Predicting density and elucidating the thermodynamic drivers of viscosity in carboxy-functionalized imidazolium ionic liquids

Cáko Bagány, Nikolett (avtor), Armaković, Sanja J. (avtor), Armaković, Stevan (avtor), Šarac, Bojan (avtor), Belić, Sanja (avtor), Cerc Korošec, Romana (avtor), Bešter-Rogač, Marija (avtor), Gadžurić, Slobodan (avtor)

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 [C$_2$COOHeim][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.

• Jezik: Angleški jezik
• Ključne besede: carboxy-functionalized imidazolium ion, idensity functional theory, symmetry-adapted perturbation theory, machine learning, ion–ion interactions, ionic liquid smart design
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FKKT - Fakulteta za kemijo in kemijsko tehnologijo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: 17 str.
• Številčenje: Vol. 31, iss. 14, art. 2495
• PID: 20.500.12556/RUL-185518
• UDK: 544.6.018.4
• ISSN pri članku: 1420-3049
• DOI: 10.3390/molecules31142495
• COBISS.SI-ID: 287193091
• Datum objave v RUL: 07.08.2026

Gradivo je del revije

• Naslov: Molecules
• Skrajšan naslov: Molecules
• Založnik: MDPI
• ISSN: 1420-3049
• COBISS.SI-ID: 18462981

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: karboksi-funkcionaliziran imidazolijev ion, teorija gostotnega funkcionala, simetriji prilagojena perturbacijska teorija, strojno učenje, interakcije med ioni, načrtovanje ionskih tekočin s ciljnimi lastnostmi