resumo
Ionic liquids (ILs) have attracted great attention, from both industry and academia, as alternative fluids for very different types of applications. The large number of cations and anions allow a wide range of physical and chemical characteristics to be designed. However, the exhaustive measurement of all these systems is impractical, thus requiring the use of a predictive model for their study. In this work, the predictive capability of the conductor-like screening model for real solvents (COSMO-RS), a model based on unimolecular quantum chemistry calculations, was evaluated for the prediction water activity coefficient at infinite dilution, gamma(infinity)(w), in several classes of ILs. A critical evaluation of the experimental and predicted data using COSMO-RS was carried out. The global average relative deviation was found to be 27.2%, indicating that the model presents a satisfactory prediction ability to estimate gamma(infinity)(w) in a broad range of ILs. The results also showed that the basicity of the ILs anions plays an important role in their interaction with water, and it considerably determines the enthalpic behavior of the binary mixtures composed by Its and water. Concerning the cation effect, it is possible to state that generally gamma(infinity)(w) increases with the cation size, but it is shown that the cation-anion interaction strength is also important and is strongly correlated to the anion ability to interact with water. The results here reported are relevant in the understanding of ILs-water interactions and the impact of the various structural features of its on the gamma(infinity)(w) as these allow the development of guidelines for the choice of the most suitable lLs with enhanced interaction with water.
palavras-chave
ORGANIC SOLUTES; COSMO-RS; MUTUAL SOLUBILITIES; BINARY-MIXTURES; QSPR ANALYSIS; THIOCYANATE; EXTRACTION; ALCOHOLS; ETHANOL; SYSTEMS
categoria
Engineering
autores
Kurnia, KA; Pinho, SP; Coutinho, JAP
nossos autores
Kiki Adi Kurnia
Bolseiro de pós-DoutoramentoGrupos
G4 - Materiais Renováveis e Economia Circular
G6 - Materiais Virtuais e Inteligência Artificial
agradecimentos
This work was financed by national funding from Fundacao para a Ciencia e a Tecnologia (FCT), through the projects PTDC/QUI-QUI/121520/2010, Pest-CTM/LA0011, and LSRE/LCM (project PEST-C/EQB/LA0020/2013). Kiki A. Kurnia acknowledges FCT for the postdoctoral grants SFRH/BPD/88101/2012.