Automated determination of size and morphology information from soot transmission electron microscope (TEM)-generated images

The thermophoretic sampling of particulates from hot media, coupled with transmission electron microscope (TEM) imaging, is a combined approach that is widely used to derive morphological information. The identification and the measurement of the particulates, however, can be complex when the TEM images are of low contrast, noisy, and have non-uniform background signal level. The image processing method can also be challenging and time consuming, when the samples collected have large variability in shape and size, or have some degree of overlapping. In this work, a three-stage image processing sequence is presented to facilitate time-efficient automated identification and measurement of particulates from the TEM grids. The proposed processing sequence is first applied to soot samples that were thermophoretically sampled from a laminar non-premixed ethylene-air flame. The parameter values that are required to be set to facilitate the automated process are identified, and sensitivity of the results to these parameters is assessed. The same analysis process is also applied to soot samples that were acquired from an externally irradiated laminar non-premixed ethylene-air flame, which have different geometrical characteristics, to assess the morphological dependence of the proposed image processing sequence. Using the optimized parameter values, statistical assessments of the automated results reveal that the largest discrepancies that are associated with the estimated values of primary particle diameter, fractal dimension, and prefactor values of the aggregates for the tested cases, are approximately 3, 1, and 10 %, respectively, when compared with the manual measurements.     

Fabrication of Ni-Co binary oxide/reduced graphene oxide composite with high capacitance and cyclicity as efficient electrode for supercapacitors

Nickel-cobalt binary oxide/reduced graphene oxide (G-NCO) composite with high capacitance is synthesized via a mild method for electrochemical capacitors. G-NCO takes advantages of reduced graphene oxide (RGO) and nickel-cobalt binary oxide. As an appropriate matrix, RGO is beneficial to form homogeneous structure and improve the electron transport ability. The binary oxide owns more active sites than those of nickel oxide and cobalt oxide to promote the redox reaction. Attributed to the well crystallinity, homogeneous structure, increased active sites, and improved charge transfer property, the G-NCO composite exhibits highly enhanced electrochemical performance compared with G-NiO and G-Co₃O₄ composites. The specific capacitance of the G-NCO composite is about 1750 F g^?1 at 1 A g^?1 together with capacitance retention of 79 % (900/1138 F g^?1) over 10,000 cycles at 4 A g^?1. To research its practical application, an asymmetric supercapacitor with G-NCO as positive electrode and activated carbon as negative electrode was fabricated. The asymmetric device exhibits a prominent energy density of 37.7 Wh kg^?1 at a power density of 800 W kg^?1. The modified G-NCO composite shows great potential for high-capacity energy storage.     

Linear analysis of degree correlations in complex networks

Many real-world networks such as the protein–protein interaction networks and metabolic networks often display nontrivial correlations between degrees of vertices connected by edges. Here, we analyse the statistical methods used usually to describe the degree correlation in the networks, and analytically give linear relation in the degree correlation. It provides a simple and interesting perspective on the analysis of the degree correlation in networks, which is usefully complementary to the existing methods for degree correlation in networks. Especially, the slope in the linear relation corresponds exactly to the degree correlation coefficient in networks, meaning that it can not only characterize the level of degree correlation in networks, but also reflects the speed that the average nearest neighbours’ degree varies with the vertex degree. Finally, we applied our results to several real-world networks, validating the conclusions of the linear analysis of degree correlation. We hope that the work in this paper can be helpful for further understanding the degree correlation in complex networks.     

Limited path percolation in complex networks

We study the stability of network communication after removal of a fraction q=1-p of links under the assumption that communication is effective only if the shortest path between nodes i and j after removal is shorter than al(ij)(a> or =1) where l(ij) is the shortest path before removal. For a large class of networks, we find analytically and numerically a new percolation transition at p(c)=(kappa(0)-1)((1-a)/a), where kappa(0) [triple bond] / and k is the node degree. Above p(c), order N nodes can communicate within the limited path length al(ij), while below p(c), N(delta) (delta<1) nodes can communicate. We expect our results to influence network design, routing algorithms, and immunization strategies, where short paths are most relevant.     

Evolution of the correlation between orthogonal polarization patterns in broad-area lasers

We measure polarization-resolved instantaneous patterns in a large-aspect ratio quasi-isotropic Nd:YAG laser. High correlation between the instantaneous orthogonal polarization patterns recorded at the earlier stages of the laser pulse has been found due to the strong cross saturation between both polarization modes.