Institution
Motilal Nehru National Institute of Technology Allahabad
Education•Allahabad, Uttar Pradesh, India•
About: Motilal Nehru National Institute of Technology Allahabad is a education organization based out in Allahabad, Uttar Pradesh, India. It is known for research contribution in the topics: Control theory & Electric power system. The organization has 2475 authors who have published 5067 publications receiving 61891 citations. The organization is also known as: NIT Allahabad & Motilal Nehru Regional Engineering College.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the development of Al/SiCp (10% weight) metal matrix composite through a conventional casting process and studied its machinability characteristics in turning using multilayer TiN coated carbide insert under dry environment based on Taguchi's L 9 orthogonal array.
85 citations
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TL;DR: This study helps to reduction the cost of adsorbents due to the large availability of marine waste (CS) and thus aims to reduce the anthropogenic CO2 gas at low cost, favourable temperature and pressure as compared to previously reported.
84 citations
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TL;DR: In this article, the first study on synthesis of water-soluble reduced fluorescent carbon dots (r-FCDs) by using lignosulfonate lignin as carbon source was presented.
84 citations
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TL;DR: In this paper, an artificial neural network (ANN) based methodology is developed to identify unknown groundwater pollution sources in terms of these source characteristics for such a missing data scenario, when concentration measurement data over an initial length of time is not available.
Abstract: Groundwater pollution sources are characterized by spatially and temporally varying source locations, injection rates, and duration of activity. Concentration measurement data at specified observation locations are generally utilized to identify these sources characteristics. Identification of unknown groundwater pollution sources in terms of these source characteristics becomes more difficult in the absence of complete breakthrough curves of concentration history at all the time steps. If concentration observations are missing over a length of time after an unknown source has become active, it is even more difficult to correctly identify the unknown sources. An artificial neural network (ANN) based methodology is developed to identify these source characteristics for such a missing data scenario, when concentration measurement data over an initial length of time is not available. The source characteristics and the corresponding concentration measurements at time steps for which it is not missing, constitute a pattern for training the ANN. A groundwater flow and transport numerical simulation model is utilized to generate the necessary patterns for training the ANN. Performance evaluation results show that the back-propagation based ANN model is essentially capable of extracting hidden relationship between patterns of available concentration measurement values, and the corresponding sources characteristics, resulting in identification of unknown groundwater pollution sources. The performance of the methodology is also evaluated for different levels of noise (or measurement errors) in concentration measurement data at available time steps.
84 citations
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TL;DR: In this article, a simple off-the-shelf chemical 6,7-dihydroxycoumarin (1) based copper complex (1·Cu2+) has been used for the selective detection of toxic cyanide in aqueous medium.
Abstract: The simple off-the-shelf chemical 6,7-dihydroxycoumarin (1) based copper complex (1·Cu2+) has been used for the selective detection of toxic cyanide in aqueous medium. The DFT calculation confirms the binding behavior between 1 and Cu2+ (2:1) and the red shift in the UV–vis spectrum with copper ion was confirmed by the decrease in energy between HOMO–LUMO band gaps. The cyanide sensing in water was confirmed by both absorption and emission spectral studies. Cyanide ion showed 13-fold increments in fluorescent intensity in emission spectrum via displacement of copper from 1·Cu2+. The limit of detection of CN– in water is 5.77 μM; 1·Cu2+ also applicable for the detection of cyanide in fresh mouse serum with detection limit of 14.4 μM. The cell images showed that 1·Cu2+ could be used to detect intracellular CN–.
84 citations
Authors
Showing all 2547 results
Name | H-index | Papers | Citations |
---|---|---|---|
Santosh Kumar | 80 | 1196 | 29391 |
Anoop Misra | 70 | 385 | 17301 |
Naresh Kumar | 66 | 1106 | 20786 |
Munindar P. Singh | 62 | 580 | 20279 |
Arvind Agarwal | 58 | 325 | 12365 |
Mahendra Kumar | 54 | 216 | 9170 |
Jay Singh | 51 | 301 | 8655 |
Lalit Kumar | 47 | 381 | 11014 |
O.N. Srivastava | 47 | 548 | 10308 |
Avinash C. Pandey | 45 | 301 | 7576 |
Sunil Gupta | 43 | 518 | 8827 |
Rakesh Mishra | 41 | 545 | 7385 |
Durgesh Kumar Tripathi | 37 | 133 | 5937 |
Vandana Singh | 35 | 190 | 4347 |
Prashant K. Sharma | 34 | 174 | 3662 |