A review on novel activation strategy on carbonaceous materials with special morphology/texture for electrochemical storage

Various novel carbonaceous materials including carbon nanotubes,nano-onions,carbon microspheres,graphene nanosheets,and carbon microfibers with unique properties,such as tunable surface area and pore size,high chemical stability,cost-effective and facile preparation,have attracted enormous interest for many applications.Also essential,the activation processes play a critical role to achieve these valuable properties.In this review,we provide a thorough analysis of the emerging nano-and microscopic carbon species with special morphology/textures and currently available types of chemical activation agents,and novel activation strategy to enhance electrochemical performance of activated carbon material in electrical energy storage devices including supercapacitor and alkaline ions batteries.A particular emphasis is set on recent advance in activated carbon materials with special morphology/textures for supercapacitors and sodium ion batteries.

Use of digital image analysis combined with fractal theory to determine particle morphology and surface texture of quartz sands

The particle morphology and surface texture play a major role in influencing mechanical and hydraulic behaviors of sandy soils. This paper presents the use of digital image analysis combined with fractal theory as a tool to quantify the particle morphology and surface texture of two types of quartz sands widely used in the region of Vitória, Espírito Santo, southeast of Brazil. The two investigated sands are sampled from different locations. The purpose of this paper is to present a simple, straightforward,reliable and reproducible methodology that can identify representative sandy soil texture parameters.The test results of the soil samples of the two sands separated by sieving into six size fractions are presented and discussed. The main advantages of the adopted methodology are its simplicity, reliability of the results, and relatively low cost. The results show that sands from the coastal spit(BS) have a greater degree of roundness and a smoother surface texture than river sands(RS). The values obtained in the test are statistically analyzed, and again it is confirmed that the BS sand has a slightly greater degree of sphericity than that of the RS sand. Moreover, the RS sand with rough surface texture has larger specific surface area values than the similar BS sand, which agree with the obtained roughness fractal dimensions. The consistent experimental results demonstrate that image analysis combined with fractal theory is an accurate and efficient method to quantify the differences in particle morphology and surface texture of quartz sands.

Effect of cold rolling on the microstructural, magnetic, mechanical, and corrosion properties of AISI 316L austenitic stainless steel

This study has evaluated the effect of different levels of cold rolling（from 0 to 50%）on the microstructural,magnetic,and mechanical properties and the corrosion behavior of 316L austenitic stainless steel in Na Cl（1 mol/L）＋H_2SO_4（0.5 mol/L）solution.Microstructural examinations using optical microscopy revealed the development of a morphological texture from coaxial to elongated grains during the cold-rolling process.Phase analysis carried out on the basis of X-ray diffraction confirmed the formation of the ferromagneticα′-martensite phase under the stresses applied during cold rolling.This finding is in agreement with magnetic measurements using a vibrating sample magnetometer.Mechanical properties determined by tensile and Vickers microhardness tests demonstrated an upward trend in the hardness-to-yield strength ratio with increasing cold-rolling percentage,representing a reduction in the material’s work-hardening ability.Uniform and localized corrosion parameters were estimated via potentiodynamic polarization corrosion tests and electrochemical impedance spectroscopy.In contrast to the uniform corrosion,wherein the corrosion current density increased with increasing cold-working degree because of the high density of microstructural defects,the passive potential range and breakdown potential increased by cold working,showing greater resistance to pit nucleation.Although pits were formed,the cold-rolled material repassivation tendency decreased because of the broader hysteresis anodic loop,as confirmed experimentally by observation of the microscopic features after electrochemical cyclic polarization evaluations.

Microstructure of polycrystalline Fe_(82)Ga_(18) sample with solidification texture

Main attention of this paper was devoted to studying the effect of solidification texture on microstructure（phase constituents,grain morphology,and magnetic domain structure） of polycrystalline Fe_（82）Ga_（18） sample.The alloy was melted using button arc melting and solidified in water-cooled copper mold.Optical microscope（OM） results confirm the development of large columnar grains in the solidification microstructure.Phase constitution and magnetic domain structures of the sample were studied by X-ray diffraction（XRD） and magnetic force microscopy（MFM）.Results show a single-phase solid solution with an A2 structure for the sample which consists of regularly aligned magnetic domains.Although some maze-like subdomains are found in few regions,well-aligned stripe-like domains are predominant patterns in the sample.It demonstrates the high dependence of grain morphology and magnetic domain structure upon a preferred crystallographic direction during solidification.

Tumor segmentation in lung CT images based on support vector machine and improved level set

In lung CT images, the edge of a tumor is frequently fuzzy because of the complex relationship between tumors and tissues, especially in cases that the tumor adheres to the chest and lung in the pathology area. This makes the tumor segmentation more difficult. In order to segment tumors in lung CT images accurately, a method based on support vector machine(SVM) and improved level set model is proposed. Firstly, the image is divided into several block units; then the texture, gray and shape features of each block are extracted to construct eigenvector and then the SVM classifier is trained to detect suspicious lung lesion areas; finally, the suspicious edge is extracted as the initial contour after optimizing lesion areas, and the complete tumor segmentation can be obtained by level set model modified with morphological gradient. Experimental results show that this method can efficiently and fast segment the tumors from complex lung CT images with higher accuracy.