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1
Biosynthesis of semiconductor nanoparticles by using sulfur reducing bacteria Serratia nematodiphila

Malarkodi, C.,Rajeshkumar, S.,Paulkumar, K.,Jobitha, G. Gnana,Vanaja, M.,Annadurai, G. Techno-Press 2013 Advances in nano research Vol.1 No.2
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The synthesis of semiconductor nanoparticles is a growing research area due to the prospective applications for the development of novel technologies. In this paper we have reported the biosynthesis of Cadmium sulfide nanoparticles (CdSNPs) by reduction of cadmium sulphate solution, using the bacteria of Serratia nematodiphila. The process for the synthesis of CdS nanoparticles is fast, novel and ecofriently. Formation of the CdS nanoparticles was confirmed by surface Plasmon spectra using UV-Vis spectrophotometer and absorbance strong peak at 420 nm. The morphology of crystalline phase of nanoparticles was determined from Scanning Electron Microscopy (SEM), Energy Dispersive X-ray spectroscopy and X-ray diffraction (XRD) spectra. The average size of CdS nanoparticles was in the range of 12 nm and the observed morphology was spherical. The results indicated that the proteins, which contain amine groups, played a reducing and controlling responsibility during the formation of CdS nanoparticles in the colloidal solution. Antibacterial activity against some bacteria such as Bacillus subtilis, Klebsiella planticola. CdS nanoparticles exhibiting good bactericidal activity.
2
Superior one-pot synthesis of a doped graphene oxide electrode for a high power density supercapacitor

Duraivel, Malarkodi,Nagappan, Saravanan,Balamuralitharan, B.,Selvam, S.,Karthick, S. N.,Prabakar, K.,Ha, Chang-Sik,Kim, Hee-Je The Royal Society of Chemistry 2018 NEW JOURNAL OF CHEMISTRY Vol.42 No.13
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In this work, we report the synthesis of sulfur-doped reduced graphene oxide (S-rGO) using sodium borohydride (NaBH4) and sodium sulfide (Na2S) by a facile one-pot approach via refluxing in deionised water. The undoped rGO chemical structure is partially reduced by NaBH4, whereas the addition of a small amount of Na2S with NaBH4 resulted in a better reduction of rGO. XPS analysis confirmed the successful doping of sulfur and Raman spectroscopy verified the increased defect density. The S-rGO electrode exhibits good power density (3202 W kg−1) with increased specific capacitance (392 F g−1) and cyclic stability (91%, 2000 cycles) in 1 M Na2SO4 aqueous electrolyte. The obtained results suggest that the simple tuning of the graphene oxide structure using Na2S with NaBH4 enhances the above properties. Moreover, this method is facile and allows the easy reproduction of bulk quantities of materials for use in commercial applications.
3
Multi Label Deep Learning classification approach for False Data Injection Attacks in Smart Grid

( Prasanna Srinivasan. V ),( Balasubadra. K ),( Saravanan. K ),( Arjun. V. S ),( Malarkodi. S ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.6
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  ScienceON
  
  KISS
The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.