Structure - property relationships for protic ionic liquids and their mixtures

Yalcin, D 2019, Structure - property relationships for protic ionic liquids and their mixtures, Doctor of Philosophy (PhD), Science, RMIT University.


Document type: Thesis
Collection: Theses

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Title Structure - property relationships for protic ionic liquids and their mixtures
Author(s) Yalcin, D
Year 2019
Abstract Ionic liquids (ILs) refer to solvent systems which are liquid below 100 °C, and consist mostly of cations and anions. Due to a number of desirable properties such as low vapour pressure, low melting point, high conductivity and thermal stability, ILs have been increasingly used in a variety of applications including as reaction media for chemical and bio-catalysis, as electrolytes for fuel cells or sensors, as lubricants, analytical and heat storage agents, as well as in organic synthesis and separation processes. ILs are highly tailorable, and their properties can be finely tuned through modifications to the cation and/or anion structure. As an interesting subclass, protic ionic liquids (PILs) have some distinguishing features such as generally being cheaper and easier to synthesize, able to form H-bonds and typically having lower viscosities compared to aprotic ILs. Although there are a huge number of possible combinations of cations and anion resulting in ILs, the viscosity and cost of many ILs imposes a significant limitation on their use for certain applications, which can be overcome through mixing ILs with other solvents. However, this results in adding another dimension to the compositional space as well as increasing the complexity in molecular and ionic interactions.

In this work, a neat PIL library including primary and secondary alkylammonium cations combined with carboxylate (formates and acetates) and nitrate anions has been first selected. The overall goal of the research was to systematically investigate the effects of solvent non-stoichiometry and the presence of water, as well as the structural modifications on key solvent properties and hence, to develop structure-property relationships for these multi-component PIL containing solvent systems. Novel high-throughput methodologies have been utilised to fast-track solvent preparation and system understanding.

Surface tension, apparent pH and liquid nanostructure have been determined to identify the potential self-assembly ability of the multi-component PIL containing solvent systems. Through the use of machine learning algorithms, the vast number of systematic data led to the establishment of design rules for the potential amphiphile self-assembly promoting ability of PIL containing solvents. These findings were then linked to the experimentally determined lyotropic liquid crystal phase behavior of a well-known surfactant, CTAB.

Furthermore, for the first time, the solvation capacities of a selection of neat PILs and their binary mixtures with selected molecular solvents have been studied utilizing spectroscopic techniques. The effect of PIL structure, molecular solvent type, and PIL-solvent ratio on the solvation capability of PIL based solvents were comparatively investigated, quantified and discussed in terms of the specific and non-specific interactions between PIL-solvent, PIL-solute and solvent-solute species.

In summary, this research has shown that the solvent properties of neat PILs and/or multi-component PIL containing solutions can be easily and effectively tuned and designed for targeted applications via the help of high-throughput strategies.
Degree Doctor of Philosophy (PhD)
Institution RMIT University
School, Department or Centre Science
Subjects Colloid and Surface Chemistry
Keyword(s) Ionic liquids
High-throughput
Machine learning
Self-assembly
Solvation
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Created: Tue, 24 Mar 2020, 09:32:25 EST by Keely Chapman
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