Synthesis and characterization of nanostructured iron compounds prepared from the decomposition of iron pentacarbonyl dispersed into carbon materials with varying porosities

This work describes the production and characterization of carbon-iron nanocomposites obtained from the decomposition of iron pentacarbonyl (Fe(CO)₅) mixed with different carbon materials: a high surface area activated carbon (AC), powdered graphite (G), milled graphite (MG), and carbon black (CB). The nanocomposites were prepared either under argon or in ambient atmosphere, with a fixed ratio of Fe(CO)₅ (4.0 mL) to carbon precursor (2.0 g). The images of scanning electron microscopy and the analysis of textural properties indicated the presence of nanostructured Fe compounds homogeneously dispersed into the different classes of pores of the carbon matrices. The elemental Fe content was always larger for samples prepared in ambient atmosphere, reaching values in the range of 20–32 wt%. On the other hand, samples prepared under argon showed reduced Fe content, with values in the range 5–10 wt% for samples prepared from precursors with low surface area (G, MG, and CB) and a much higher value (~19 wt%) for samples prepared from the precursor of high surface area (AC). Mössbauer spectroscopy and X-ray diffractometry showed that the nanoparticles were mostly composed of iron oxides in the case of the samples prepared in oxygen-rich ambient atmosphere and also for the AC-derived nanocomposite prepared under argon, which is consistent with the large oxygen content of this precursor. For the other precursors, with reduced or no oxygen content, metallic iron and iron carbides were found to be the dominant phases in samples prepared under oxygen-free atmosphere. The samples prepared in ambient atmosphere and the AC-derived sample prepared under argon exhibited superparamagnetic behavior at room temperature, as revealed by temperature-dependent magnetization curves and Mössbauer spectroscopy.     

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.     

Generation of Bianchi type V cosmological models with varying Λ-term

Bianchi type V perfect fluid cosmological models are investigated with cosmological term varying with time. Using a generation technique (Camci et al., 2001), it is shown that Einsteins field equations are solvable for any arbitrary cosmic scale function. Solutions for particular forms of cosmic scale functions are also obtained. The cosmological constant is found to be a decreasing function of time, which is supported by results from recent type Ia supernovae observations. Some physical aspects of the models are also discussed.