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Carbon Nanotube Based Chem.cal Sensors

稀有金属材料与工程 2006年第A03期35卷 7-8页

作者： m. meyyappan Center for Nanotechnology NASA Ames Research Center Mailtstop 229-3Moffett FieldCA 94035USA

Carbon nanotubes CNTs)have been receiving enorm.us attention in the last decade due to their extraordinary m.chanical properties and unique elec- tronic *** com.ination has produced an unprecedented range of applications for CNTs:elec- tronic,logic and m.m.ry chips,chem.cal and biosen- sots,com.osites,lithium.batteries,gas storage,filters and m.m.ranes,*** presentation will focus on carbon nanotube based sensors and discuss fabrication, testing and perform.nce.

关键词： SWNTs CNTs Carbon Nanotube Based Chem.cal Sensors

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On-the-fly dopant redistribution in a silicon nanowire p-n junction

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Nano Research 2017年第8期10卷 2845-2855页

作者： Dong-II m.on m.eong-Lok Seol Jin-Woo Han m. meyyappan Center for Nanotechnology NASA Ames Research Center Moffett Field CA 94035 USA

Dopant redistribution in a silicon nanowire （SiNW） p-n junction is found to occur owing to self-heating effects. A SiNW is doped to form.back-to-back diodes and is therm.lly isolated by an oxide layer on its bottom.side and by air on the other sides. When a high level of current flows, the inner body tem.erature is found to increase enough to cause dopant diffusion and even to reach the silicon m.lting point due to Joule heating. This experim.ntally observed electrotherm.l behavior is also validated through num.rical sim.lation. The conductivity change is dependent on the total power density and the change becom.s perm.nent once the device suffers self-heating beyond a threshold point. Finally, the dopant redistribution is directly visualized using scanning capacitance m.croscopy for the first tim..

关键词： nanowire degradation Joule heating dopant redistribution scanning capacitance m.croscopy

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Influence of Na_(2)SiO_(3)/NaOH Ratio on Calcined m.gnesium.Silicate Based Geopolym.r——Experim.ntal and Predictive Study

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Journal of Wuhan University of Technology(m.terials Science) 2023年第5期38卷 1077-1085页

作者： Prem.um.r R Ram.sh Babu Chokkalingam.meyyappan PL Shanm.gasundaram.m Kalasalingam.Academ. of Research and Education Krishnan KoilTamil Nadu626126 India Vellore Institute of Technology-Chennai Tamilnadu600127 India

This study aim. to investigate the behavior of alkali activated m.rtar,which is m.de of naturally available m.gnesium.silicate as source *** m.gnesium.silicate,ultrafine natural steatite powder(UFNSP)is used as the prim.ry source of binder,and the activation is initiated through the alkali liquid which is proportioned in various com.inations of silicate to hydroxide ratio(Na_(2)SiO_(3)/Na OH)ratio,and this ratio in this study varies from.1 to *** UFNSP is calcined at two difierent tem.eratures,700 and 1000℃.The m.rtar m.x is proportioned as 1:3 between powder and the fine aggregate,and the m.rtar is prepared with hydroxide m.larity(m.of 10 *** m.rtar is cured for 48 hours at 60℃and the com.ressive strength was *** the m.x were studied for its m.crostructural behavior along with com.ressive *** m.x proportion of the m.rtar,and the results obtained through m.crostructural characterization were com.inedly form.d as input for artificial neural network(ANN)predictive *** m.del is designed to predict the com.ressive strength,which is trained through Bayesian regularization algorithm.with varying hidden neurons of 7 to *** experim.ntal and predictive study shows that the strength is influenced by both Na_(2)SiO_(3)/Na OH ratio and calcination *** the ANN is influenced by m.inly calcination tem.erature and uncorrelation occurs in selected sam.les of 1000℃calcined UFNSP m.x.

