About: Indian Institute of Technology (BHU) Varanasi is a education organization based out in Varanasi, India. It is known for research contribution in the topics: Catalysis & Dielectric. The organization has 2309 authors who have published 4540 publications receiving 41418 citations.
Papers published on a yearly basis
University of South Carolina1, Los Alamos National Laboratory2, Moscow State University3, Delhi Technological University4, University of Paris5, University of California, Davis6, Indian Institute of Technology (BHU) Varanasi7, University of Moratuwa8, University of Illinois at Urbana–Champaign9, California Polytechnic State University10, Sandia National Laboratories11, Max Planck Society12, Indian Institute of Technology Kharagpur13, French Institute for Research in Computer Science and Automation14, University of New Mexico15, Charles University in Prague16, Birla Institute of Technology and Science17, Indian Institute of Technology Bombay18, University of West Bohemia19
TL;DR: The architecture of SymPy is presented, a description of its features, and a discussion of select domain specific submodules are discussed, to become the standard symbolic library for the scientific Python ecosystem.
Abstract: SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.
TL;DR: In this paper, a review summarizes recent researches on synthesis, thermophysical properties, heat transfer and pressure drop characteristics, possible applications and challenges of hybrid nanofluids, and showed that proper hybridization may make the hybrid nanoparticles very promising for heat transfer enhancement, however, lot of research works are still needed in the fields of preparation and stability, characterization and applications to overcome the challenges.
Abstract: Researches on the nanofluids have been increased very rapidly over the past decade. In spite of some inconsistency in the reported results and insufficient understanding of the mechanism of the heat transfer in nanofluids, it has been emerged as a promising heat transfer fluid. In the continuation of nanofluids research, the researchers have also tried to use hybrid nanofluid recently, which is engineered by suspending dissimilar nanoparticles either in mixture or composite form. The idea of using hybrid nanofluids is to further improvement of heat transfer and pressure drop characteristics by trade-off between advantages and disadvantages of individual suspension, attributed to good aspect ratio, better thermal network and synergistic effect of nanomaterials. This review summarizes recent researches on synthesis, thermophysical properties, heat transfer and pressure drop characteristics, possible applications and challenges of hybrid nanofluids. Review showed that proper hybridization may make the hybrid nanofluids very promising for heat transfer enhancement, however, lot of research works is still needed in the fields of preparation and stability, characterization and applications to overcome the challenges.
TL;DR: In this paper, a comprehensive review of the resources of lithium and status of different processes/technologies in vogue or being developed for extracting lithium and associated metals from both primary and secondary resources are summarized.
Abstract: In this comprehensive review resources of lithium and status of different processes/technologies in vogue or being developed for extraction of lithium and associated metals from both primary and secondary resources are summarized Lithium extraction from primary resources such as ores/minerals (spodumene, petalite and lepidolite) by acid, alkaline and chlorination processes and from brines by adsorption, precipitation and ion exchange processes, is critically examined Problems associated with the exploitation of other resources such as bitterns and seawater are highlighted As regards the secondary resources, the industrial processes followed and the newer developments aiming at the recovery of lithium from lithium ion batteries (LIBs) are described in detail In particular pre-treatment of the spent LIBs, leaching of metals from the cathode material in different acids and separation of lithium and other metals from the leach liquors, are discussed Although spent LIBs are currently processed to recover cobalt and other base metals but not lithium, there is a good prospect for the recovery of lithium in the coming years Varying compositions of batteries for different applications require development of a suitable recycling process to recover metals from all types of LIBs
18 Jun 2018
TL;DR: The use of stereo sequences for learning depth and visual odometry enables the use of both spatial and temporal photometric warp error, and constrains the scene depth and camera motion to be in a common, real-world scale.
Abstract: Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner. Recent approaches to single view depth estimation explore the possibility of learning without full supervision via minimizing photometric error. In this paper, we explore the use of stereo sequences for learning depth and visual odometry. The use of stereo sequences enables the use of both spatial (between left-right pairs) and temporal (forward backward) photometric warp error, and constrains the scene depth and camera motion to be in a common, real-world scale. At test time our framework is able to estimate single view depth and two-view odometry from a monocular sequence. We also show how we can improve on a standard photometric warp loss by considering a warp of deep features. We show through extensive experiments that: (i) jointly training for single view depth and visual odometry improves depth prediction because of the additional constraint imposed on depths and achieves competitive results for visual odometry; (ii) deep feature-based warping loss improves upon simple photometric warp loss for both single view depth estimation and visual odometry. Our method outperforms existing learning based methods on the KITTI driving dataset in both tasks. The source code is available at https://github.com/Huangying-Zhan/Depth-VO-Feat.
TL;DR: In this paper, the conditions for the dissolution of valuable metals were optimized while varying the parameters such as acid concentration, leaching time, temperature and pulp density, and it was found that with 1M H2SO4 and 0.075 M NaHSO3 as reducing agent ∼96.7% Li, 91.6% Co, 96.4% Ni and 87.9% Mn were recovered in 4h at 368 K and a pulp density of 20 g/L.
Abstract: In this study, sulfuric acid leaching was applied to recover lithium, cobalt, nickel and manganese from the cathodic active material of spent LIBs in presence of a reducing agent, sodium bisulfite. The conditions for the dissolution of valuable metals were optimized while varying the parameters such as acid concentration, leaching time, temperature and pulp density. It was found that with 1 M H2SO4 and 0.075 M NaHSO3 as reducing agent ∼96.7% Li, 91.6% Co, 96.4% Ni and 87.9% Mn were recovered in 4 h at 368 K and a pulp density of 20 g/L. Kinetic data for the dissolution of the metals such as Li, Co and Ni in the temperature range 308–368 K showed best fit to the kinetic model governed by the empirical logarithmic rate law. Leaching of the metals proceeded through the diffusion of lixiviant on the surface of the substrate particles, which was corroborated by XRD phase analyses and SEM–EDS of the untreated sample and the leach residues. From the leach liquor, >98% Co was recovered as cobalt oxalate (CoC2O4·2H2O) by precipitation with oxalic acid. MnCO3, NiCO3 and Li2CO3 were precipitated from the cobalt depleted solution. By this process, high recovery of Li and Co could be achieved in the solution and then in the form of carbonate and oxalate, respectively along with the recovery of Mn and Ni as their carbonates.
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|Vijay P. Singh||106||1699||55831|
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