Prediction of Viscosity and Ionic Conductivity of Imidazolium-Based Ionic Liquids at Different Temperatures Using a Quantitative Structure-Property Relationship Approach

KOI, ZI KANG (2021) Prediction of Viscosity and Ionic Conductivity of Imidazolium-Based Ionic Liquids at Different Temperatures Using a Quantitative Structure-Property Relationship Approach. Masters thesis, Universiti Teknologi PETRONAS.

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Abstract

Ionic liquids (ILs) have been widely explored in various industrial processes and applications because of their remarkable features besides being regarded as promising and environmentally friendly solvents. Nevertheless, it requires a huge effort to find the proper combinations of cations and anions in formulating ILs with desired properties. Thus, the development of a simple and accurate model to predict the physicochemical properties of ILs is of high interest. Notably, both viscosity and ionic conductivity are considered crucial physical properties which affect the transport capabilities of ILs and remain as one of the barriers in the design of suitable ILs. In the present study, the viscosity and ionic conductivity of different ILs have been estimated and correlated via Quantitative Structure-Property Relationship (QSPR) approach with a set of descriptors, namely interaction energies of the ILs cation–anion pairs generated by the Conductor-like Screening Model for Real Solvents (COSMO-RS). The experimental data of imidazolium-based ionic liquids collected from literatures with viscosity in the range of 5.3-6410 mPa·s at temperatures in the range of 273.15-393.15 K were used to develop the viscosity model whereas the conductivity model was established using experimental conductivity data in the range of 0.008-5.1 S·m-1 at temperatures in the range of 268.15-398.15 K. The proposed QSPR viscosity model using Multiple Linear Regression (MLR) algorithm produced a low average absolute relative deviation (AARD) and a high coefficient of determination (R2) suggesting that the proposed model fits well with the available training set data and can be further validated with larger data set. For prediction of ionic conductivity, a non-linear regression approach results in a more superior accuracy, as compared to MLR. The stepwise approach suggests that both viscosity and conductivity are highly affected by van der Waals forces and temperature, followed by electrostatic energy and hydrogen-bonding energy. The results from this work will aid in the screening process of suitable ILs with desired viscosity and ionic conductivity for specific applications.

Item Type: Thesis (Masters)
Subjects: T Technology > TP Chemical technology
Departments / MOR / COE: Engineering > Chemical
Depositing User: Mr Ahmad Suhairi Mohamed Lazim
Date Deposited: 27 Feb 2022 04:32
Last Modified: 27 Feb 2022 04:32
URI: http://utpedia.utp.edu.my/id/eprint/22767

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