科研成果

The critical behaviors of a granular system at the jamming transition have been extensively studied from both mechanical and thermodynamic perspectives. In this work, we numerically investigate the jamming behaviors of a variety of frictionless non-spherical particles, including spherocylinder, ellipsoid, spherotetrahedron and spherocube. In particular, for a given particle shape, a series of random configurations at different fixed densities are generated and relaxed to minimize interparticle overlaps using the relaxation algorithm. We find that as the jamming point (i.e., point J">J) is approached, the number of iteration steps (defined as the “time-scale” for our systems) required to completely relax the interparticle overlaps exhibits a clear power-law divergence. The dependence of the detailed mathematical form of the power-law divergence on particle shapes is systematically investigated and elucidated, which suggests that the shape effects can be generally categorized as elongation and roundness. Importantly, we show the jamming transition density can be accurately determined from the analysis of time-scale divergence for different non-spherical shapes, and the obtained values agree very well with corresponding ones reported in literature. Moreover, we study the plastic behaviors of over-jammed packings of different particles under a compression–expansion procedure and find that the jamming of ellipsoid is much more robust than other non-spherical particles. This work offers an alternative approximate procedure besides conventional packing algorithms for studying athermal jamming transition in granular system of frictionless non-spherical particles.

The side-chain structures of conjugated molecules are well recognized to sensitively influence the crystallinity, morphology and thus carrier transport properties of organic semiconductors. Here, by varying the alkyl side-chain length in the polymer acceptors, the effect of side-chain engineering on the photovoltaic performance is systematically studied in all-polymer solar cells. Clear trends of first an increase and then a decrease in the J(sc) and FF values are observed as the branched alkyl groups are extended from 4 to 8 carbons. Correspondingly, the maximum average PCE (ca. 7.40%) is attained with an acceptor bearing a branched side-chain length of seven carbon atoms.

Thermionic electron emitters have recently been scaled down to the microscale using microfabrication technologies and graphene as the filament. While possessing several advantages over field emitters, graphene-based thermionic micro-emitters still exhibit low emission current density and efficiency. Here, we report nanoscale thermionic electron emitters (NTEEs) fabricated using microfabrication technologies and single-walled carbon nanotubes (SWCNTs), the thinnest conducting filament we can use. The SWCNT NTEEs exhibit an emission current density as high as 0.45 × 105 A cm−2, which is superior to that of traditional thermionic emitters and five orders of magnitude higher than that of graphene-based thermionic emitters. The emission characteristics of SWCNT NTEEs are found to strongly depend on the electrical properties of the SWCNTs, with metallic SWCNT NTEEs showing a substantially lower turn-on voltage and more reproducible emission performances than those based on semiconducting SWCNTs. Our results indicate that SWCNT NTEEs are promising for electron source applications

FPGA-based reconfigurable dataflow engines provide a novel architecture to achieve breakthroughs in both time and energy to solution in numerical simulations. This article presents an efficient dataflow methodology for solving the Euler atmospheric dynamic equations, an essential step for mesoscale atmospheric simulation. The authors present customizable optimizations such as hybrid decomposition, algorithmic offsetting, customizable window buffer, and mixed-precision arithmetic. Combining algorithmic and architectural optimizations, they map a complex Euler stencil kernel into a single FPGA chip and develop a long streaming pipeline that can perform 956 mixed-precision operations per cycle. They also fully optimize the Euler performance over different traditional processors and accelerators based on multicore and many-core architectures. Their dataflow design outperforms traditional multicore and many-core counterparts in both time and energy to solution. This work demonstrates the promising potential of employing dataflow architectures in numerical simulations to overcome some of the major constraints facing mainstream processors and accelerators.

Atmospheric particulate polycyclic aromatic hydrocarbons (PAHs) have been drawing sustained attention due to their health risk and effects on air pollution. It is essential to determine the main sources and reduce atmospheric levels of PAHs to protect human health. PAHs in PM2.5 have been detected at five sites located in five districts in Shanghai, a modern metropolitan city in China. Spatial and temporal variations of composition profiles and sources of PAHs at each site in each season were investigated. The results showed that atmospheric particulate PAHs level in Shanghai was the lowest in summer and the highest in winter, dominated by high molecular weight (HMW) PAHs. Analysis with a combination of coefficients of Pearson's correlation and coefficient of divergences indicated heterogeneous spatial and temporal distribution for LMW PAHs and homogenous distribution for HMW PAHs. Diagnostic ratios and positive matrix factorization (PMF) model both identified pyrogenic sources as the main contributor of PAHs in Shanghai, with vehicular source contribution of 32-43% to the total PAHs annually and around 20% from biomass burning emissions in urban and urban buildup areas. While in winter, coal combustion and biomass burning could act as two major sources of PAHs in suburban areas, which could contribute to >70% of total PAHs measured in PM2.5 in Shanghai. (C) 2016 Elsevier B.V. All rights reserved.