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Synthesis of High Purity Nonsymmetric Dialkylphosphinic Acid Extractants
Published on: October 19, 2017
Molecular Insights into Phosphonium-Based Ionic Liquid Extraction of Phenolic Pollutants from Aqueous Solutions
Mohammad Sadegh Sadeghi Googheri1,2, Samira Hozhabr Araghi3, Jans Alzate-Morales2
1Instituto de Investigación Interdisciplinaria (I3), Vicerrectoría Académica, Universidad de Talca, 1 Poniente 1141, 3460000 Talca, Chile.
Abstract:
Ionic liquids (ILs) are effective extractants for removing hazardous pollutants from aqueous solutions using liquid-liquid extraction (LLE). However, optimizing this process requires molecular-level insight into contaminant-IL interactions. Here, we investigated the extraction mechanisms of phenolic pollutants (PPs) utilizing phosphonium-based ionic liquids (PhILs) through molecular dynamics (MD) simulations and quantum mechanics (QM) calculations. Pair correlation function analysis revealed that PPs preferentially accumulated near PhIL components rather than water, with 2,4-dichlorophenol (2,4-DCPhOH) exhibiting greater accumulation than phenol (PhOH), which correlated with higher extraction efficiency. Switching to 2,4-DCPhOH reduced hydrogen bonding (HB) with water and enhanced interactions with PhIL anions, stabilizing it in the IL phase. In contrast, deprotonated PPs showed stronger HB interactions with water, explaining their lower extraction at high pH. Lennard-Jones short-range interaction energies (LJ-SR IE) indicated stronger binding of 2,4-DCPhOH to PhILs, while PhOH exhibited stronger affinity for water at low IL concentrations. LJ-SR IE emerged as the key parameter explaining efficiency differences based on the MD results. Additionally, QM calculations revealed that chlorine atoms in 2,4-DCPhOH enhance PP-PhILs interaction energies, reduce the highest occupied molecular orbital-lowest-unoccupied molecular orbital (HOMO-LUMO) gap, and strengthen noncovalent interactions. Our findings offer a comprehensive molecular explanation for experimental results, supporting the computational modeling's predictive capacity.
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