Title of article :
Application of the Monte Carlo Method to Prediction of Dispersibility of Graphene in Various Solvents
Author/Authors :
Toropova، A. P. نويسنده IRCCS-Istituto di Ricerche Farmacologiche Mario Negri,Via LaMasa 19,Milano, Italy , , Toropov، A. A. نويسنده IRCCS-Istituto di Ricerche Farmacologiche Mario Negri,Via LaMasa 19,Milano, Italy , , Veselinovic، J. B. نويسنده University of Ni?, Faculty of Medicine, Department of Chemistry, Ni?, Serbia , , Veselinovic، A. M. نويسنده University of Ni?, Faculty of Medicine, Department of Chemistry, Ni?, Serbia , , Benfenati، E. نويسنده , , Leszczynska، D. نويسنده Interdisciplinary Nanotoxicity Center,Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch St, Jackson,MS 39217-0510, USA , , Leszczynski، J. نويسنده InterdisciplinaryNanotoxicityCenter, Department of Chemistry and Biochemistry, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17910, Jackson,MS 39217,USA ,
Issue Information :
فصلنامه با شماره پیاپی 36 سال 2015
Pages :
6
From page :
1211
To page :
1216
Abstract :
The dispersibility of graphene ismodeled as amathematical function of themolecular structure of solvent represented by simplified molecular input-line entry systems (SMILES) together with the graph of atomic orbitals (GAO). TheGAOismolecular graph where atomic orbitals e.g. 1s1, 2p4, 3d7 etc., are vertexes of the graph instead of the chemical elements used as the graph vertexes in the traditionally used molecular graph (hydrogen suppressed molecular graph or hydrogen filled molecular graph). The optimal descriptors calculated with theMonte Carlomethodwere used to build up one variable correlations "descriptor- dispersibility". The CORAL software is used as a tool to build up themodel. Based on the results of calculations the structural features which are promoters of increase or those which are promoters of decrease of the dispersibility are detected and discussed. The predictive potential of the used approach is checked up with three random and non identical splits of available data into the training, calibration, and validation (invisible during building up the model) sets. The statistics for external validation sets are the following: n=11, r2=0.6379, s=0.392 (split 1); n=8, r2=0.7308, s=0.378 (split 2); and n=5, r2=0.7797, s=0.504 (split 3).
Journal title :
International Journal of Environmental Research(IJER)
Serial Year :
2015
Journal title :
International Journal of Environmental Research(IJER)
Record number :
2388348
Link To Document :
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