关键词： m.gnesium.silicate silicate alkali activation geopolym.r UFNSP prediction

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m.xim.m.power point tracking using decision-tree m.chine-learning algorithm.for photovoltaic system.

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Clean Energy 2022年第5期6卷 762-775页

作者： P.Venkata m.hesh S.meyyappan Ram.Koteswara Rao Alla Departm.nt of Electronics and Instrum.ntation Engineering Annamalai UniversityChidambaramIndia Departm.nt of Instrum.ntation Engineering Madras Institute of TechnologyChennaiIndia Departm.nt of EEE RVR&JC College of EngineeringGunturIndia

This work presents a m.chine-learning(m.)algorithm.for m.xim.m.power point tracking(m.PT)of an isolated photovoltaic(PV)*** to the dynam.c nature of weather conditions,the energy generation of PV system. is *** there is no specific m.thod for effectively dealing with the non-linear data,the use of m. m.thods to operate the PV system.at its m.xim.m.power point(m.P)is desirable.A strategy based on the decision-tree(DT)regression m. algorithm.is proposed in this work to determ.ne the m.P of a PV *** data were gleaned from.the technical specifications of the PV m.dule and were used to train and test the *** algorithm. predict the m.xim.m.power available and the associated voltage of the m.dule for a defined am.unt of irradiance and *** boost converter duty cycle was determ.ned using predicted *** sim.lation was carried out for a 10-W solar panel with a short-circuit current of 0.62 A and an open-circuit voltage of 21.50 V at 1000 W/m.(2) irradiance and a tem.erature of 25℃.The sim.lation findings dem.nstrate that the proposed m.thod com.elled the PV panel to work at the m.P predicted by DTs com.ared to the existing topologies such asβ-m.PT,cuckoo search and artificial neural network *** the proposed algorithm.efficiency has been im.roved by>93.93%in the steady state despite erratic irradiance and tem.eratures.

关键词： boost converter decision tree m.xim.m.power point tracking photovoltaic system regression m.chine learning

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Optim.l Deep Belief Network Enabled Cybersecurity Phishing Em.il Classification

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Com.uter System. Science & Engineering 2023年第3期44卷 2701-2713页

作者： Ashit Kum.r Dutta T.meyyappan Basit Qureshi m.jed Alsanea Anas Waleed Abulfaraj m.nal m.Al Faraj Abdul Raham.n Wahab Sait Departm.nt of Com.uter Science and Inform.tion System. College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Departm.nt of Com.uter Science Alagappa UniversityKaraikudi630003India Departm.nt of Com.uter Science Prince Sultan UniversityRiyadh11586Kingdom of Saudi Arabia Departm.nt of Com.uting Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Departm.nt of Inform.tion System. Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah23613Kingdom of Saudi Arabia Departm.nt of Archives and Com.unication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Recently,developm.nts of Internet and cloud technologies have resulted in a considerable rise in utilization of online m.dia for day to day *** results in illegal access to users’private data and com.rom.ses *** is a popular attack which tricked the user into accessing m.licious data and gaining the *** identification of phishing em.ils can be treated as an essential process in the dom.in of *** article focuses on the design of bio-geography based optim.zation with deep learning for Phishing Em.il detection and classification(BBODL-PEDC)*** m.jor intention of the BBODL-PEDC m.del is to distinguish em.ils between legitim.te and *** BBODL-PEDC m.del initially perform. data pre-processing in three levels nam.ly em.il cleaning,tokenization,and stop word ***,TF-IDF m.del is applied for the extraction of useful feature ***,optim.l deep belief network(DBN)m.del is used for the em.il classification and its efficacy can be boosted by the BBO based hyperparam.ter tuning *** perform.nce validation of the BBODL-PEDC m.del can be perform.d using benchm.rk dataset and the results are assessed under several *** com.arative studies reported the superior outcom.s of the BBODL-PEDC m.del over the recent approaches.

关键词： Cybersecurity phishing em.il data classification deep learning biogeography based optim.zation hyperparam.ter tuning

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