Shiv Nadar University

About: Shiv Nadar University is a education organization based out in Dadri, Uttar Pradesh, India. It is known for research contribution in the topics: Population & Graphene. The organization has 1015 authors who have published 1924 publications receiving 18420 citations.

Synoptic weather conditions and aerosol episodes over Indo-Gangetic Plains, India

Abstract: The present study focuses on identifying the main atmospheric circulation characteristics associated with aerosol episodes (AEs) over Kanpur, India during the period 2001–2010. In this respect, mean sea level pressure (MSLP) and geopotential height of 700 hPa (Z700) data obtained from the NCEP/NCAR Reanalysis Project were used along with daily Terra-MODIS AOD550 data. The analysis identifies 277 AEs [AOD500 > $$ \overline{AOD} $$ 500 + 1STDEV (standard deviation)] over Kanpur corresponding to 13.2 % of the available AERONET dataset, which are seasonally distributed as 12.5, 9.1, 14.7 and 18.6 % for winter (Dec–Feb), pre-monsoon (Mar–May), monsoon (Jun–Sep) and post-monsoon (Oct–Nov), respectively. The post-monsoon and winter AEs are mostly related to anthropogenic emissions, in contrast to pre-monsoon and monsoon episodes when a significant component of dust is found. The multivariate statistical methods Factor and Cluster Analysis are applied on the dataset of the AEs days’ Z700 patterns over south Asia, to group them into discrete clusters. Six clusters are identified and for each of them the composite means for MSLP and Z700 as well as their anomalies from the mean 1981–2010 climatology are studied. Furthermore, the spatial distribution of Terra-MODIS AOD550 over Indian sub-continent is examined to identify aerosol hot-spot areas for each cluster, while the SPRINTARS model simulations reveal incapability in reproducing the large anthropogenic AOD, suggesting need of further improvement in model emission inventories. This work is the first performed over India aiming to analyze and group the atmospheric circulation patterns associated with AEs over Indo-Gangetic Plains and to explore the influence of meteorology on the accumulation of aerosols.

Graphene Supported Graphone/Graphane Bilayer Nanostructure Material for Spintronics

Abstract: We report an investigation into the magnetic and electronic properties of partially hydrogenated vertically aligned few layers graphene (FLG) synthesized by microwave plasma enhanced chemical vapor deposition. The FLG samples are hydrogenated at different substrate temperatures to alter the degree of hydrogenation and their depth profile. The unique morphology of the structure gives rise to a unique geometry in which graphane/graphone is supported by graphene layers in the bulk, which is very different from other widely studied structures such as one-dimensional nanoribbons. Synchrotron based x-ray absorption fine structure spectroscopy measurements have been used to investigate the electronic structure and the underlying hydrogenation mechanism responsible for the magnetic properties. While ferromagnetic interactions seem to be predominant, the presence of antiferromagnetic interaction was also observed. Free spins available via the conversion of sp2 to sp3 hybridized structures, and the possibility of unpaired electrons from defects induced upon hydrogenation are thought to be likely mechanisms for the observed ferromagnetic orders.

Tool condition monitoring by SVM classification of machined surface images in turning

Abstract: Tool condition monitoring has found its importance to meet the requirement of quality production in industries. Machined surface is directly affected by the extent of tool wear. Hence, by analyzing the machined surface, the information about the cutting tool condition can be obtained. This paper presents a novel technique for multi-classification of tool wear states using a kernel-based support vector machine (SVM) technique applied on the features extracted from the gray-level co-occurrence matrix (GLCM) of machined surface images. The tool conditions are classified into sharp, semi-dull, and dull tool states by using Gaussian and polynomial kernels. The proposed method is found to be cost-effective and reliable for online tool wear classification.