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RISS 인기검색어
- 검색키워드
- 저자 : Ramesh Rajendran (검색결과 5 건)
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Loganathan Krishnaraj,Niranjan Ramesh,Rajendran Senthil Kumar,Prem Kumar George 대한토목학회 2020 KSCE JOURNAL OF CIVIL ENGINEERING Vol.24 No.9
Pozzolan, a modified cementitious material, has a major impact on the construction industry. Among the different composite materials, fly ash blended cement has great part due to its abundance. In recent years, the pulverized fly ash has become the key interest among the researchers because of its improved physical properties. This makes it necessary to study the effect of fly ash blends with various chemical admixtures. This study primarily focuses on bringing the changes happening in the strength, retarding mechanism, setting time, mineralogy, and microstructure caused by the addition of Zinc Sulphate (ZnSO4) in concrete having various percentages of raw fly ash (RFA) and ultrafine fly ash (UFFA). The result shows that ZnSO4 increases the setting time by more than 60%. Also, the study indicates decreased strength in concrete because of the reduced formation of hydrated products; this phenomenon is confirmed by scanning electron microscope (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and X-ray fluorescence (XRF) analysis.
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Bioimaging of targeting cancers using aptamer-conjugated carbon nanodots
Lee, Chang Hyun,Rajendran, Ramesh,Jeong, Myoung-Seok,Ko, Hae Young,Joo, Jin Young,Cho, Sujeong,Chang, Young Wook,Kim, Soonhag The Royal Society of Chemistry 2013 Chemical communications Vol.49 No.58
<P>Using the excellent emission properties of carbon nanodots, we have developed an aptamer-conjugated imaging probe for targeting cancers.</P> <P>Graphic Abstract</P><P>Using the excellent emission properties of carbon nanodots, we have developed an aptamer-conjugated imaging probe for targeting cancers. <IMG SRC='http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=c3cc42752h'> </P>
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Thillaikkarasi Dhanapal,Karthikeyan Srinivasan,Ramesh Rajendran,Sengodan Prabhu,Kavitha Dhamodaran,Muthubalasubramanian Malarvizhi 한국탄소학회 2022 Carbon Letters Vol.32 No.6
An electrical double-layer capacitor is fabricated with biomass-derived activated carbon (AC) and multi-walled carbon nanotubes (MWCNTs), which are synthesized from Pongamia pinnata fruit shell and its seed oil, respectively. The activated carbon is produced by the chemical activation process at varying carbonization temperatures from 600 to 900 °C for 5 h at a rate of 10 min in an N2 atmosphere. The surface area of activated carbon and MWCNTs is 1170 m2 g−1 and 216 m2 g−1, respectively. The total pore volumes of activated carbon and MWCNTs are 1.51 cm3 g−1 and 0.5907 cm3 g−1, respectively. The as-prepared AC and MWCNTs are characterized by surface area analysis Brunner–Emmett–Teller method (BET), X-ray diffraction, X-ray photoelectron spectroscopy and Raman spectroscopic analysis, field emission scanning electron microscopy, high-resolution transmission electron microscopy and energy-dispersive X-ray spectroscopy. The electrochemical performances of AC-AC, MWCNTs-MWCNTs and AC-MWCNTs (25:75) symmetric electrodes are studied by cyclic voltammetry, galvanostatic charge–discharge and electrochemical impedance spectroscopy. The AC-MWCNTs (25:75) single electrode performance is also studied in two different electrolytes, such as 0.5 M Na2SO4 and 0.5 M H2SO4. The fabricated AC-MWCNTs (25:75) symmetric supercapacitor cell exhibits excellent electrochemical performance in 0.5 M Na2SO4. It shows a specific capacitance of 55.51 Fg−1, energy density 4.852 Wh Kg−1 and power density of 199.18 W Kg−1 at a current density of 1 Ag−1 in the voltage window of 0–1.8 V. The AC-AC and AC-MWCNTs (25:75) symmetric supercapacitor electrodes show outstanding performance.
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Carbon quantum dots supported ZnO sphere based photocatalyst for dye degradation application
Arun Velumani,Prabhu Sengodan,Priyadharsan Arumugam,Ramesh Rajendran,Sivakumar Santhanam,Maadeswaran Palanisamy 한국물리학회 2020 Current Applied Physics Vol.20 No.10
A Carbon quantum dots supported ZnO hollow Sphere (ZnO/C-dots) were synthesized through a solvothermal method using polyethylene glycol 400 (PEG 400) as a solvent. The phase and crystal structure of as-prepared ZnO/C-dots photocatalyst were characterized by powder X-ray diffraction (XRD). The surface morphology and size of the composite were analyzed using field emission scanning microscopy (FE-SEM). The optical properties of the as-prepared nanocomposites were examined using UV–visible (UV–Vis) spectrometer. The photocatalytic activity of pure ZnO and ZnO/C-dots nanocomposites were evaluated by the degradation of methylene blue (MB) under UV–Visible light irradiation. The ZnO/C-dots nanocomposites exhibited maximum photocatalytic MB dye degradation efficiency of 96% which is much higher that the pure ZnO (63%). The enhanced photocatalytic activity of ZnO/C-dots is due to the extended light absorption in the visible region and suppressed photoexcited electron-hole pair recombination rate. Moreover, the activity of photocatalyst after five cycles exhibits high stability, which is vital for the sustainable photocatalytic procedures. It is concluded that the prepared ZnO/Cdots composite have low cost, good stability and has a great potential application for Photocatalytic dye degradation.
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An Explainable Deep Learning Approach for Oral Cancer Detection
Babu P. Ashok,Rai Anjani Kumar,Ramesh Janjhyam Venkata Naga,Nithyasri A.,Sangeetha S.,Kshirsagar Pravin R.,Rajendran A.,Rajaram A.,Dilipkumar S. 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.3
With a high death rate, oral cancer is a major worldwide health problem, particularly in low- and middle-income nations. Timely detection and diagnosis are crucial for efective prevention and treatment. To address this challenge, there is a growing need for automated detection systems to aid healthcare professionals. Regular dental examinations play a vital role in early detection. Transfer learning, which leverages knowledge from related domains, can enhance performance in target categories. This study presents a unique approach to the early detection and diagnosis of oral cancer that makes use of the exceptional sensory capabilities of the mouth. Deep neural networks, particularly those based on automated systems, are employed to identify intricate patterns associated with the disease. By combining various transfer learning approaches and conducting comparative analyses, an optimal learning rate is achieved. The categorization analysis of the reference results is presented in detail. Our preliminary fndings demonstrate that deep learning efectively addresses this challenging problem, with the Inception-V3 algorithm exhibiting superior accuracy compared to other algorithms.
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