Investigation of Eccentric Open Eye Formation in a Slab Caster Tundish

Metallurgical and Materials Transactions B - Inert gas shrouding in tundish can result in the formation of a tundish open eye (TOE) due to the presence of reversed flows on the upper surface of the...     

天然有机质对纳米碳管环境行为的影响研究进展

随着纳米技术的快速发展,大量的纳米碳管(CNTs)不可避免地释放到环境中。由于其较大的憎水性表面,CNTs与有机污染物和天然有机质(NOM)强烈地相互作用。综述了NOM存在下CNTs的环境行为,包括NOM对CNTs分散特性和吸附有机污染物特性的影响。着重论述了NOM的理化性质对CNTs分散的影响,"拉拉链"或"胶束包裹"是主要的分散机制。强调应该对不同分散机制下分散的CNTs与有机污染物的相互作用给予更多的关注,提出了目前在液相环境中直接测定CNTs表面积的新思路,并对今后的研究方向进行了展望。     

Modeling the Interaction Between Fly Ash and Lime Under Water Curing by an Artificial Neural Network

An artificial neural network was used to model the chemical interaction between fly ash and lime with different ratios in water-cured compacts. As inputs for the model, different process parameters like pozzolanicity, surface area, unburnt carbon content of the ash samples, water-curing periods, and the proportion of initial lime content in the fly ash–lime mixes were used. Free lime remaining in the compacts after different curing periods was used as the output parameter. A generalized feedforward back-propagation three-layered neural network model was used with a tanhyperbolic transfer function at both the input and the output layer with 400 exemplars. For the training data, after 3500 iterations, the error value was found to be the minimum for the prediction mode. When the model was tested for the test data, the difference between the actual value of the strength and the predicted value of the strength was found to be within ±15%.     

Structural,microstructural, and thermal characterizations of a chalcopyrite concentrate from the Singhbhum shear zone,India

The structural and morphological characterizations of a chalcopyrite concentrate, collected from the Indian Copper Complex, Ghatshila, India, were carried out by X-ray diffraction, scanning electron mi...     

Learning Sparse Feature Representations Using Probabilistic Quadtrees and Deep Belief Nets

Learning sparse feature representations is a useful instrument for solving an unsupervised learning problem. In this paper, we present three labeled handwritten digit datasets, collectively called n-MNIST by adding noise to the MNIST dataset, and three labeled datasets formed by adding noise to the offline Bangla numeral database. Then we propose a novel framework for the classification of handwritten digits that learns sparse representations using probabilistic quadtrees and Deep Belief Nets. On the MNIST, n-MNIST and noisy Bangla datasets, our framework shows promising results and outperforms traditional Deep Belief Networks.     

Design and characterization of asymetrical super-lattice Si/4H-SiC pin photo diode array: a potential opto-sensor for future applications in bio-medical domain

Microsystem Technologies - Photo-sensors are integral part of different bio-medical diagnostic equipment. Each type of bio-molecules possess unique spectral fingerprint in visible wavelength region...     

Improved tire tread compounds using functionalized styrene butadiene rubber-silica filler/hybrid filler systems

Automotive industry is currently looking for an eco-friendly tire with low rolling resistance coefficient (RRc), better traction, wear resistance, and fatigue properties. Presently, solution styrene-butadiene rubber (SSBR)-silica systems are pursued for balancing between traction and RRc. However, the interaction between SSBR and silica is not enough to give satisfactory results. Functionalized-SSBR (FSSBR) leads to better rubber-silica interaction due to introduction of polar groups in the polymer chain. The present study investigates the influence of FSSBR, highly dispersible (HD) silica, and its hybrid filler systems with organically modified nanoclay (ONC) and exfoliated graphene nanoplatelet (xGnP). Both M_H, and Δtorque were higher for the FSSBR-HD silica compound (S1) with the lowest change in storage modulus (∆G') value, due to higher polymer-filler interaction. S1 exhibited 16% ice traction and 12% wet traction improvement with 29% lower rolling resistance over SSBR-silica compound. S1 showed the best wet traction rating and wear resistance. Replacing small portion of silica by ONC and xGnP improved the properties further. At 5 phr of nanofiller, TEM images revealed well-dispersed nanofillers in the FSSBR matrix. The xGnP compound showed the least crack growth. For both the cases, abradability decreased with higher nanofiller amount, due to better reinforcement of the rubber.     

The use of recycled elastomeric materials for sustainability in circular economy

The use of scrapped elastomeric goods primarily service failed or worn-out tires is a challenging task for the rubber society. To address this issue, recycling and reusing of these tires by different technique bring major relief toward environmental concern. Recycling technology generates secondary raw materials, which are important resources for manufacturing industries and contribute significantly for the sustainability in circular economy. In this work, an effort was made to understand the retention of physical properties as a function of crumb rubber loading and particle size. Four different sizes of cryogenically shredded crumb rubber particles (120, 140, 200, and 270 mesh) were physicochemically characterized and incorporated by 10 phr of each in two different grades of carbon black–filled, natural rubber–based tread compound. The mixed compounds were characterized and compared with the control one. Optical microscope and scanning electron microscope analyses concluded that the finer the particle size, the better is the dispersion of crumb rubber particles which provide superior mechanical, extrusion, and wear properties. Tensile properties were retained around 95%–97% for both 270-mesh loaded compounds, which indicates that reduction in particle size can increase the volume of recycled product utilization.     

Averaging theory and low-dimensional models for chemical reactors and reacting flows

A novel approach based on the Liapunov-Schmidt technique of bifurcation theory is presented for the spatial averaging of a class of convection-diffusion-reaction models. It is used to derive low-dimensional averaged models for different types of homogeneous and catalytic reactors, as well as coupled homogeneous-heterogeneous systems. For the homogeneous isothermal case, the averaged models consist of a pair of balance equations for each species A_j in terms of the mixing-cup (C_j,m) and spatially averaged (〈C_j〉) concentrations. The first (global) equation traces the evolution of C_j,m with residence time while the second (local) equation, which is independent of the reactor type, gives the local concentration gradient as a difference between C_j,m and 〈C_j〉 in terms of the local variables (such as species diffusivities, shear and reaction rates). For the wall-catalyzed reaction case, the averaged models are described by a pair of equations for each species in terms of C_j,m and the surface concentration C_j,s and are similar to the classical two-phase models of catalytic reactors. For the coupled homogeneous-heterogeneous case, the averaged models consist of three balance equations for each species in terms of C_j,m, 〈C_j〉 and C_j,s, and contain four mass transfer or exchange coefficients. The accuracy, convergence and the region of validity of the averaged models are examined for some special cases. Finally, the usefulness of the averaged models in predicting the reactor behavior is illustrated with an example for each of the three cases, homogeneous, heterogeneous and coupled homogeneous-heterogeneous case.