The COVID-19 pandemic highlighted the need to create and study new substances with improved lipophilicity and antimicrobial properties, such as ionic liquids (ILs), with easily tunable physicochemical properties. Most ILs possess strong antibacterial effects, but ILs containing the imidazolium cation are even more effective than the positive control. Thus, in this study, three ionic liquids with 1-butyl-3-methylimidazolium cation and various carboxylate anions (phenylacetate, benzoate, and 4-methoxyphenylacetate) were synthesized and fully characterized. The interactions between the cations and anions were discussed based on the experimental density, viscosity, and electrical conductivity. From the measured electrical conductivity and viscosity, the Walden plot is constructed and ionicity of the studied ILs is discussed. The similarities and dissimilarities among the studied ILs and their physicochemical properties are analyzed by applying the hierarchical cluster analysis and in silico calculated properties. The antimicrobial activity of the studied ionic liquids is tested on two bacterial (E. coli and P. aeruginosa) and three fungi (P. verrucosum, A. flavus, and A. parasiticus) strains, finding that they showed improved antimicrobial activity compared to the individual components.

Designing ionic liquids (ILs) where a single functional group orchestrates a suite of enhanced properties remains a key challenge in materials science. Here, we introduce 1-butyl-3-methylimidazolium mandelate, [Bmim][Man], a novel IL where the hydroxyl group on the mandelate anion simultaneously enhances hydrogen bonding, thermal stability, antimicrobial activity, and extraction selectivity. The structure-property relationships of [Bmim][Man] were investigated through measurements of density, viscosity, and conductivity and were compared with analogous ILs. The presence of the hydroxyl group on the mandelate anion resulted in the highest density and viscosity among the series, attributed to strong hydrogen bonding and efficient ion packing. Notably, [Bmim][Man] exhibited a high molar conductivity that decouples from its high viscosity, suggesting an unusual degree of ion dissociation facilitated by the hydroxyl group. Thermogravimetric analysis revealed superior thermal stability. Furthermore, the investigated ionic liquid demonstrated a low critical aggregation concentration (CAC = 0.01982 mol·dm⁻³) in water, indicating a strong propensity for self-aggregation. [Bmim][Man] showed synergistic, enhanced antibacterial activity against E. coli and P. aeruginosa. Finally, the functional utility of this designed liquid was demonstrated in separation science, where [Bmim][Man]-based aqueous biphasic systems showed selective extraction capabilities for transition metals, a process driven by the same hydrogen-bonding and coordination interactions that define its bulk properties. These findings establish [Bmim][Man] as a promising multifunctional material where the mandelate anion concurrently dictates liquid microstructure, thermal resilience, antimicrobial performance, and application in extraction.

The rational design of ionic liquids (ILs) is often hindered when promising candidates, such as carboxy-functionalized imidazolium chlorides, exhibit properties like extreme viscosity that preclude direct experimental measurement. In this study, we synthesized a series of these ILs and addressed this “experimental gap” with a combined computational strategy. For the few liquids accessible to measurement, we obtained density, viscosity, and conductivity data. For the majority, we turned to atomistic modeling and machine learning. Symmetry-adapted perturbation theory (SAPT2) energy decomposition uncovered the dominance of electrostatic interactions in governing viscosity, an insight obscured by total binding energies from DFT. In addition, a recently developed machine learning model, named IonIL-IM-D1, predicted the density of [C₂COOHeim][Cl] with an error of less than 1% upon validation, though experimental verification for the solid candidates was not possible. This predictive framework was extended to propose and evaluate new IL candidates, offering a complementary strategy for exploring macroscopic behavior when direct experimental measurements are not feasible.