Key research themes
1. How do molecular interactions and microstructural heterogeneities determine the ionicity and dynamical behavior of ionic liquids?
This research theme investigates the fundamental molecular-scale origins of ionicity in ionic liquids (ILs) by analyzing the nature of interionic interactions (such as Coulombic forces, van der Waals forces, hydrogen bonding), and the resulting heterogeneous microstructures and dynamics. Understanding these aspects is crucial because ionicity profoundly influences transport properties, conductivity, viscosity, and application performance of ILs. The interplay between strong electrostatic and weaker directional forces shapes the liquid morphology, impacting ion pairing, aggregation, and ionic mobility.
2. What is the effect of molecular solvents, water, and cosolvents on the ionicity and interionic interactions in ionic liquid mixtures?
This theme addresses how varying the composition of IL mixtures with molecular solvents such as water, alcohols, or organic co-solvents perturbs the ionicity and ion-ion interactions in IL systems. Understanding solvent effects is critical because water and molecular solutes affect the equilibrium ion pairing, hydrogen bonding networks, microstructure, dynamics, and therefore the macroscopic properties like viscosity, conductivity, and CO2 capture ability. Elucidating solvent-dependent modulations enables optimized design for specific applications like separations and electrochemistry.
3. How can predictive modeling improve understanding and prediction of ionic liquid physicochemical properties related to ionicity, such as density and viscosity?
This theme focuses on developing and applying quantitative models and computational approaches to predict IL macroscopic physical properties that directly link to ionic organization and ionicity. Accurate modeling of density, viscosity, and related transport properties enables systematic tuning of ILs for applications without the need for exhaustive empirical measurements. It encompasses group contribution methods, quantitative structure-property relationships (QSPR), computational chemistry (COSMO-RS), and machine learning techniques.