Rapid sintering of ultra-fine WC-10 wt% Co by high-frequency induction heating

High-frequency induction-heated sintering (HFIHS) is utilized to consolidate ultra-fine grain WC-10 wt% Co. Densification to near theoretical density in a relatively short time can be accomplished with insignificant change in grain size. WC-10 wt.% Co with a relative density of up to 99.5% was produced within 1 min with the simultaneous application of 60 MPa pressure. The average grain size of the densified material was about 260 nm and the mean free path in the cemented carbide was about 11 nm. The sintered material had fracture toughness and hardness values of 13 MPa^.m^1/2 and 1886 kg/mm², respectively. The hardness is comparable to literature values but the fracture toughness is about two times higher. These results are interpreted in terms of current effects on sintering and mass transport. Higher heating rates result in higher density with smaller WC grain size, and higher current-induced solubility of WC in Co is proposed as an explanation for the high fracture toughness.     

Influence of Synthesis Temperature on the Defect Structure of Boron Carbide: Experimental and Modeling Studies

Boron carbide (B₄C) was synthesized from the elements at temperatures ranging from 1300° to 2100°C using the spark plasma synthesis method. Significant densification commenced at about 1500°C and was accompanied by a corresponding decrease in the defect structure of this carbide. Changes in the X-ray diffraction patterns were in agreement with predictions of simulation studies based on the presence of twins. Transmission electron microscopy observations were consistent with the experimental observations and the modeling predictions.     

Hep-Pred: Hepatitis C Staging Prediction Using Fine Gaussian SVM

Hepatitis C is a contagious blood-borne infection, and it is mostly asymptomatic during the initial stages. Therefore, it is difficult to diagnose and treat patients in the early stages of infection. The disease’s progression to its last stages makes diagnosis and treatment more difficult. In this study, an AI system based on machine learning algorithms is presented to help healthcare professionals with an early diagnosis of hepatitis C. The dataset used for our Hep-Pred model is based on a literature study, and includes the records of 1385 patients infected with the hepatitis C virus. Patients in this dataset received treatment dosages for the hepatitis C virus for about 18 months. A former study divided the disease into four main stages. These stages have proven helpful for doctors to analyze the liver’s condition. The traditional way to check the staging is the biopsy, which is a painful and time-consuming process. This article aims to provide an effective and efficient approach to predict hepatitis C staging. For this purpose, the proposed technique uses a fine Gaussian SVM learning algorithm, providing 97.9% accurate results.     

Automated techniques for visualization and mapping of articular cartilage in MR images of the osteoarthritic knee: a base technique for the assessment of microdamage and submicro damage

The purpose of this paper is to describe automated techniques for the visualization and mapping of articular cartilage in magnetic resonance (MR) images of the osteoarthritic knee. The MR sequences and analysis software which will be described allow the assessment of cartilage damage using a range of standard scanners. With high field strength systems it would be possible, using these techniques, to assess micro-damage. The specific aim of the paper is to develop and validate software for automated segmentation and thickness mapping of articular cartilage from three-dimensional (3-D) gradient-echo MR images of the knee. The method can also be used for MR-based assessment of tissue engineered grafts. Typical values of cartilage thickness over seven defined regions can be obtained in patients with osteoarthritis (OA) and control subjects without OA. Three groups of patients were studied. The first group comprised patients with moderate OA in the age range 45-73 years. The second group comprised asymptomatic volunteers of 50-65 years; the third group, younger volunteers selected by clinical interview, history and X-ray. In this paper, sagittal 3-D spoiled-gradient steady-state acquisition images were obtained using a 1.5-T GE whole-body scanner with a specialist knee coil. For validation bovine and porcine cadaveric knees were given artificial cartilage lesions and then imaged. The animal validations showed close agreement between direct lesion measurements and those obtained from the MR images. The feasibility of semi-automated segmentation is demonstrated. Regional cartilage thickness values are seen as having practical application for fully automated detection of OA lesions even down to the submicrometer level.     

原料粒度及组成对TiB2－xAl复合体系的SHS过程影响

从理论和试验的角度,分析了原料粒度及组成对TiB₂-xAl复合体系的SHS过程的影响.随着金属相含量的不同,实际燃烧温度与理论计算值的变化趋势是一致的.燃烧波速度则随着金属相含量的不同出现一个极大值.由于Ti和B颗粒直接参入化学反应,而Al相在SHS过程中只是产生状态的变化,因而Ti、B颗粒尺寸的变化比Al颗粒尺寸的变化对SHS过程的影响要更大一些.     

Numerical analysis of a magnetic nanoparticle-enhanced microfluidic surface-based bioassay

An innovative magnetic nanoparticle (MNP)-based strategy for enhancing the dynamics and kinetics of surface-based antigen–antibody binding in a microfluidic platform is presented in this study. Finite element technique was employed for quantifying the effect of convection, diffusion, reaction and magnetic field on the detection performance of surface-based bio-assay. It was identified that diffusion is rate limiting when compared with reaction and convection. In order to reduce the detection time, increasing diffusion transport or in general bringing more target antigen towards the surface-bound antibody will be most effective. A novel and simple strategy based on tagging the antigen with MNPs was demonstrated using the numerical model. It was found that local concentration of antigen–MNP complex in the vicinity of sensing surface was increased when magnetic field was used. Different configurations of magnetic field around the microchannel for focusing target antigen towards the sensing surface were simulated and the most optimized configuration was identified. Furthermore, it was quantitatively demonstrated that MNP enhanced the surface-binding kinetics and reduced the detection time of target antigen by almost 42%. Moreover, when compared with physical means of reducing diffusion barrier, MNP-based detection was 35% more efficient. Overall, MNPs enhanced the mass transport of target antigen towards sensing surface which resulted in considerable reduction in detection time. The simulations performed using the developed model will not only help to investigate a wide range of design parameters but also provide generic strategy that can be exploited at the concept stage for designing, optimizing and developing efficient and fast small-scale surface-based bioassays.     

PERFORMANCE CHARACTERISTICS OF A CERAMIC MEMBRANE SYSTEM FOR MICROFILTRATION OF CORN STARCH HYDROLYSATE

A ceramic microfiltration membrane was used for the clarification of corn starch hydrolysate, having a dextrose equivalence of 95, to study the effect of process variables (transmembrane pressure, cross-flow velocity, and feed concentration) on permeate flux. Flux increased with increased cross-flow velocity for all transmembrane pressures and feed concentrations up to a volume concentration ratio of 100. Flux became asymptotic at pressures of 200-375kPa, indicating that microfiltration performance was limited by concentration-polarization. The optimum transmembrane pressure was higher at higher cross-flow velocities. A process model based on the resistances-in-series concept adequately described the observed variation of permeate flux with process variables such as transmembrane pressure, cross-flow velocity and feed concentration. Resistance due to concentration polarization decreased linearly with increase in cross-flow velocities for all feed concentrations, while fouling resistance increased linearly with increase in feed concentration.