Upgrading of Low-Grade Manganese Ore by Selective Reduction of Iron Oxide and Magnetic Separation

The utilization of low-grade manganese ores has become necessary due to the intensive mining of high-grade ores for a long time. In this study, calcined ferruginous low-grade manganese ore was selectively reduced by CO, which converted hematite to magnetite, while manganese oxide was reduced to MnO. The iron-rich component was then separated by magnetic separation. The effects of the various reduction parameters such as particle size, reduction time, temperature, and CO content on the efficiency of magnetic separation were studied by single-factor experiments and by a comprehensive full factorial experiment. Under the best experimental conditions tested, the manganese content in the ore increased from around 36?wt?pct to more than 44?wt?pct, and almost 50?wt?pct of iron was removed at a Mn loss of around 5?pct. The results of the full factorial experiments allowed the identification of the significant effects and yielded regression equations for pct Fe removed, Mn/Fe, and pct Mn loss that characterize the efficiency of the upgrading process.     

A mechano- and electrochemically controlled SnSb/C nanocomposite for rechargeable Li-ion batteries

At present, graphite (LiC₆: 372 mAh g⁻¹, 840 mAh cm⁻³) is used as the anode material for lithium-ion batteries. However, methods to enhance the energy density, cyclability, initial Coulombic efficiency, and rate capability of lithium-ion batteries are still actively being researched. Here, we report a simple, fast, and novel method for transforming micron-sized Sn and Sb powders into ca. 10 nm- and 2–3 nm-sized SnSb crystallites by mechanochemical synthesis and electrochemical reactions, respectively. These nanocrystallites are uniformly distributed in an amorphous carbon matrix, resulting in a SnSb/C nanocomposite structure. The fabricated SnSb/C nanocomposite showed excellent electrochemical properties, such as a high energy density (1st charge: 706 mAh g⁻¹), long cyclability (ca. 550 mAh g⁻¹ over 300 cycles), good initial Coulombic efficiency (ca. 81%), and a fast rate capability (1C: 590 mAh g⁻¹, 2C: 550 mAh g⁻¹).     

Influence of corner radius on the near wake structure of a transversely oscillating square cylinder

The near wake flow field features of transversely oscillating square section cylinders with different corner radii were studied in an attempt to assess the influence of corner radius. The investigation was performed by using particle image velocimetry (PIV) technique in a water channel with a turbulence intensity of 6.5%. Five models were studied with r/B=0, 0.1, 0.2, 0.3 and 0.5 (r is the corner radius and B is the characteristic dimension of the body), and the body oscillation was limited to lock-in condition (at fe/fo=1.0; fe is the excitation frequency and fo is the vortex shedding frequency from a stationary cylinder at the same Re). The corner radius was found to significantly influence the flow features around the bodies. Except for r/B=0.5, for all the other cases of r/B ratios, cycle-to cycle variation in the mode of vortex shedding was observed in the case of oscillating cylinders inducing highly non-linear wake characteristics. Apart from variation in the shedding mode, changes in shedding cycle timing were also observed for sharp and rounded square cylinders. The hgher the r/B ratio, shedding in the near wake was found to be more uniform (lesser variation in shedding cycle timings). Another admissible shedding mechanism is newly identified to operate in the near wake of oscillating cylinders now being called as the ‘passive shedding’ mechanism. Results indicate that increasing the corner radius suppresses the possible instabilities of the cylinder.     

Stock price prediction using neural networks with RasID-GA

In general, neural networks are widely used in pattern recognition, system modeling and prediction, and can model complex nonlinear systems. In the previous work, we proposed a novel training algorithm, Adaptive Random Search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA), for training the multibranch recurrent neural networks recently developed. In this paper, RasID-GA has been applied to predict stock market prices using the multibranch feed forward neural networks. We predicted the next day's closing stock price with several past closing stock prices. We used the stock prices of 20 brands for 720 days in order to evaluate the generalization ability of the proposed method. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Effects of Cr Reduction on High-Temperature Strength of High-Ni Austenitic Cast Steels Used for High-Performance Turbo-chargers

Since greater high-temperature strength is required for maintaining high-performance turbo-chargers at higher exhaust gas temperatures, e.g., 1323 K (1050 °C), high-Ni (20 wt pct) austenitic steel (ASTM HK40 steel) is presented as an excellent turbo-charger candidate material. To enhance the strength, three types of austenitic cast steel were fabricated in this study by controlling the Cr content in HK40 steel, and high-temperature strength improvement was achieved by detailed microstructural evolution including carbide formation and matrix strengthening. Room temperature and high-temperature strengths were expected to be proportional to the carbide volume fraction, but revealed an opposite trend because the steel containing more Cr (having more carbides) revealed lower strength than the steel containing less Cr (having fewer carbides). This result was associated mainly with the M₇C₃ to M₂₃C₆ decomposition occurring at high temperatures in the less-Cr-steel that beneficially strengthened the austenite matrix and reduced the hardness difference between the carbide and matrix, consequently improving the high-temperature strength. In considering the alloying prices (14 pct cost saving of alloying elements) as well as the high-temperature strength, the steel containing less Cr is promising for new high-performance turbo-charger applications.

