Indian Institute of Technology Bombay

About: Indian Institute of Technology Bombay is a education organization based out in Mumbai, India. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 16756 authors who have published 33588 publications receiving 570559 citations.

1D copper nanostructures: progress, challenges and opportunities.

Abstract: One-dimensional noble metal nanostructures are important components in modern nanoscience and nanotechnology due to their unique optical, electrical, mechanical, and thermal properties. However, their cost and scalability may become a major bottleneck for real-world applications. Copper, being an earth-abundant metallic element, is an ideal candidate for commercial applications. It is critical to develop technologies to produce 1D copper nanostructures with high monodispersity, stability and oxygen-resistance for future low-cost nano-enabled materials and devices. This article covers comprehensively the current progress in 1D copper nanostructures, most predominantly nanorods and nanowires. First, various synthetic methodologies developed so far to generate 1D copper nanostructures are thoroughly described; the methodologies are in conjunction with the discussion of microscopic, spectrophotometric, crystallographic and morphological characterizations. Next, striking electrical, optical, mechanical and thermal properties of 1D copper nanostructures are highlighted. Additionally, the emerging applications of 1D copper nanostructures in flexible electronics, transparent electrodes, low cost solar cells, field emission devices are covered, amongst others. Finally, there is a brief discussion of the remaining challenges and opportunities.

GRAINS: Generative Recursive Autoencoders for INdoor Scenes

Abstract: We present a generative neural network that enables us to generate plausible 3D indoor scenes in large quantities and varieties, easily and highly efficiently. Our key observation is that indoor scene structures are inherently hierarchical. Hence, our network is not convolutional; it is a recursive neural network, or RvNN. Using a dataset of annotated scene hierarchies, we train a variational recursive autoencoder, or RvNN-VAE, which performs scene object grouping during its encoding phase and scene generation during decoding. Specifically, a set of encoders are recursively applied to group 3D objects based on support, surround, and co-occurrence relations in a scene, encoding information about objects’ spatial properties, semantics, and relative positioning with respect to other objects in the hierarchy. By training a variational autoencoder (VAE), the resulting fixed-length codes roughly follow a Gaussian distribution. A novel 3D scene can be generated hierarchically by the decoder from a randomly sampled code from the learned distribution. We coin our method GRAINS, for Generative Recursive Autoencoders for INdoor Scenes. We demonstrate the capability of GRAINS to generate plausible and diverse 3D indoor scenes and compare with existing methods for 3D scene synthesis. We show applications of GRAINS including 3D scene modeling from 2D layouts, scene editing, and semantic scene segmentation via PointNet whose performance is boosted by the large quantity and variety of 3D scenes generated by our method.

Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions

Abstract: Deep weathering, residual material (colluvium) and random rainfall intensity are mainly responsible for landslides in tropical monsoon regions. These parameters are often not taken into consideration in a landslide susceptibility assessment. Sustainable resources development in this region requires information on the spatial distribution of areas susceptible to landslides. This study highlights various aspects of the landslides that take place on the west coast of India and a methodology developed for landslide susceptibility mapping.

Iron oxide nanorods as high-performance magnetic resonance imaging contrast agents.

Abstract: An efficient magnetic resonance imaging (MRI) contrast agent with a high R2 relaxivity value is achieved by controlling the shape of iron oxide to rod like morphology with a length of 30–70 nm and diameter of 4–12 nm. Fe3O4 nanorods of 70 nm length, encapsulated with polyethyleneimine show a very high R2 relaxivity value of 608 mM−1 s−1. The enhanced MRI contrast of nanorods is attributed to their higher surface area and anisotropic morphology. The higher surface area induces a stronger magnetic field perturbation over a larger volume more effectively for the outer sphere protons. The shape anisotropy contribution is understood by calculating the local magnetic field of nanorods and spherical nanoparticles under an applied magnetic field (3 Tesla). As compared to spherical geometry, the induced magnetic field of a rod is stronger and hence the stronger magnetic field over a large volume leads to a higher R2 relaxivity of nanorods.

Crystallisation kinetics in AO-Al2O3-SiO2-B2O3 glasses (A = Ba, Ca, Mg)

Abstract: The crystallisation kinetics of AO-Al2O3-SiO2-B2O3glasses (A = Ba, Ca, Mg) was investigated using DTA, XRD, and microstructural studies. Moreover, the influence of nucleating agentssuch as TiO2, ZrO2, Cr2O3, and Ni on MgO base glasses waselucidated. The glasses are of interest for the development ofsealants in Solid Oxide Fuel Cells (SOFC). The activation energy ofcrystal growth, E a, was evaluated for the different glassesusing the modified Kissinger equation. The preparation method of theglasses seems to determine whether surface or bulk nucleation is thedominant mechanism. The E a values vary between 330 and622 kJ/mol. The nucleating agents tend to enhance E a exceptZrO2. An increase of the Al2O3 concentration induces phaseseparation and decreases E a. The results are discussed onthe basis of the structural role and chemical properties of the Alions as well as with respect to the possible use of the glasses inSOFC.