Abstract
Background: The prediction of physicochemical properties is important task of the natural sciences. Quantitative structure – property relationships (QSPR) are a tool to solve the task.
Objective: QSPR for dispersibility of graphene in various organic solvents has been built up by means of the CORAL software (http://www.insilico.eu/coral).
Method: The Monte Carlo technique is the basis of the models for dispersibility of graphene in various organic solvents. Simplified molecular input-line entry systems (SMILES) are used to represent the molecular structure for the QSPR analysis. In other words, the graphene dispersibility is modeled as a mathematical function of the molecular structure.
Results: The statistical characteristics of the models are quite good. They have the mechanistic interpretation: the structural features of molecules of solvents which are promoters of increase or decrease of graphene dispersibility have been discovered.
Conclusion: The suggested approach can be used to predict dispersibility of graphene in various organic solvents.
Keywords: QSPR, monte carlo method, graphene dispesibility, CORAL software.
Letters in Drug Design & Discovery
Title:QSPR Model for Dispersibility of Graphene in Various Solvents
Volume: 13 Issue: 6
Author(s): Alla P. Toropova and Andrey A. Toropov
Affiliation:
Keywords: QSPR, monte carlo method, graphene dispesibility, CORAL software.
Abstract: Background: The prediction of physicochemical properties is important task of the natural sciences. Quantitative structure – property relationships (QSPR) are a tool to solve the task.
Objective: QSPR for dispersibility of graphene in various organic solvents has been built up by means of the CORAL software (http://www.insilico.eu/coral).
Method: The Monte Carlo technique is the basis of the models for dispersibility of graphene in various organic solvents. Simplified molecular input-line entry systems (SMILES) are used to represent the molecular structure for the QSPR analysis. In other words, the graphene dispersibility is modeled as a mathematical function of the molecular structure.
Results: The statistical characteristics of the models are quite good. They have the mechanistic interpretation: the structural features of molecules of solvents which are promoters of increase or decrease of graphene dispersibility have been discovered.
Conclusion: The suggested approach can be used to predict dispersibility of graphene in various organic solvents.
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Cite this article as:
P. Toropova Alla and A. Toropov Andrey, QSPR Model for Dispersibility of Graphene in Various Solvents, Letters in Drug Design & Discovery 2016; 13 (6) . https://dx.doi.org/10.2174/1570180812666151022221942
DOI https://dx.doi.org/10.2174/1570180812666151022221942 |
Print ISSN 1570-1808 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-628X |
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