     

Automated Estimation of Reinforced Precast Concrete Rebar Positions Using Colored Laser Scan Data

Precast concrete elements are widely adopted and the performance of precast structures is relying on the quality of connections between adjacent elements. For reinforced precast concrete elements, rebar positions are important for the overall structural performance, however, they are usually manually inspected. This study develops a technique for automated position estimation of rebars on reinforced precast concrete elements using colored laser scan data. A novel mixed pixel filter is developed to remove mixed pixels from the raw scan data based on both distance and color difference. A one‐class classifier is used for extracting rebars from all the data based on both geometric and color features of points. Furthermore, a novel rebar recognition algorithm is developed to recognize individual rebars based on two newly defined metrics. Experiments on two reinforced precast concrete bridge deck panels were conducted and showed that the proposed technique can accurately and efficiently estimate rebar positions.     

p-Type polymer-hybridized high-performance piezoelectric nanogenerators

Enhancing the output power of a nanogenerator is essential in applications as a sustainable power source for wireless sensors and microelectronics. We report here a novel approach that greatly enhances piezoelectric power generation by introducing a p-type polymer layer on a piezoelectric semiconducting thin film. Holes at the film surface greatly reduce the piezoelectric potential screening effect caused by free electrons in a piezoelectric semiconducting material. Furthermore, additional carriers from a conducting polymer and a shift in the Fermi level help in increasing the power output. Poly(3-hexylthiophene) (P3HT) was used as a p-type polymer on piezoelectric semiconducting zinc oxide (ZnO) thin film, and phenyl-C(61)-butyric acid methyl ester (PCBM) was added to P3HT to improve carrier transport. The ZnO/P3HT:PCBM-assembled piezoelectric power generator demonstrated 18-fold enhancement in the output voltage and tripled the current, relative to a power generator with ZnO only at a strain of 0.068%. The overall output power density exceeded 0.88 W/cm(3), and the average power conversion efficiency was up to 18%. This high power generation enabled red, green, and blue light-emitting diodes to turn on after only tens of times bending the generator. This approach offers a breakthrough in realizing a high-performance flexible piezoelectric energy harvester for self-powered electronics.     

Synthesis and characterization of silver nanoparticle composite with poly(p-Br-phenylsilane)

The one-pot synthesis and characterization of silver nanoparticle-poly(p-Br-phenylsilane) composites have been carried out. The conversion of silver(+1) salt to stable silver(0) nanoparticles is promoted by poly(p-Br-phenylsilane), Br-PPS possessing both possible reactive Si-H bonds in the polymer backbone and C-Br bonds in the substituents. The composites were characterized using XRD, TEM, FE-SEM, and solid-state UV-vis analytical techniques. TEM and FE-SEM data show the formation of the composites where large number of silver nanoparticles (less than 30 nm of size) are well dispersed throughout the Br-PPS matrix. XRD patterns are consistent with that for fcc-typed silver. The elemental analysis for Br atom and the polymer solubility confirm that the cleavage of C-Br bond and the Si-Br dative bonding were not occurred appreciably at ambient temperature. Nonetheless, TGA data suggest that some sort of cross-linking was occurred at high temperature. The size and processability of such nanoparticles depend on the ratio of metal to Br-PPS. In the absence of Br-PPS, most of the silver particles undergo macroscopic aggregation, which indicates that the polysilane is necessary for stabilizing the silver nanoparticles.     

Detection of nitroaromatic compounds based on phenylethylene-derivatized porous silicon

Nanocrystalline porous silicon (PSi) surfaces have been used to detect nitroaromatic compounds in vapor phase. The mode of photoluminescence (PL) is emphasized as a sensing attitude or detection technique. Quenching of PL from nanocrystalline porous surfaces as a transduction mode is measured upon the exposure of nitroaromatic compounds. To verify the detection of explosives, the surface of PSi is functionalized with different groups. The quenching mechanism of PL is attributed to the electron transfer behaviors of quantum-sized nano-crystallites in the PSi matrix to the analytes (nitroaromatics). An attempt has been done to prove that the surface-derivatized photoluminescent PSi surfaces can act as versatile substrates for sensing behaviors due to having a large surface area and highly sensitive transduction mode.     

Fabrication of human IgG sensors based on porous silicon interferometer containing Bragg structures

A simply modified biosensor based on protein A-modified distributed Bragg reflectors (DBR) porous silicon (PSi) chip for the detection of human immunoglobin G (IgG) are developed. The fabrication, optical characterization, and surface derivatization of DBR PSi are investigated. The sensor system studied consist of multi-layer of porous silicon modified with protein-A. The sensor is operated by the measurement of the reflection peak in the white light reflection spectrum. Molecular binding is detected as a shift in wavelength of reflection peaks.