstract: Thin films of single-wall carbon nanotube have been used to create stretchable devices that can be incorporated into clothes and used to detect human motions.