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( Saravanan ),( Venkatakrishnan Sivaraj ),( Jabez Osborne ),( Munusamy Madhaiyan ),( Lazar Mathew ),( Jong Bae Chung ),( Ki Sup Ahn ),( Tong Min Sa ) 한국미생물생명공학회 2007 Journal of microbiology and biotechnology Vol.17 No.9
Saravanan, Chokalingam,Chitumalla, Ramesh Kumar,Ashwin, Bosco Christin Maria Arputham,Senthilkumaran, Marimuthu,Suresh, Palaniswamy,Jang, Joonkyung,Muthu Mareeswaran, Paulpandian Elsevier 2018 Journal of luminescence Vol.196 No.-
<P><B>Abstract</B></P> <P>The host-guest interaction of gallic acid (GA) with p-sulfonatocalixarene (p-SC4) is studied using emission and excited state lifetime techniques. The quenching effect on the emission intensity and excited state lifetime is observed upon binding. The impact of oxidation potential upon binding is studied using cyclic voltammetric technique. The structural features and the mode of binding of GA with p-SC4 is examined using <SUP>1</SUP>H NMR and rotating frame overhauser effect spectroscopy (ROESY) techniques. The binding of GA with p-SC4 has also been examined by means of density functional theory simulations. The calculated interaction energy of GA with p-SC4 (22.15kcal/mol) indicates the strong binding nature.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Formation of stable complex between GA and p-SC4 is studied in solution state. </LI> <LI> Quenching on emission intensity and lifetime of GA is observed upon addition of p-SC4. </LI> <LI> The downward potential shift is detected upon addition of GA on p-SC4 and vice versa. </LI> <LI> The <SUP>1</SUP>H NMR and ROESY spectral studies reveals that the GA is included into p-SC4. </LI> <LI> The theoretical simulations explains the intermolecular hydrogen bonding interactions between the GA and the p-SC4. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
The magnitude of wear should be at a minimum for numerous automobile and aeronautical components. In the current work, composites were prepared by varying the cenosphere content using the conventional stir casting method. A uniform distribution of particles was ensured with the help of scanning electron microscopy (SEM). Three major parameters were chosen from various factors that affect the wear. A wear test was conducted with a pin-on-disc apparatus; the controlling parameters were volume percentages of reinforcement of 5, 10, 15, and 20%, applied loads of 9.8, 29.42, and 49.03 N, and sliding speeds of 1.26, 2.51, and 3.77 m/s. The design of the experiments (DOE) was performed by varying the different influencing parameters using the full factorial method. An analysis of variance (ANOVA) was used to analyze the effects of the parameters on the wear rate. Using regression analysis, a response curve was obtained based on the experimental results. The parameters in the resulting curve were optimized using the Genetic Algorithm (GA). The GA results were compared with those of an alternate efficient algorithm called Neural Networks (NNs).