relation: https://khub.utp.edu.my/scholars/19193/
title: Ionic liquid-based dispersive liquid�liquid microextraction of succinic acid from aqueous streams: COSMO-RS screening and experimental verification
creator: Khan, H.W.
creator: Zailan, A.A.
creator: Bhaskar Reddy, A.V.
creator: Goto, M.
creator: Moniruzzaman, M.
description: In the present investigation, a total of 108 combinations of ionic liquids (ILs) were screened using the conductor-like screening model for real solvents (COSMO-RS) with the aid of six cations and eighteen anions for the extraction of succinic acid (SA) from aqueous streams through dispersive liquid�liquid microextraction (DLLME). Using the screened ILs, an ionic liquid-based DLLME (IL-DLLME) was developed to extract SA and the role of different reaction parameters in the effectiveness of IL-DLLME approach was investigated. COSMO-RS results suggested that, quaternary ammonium and choline cations form effective IL combinations with OH¯, F¯, and SO42¯ anions due to hydrogen bonding. In view of these results, one of the screened ILs, tetramethylammonium hydroxide TMAmOH was chosen as the extractant in IL-DLLME process and acetonitrile was adopted as the dispersive solvent. The highest SA removal efficiency of 97.8% was achieved using 25 μL of IL TMAmOH as a carrier and 500 μL of acetonitrile as dispersive solvent. The highest amount of SA was extracted with a stir time of 20 min at 300 rpm, followed by centrifugation for 5 min at 4500 rpm. Overall, the findings showed that IL-DLLME is efficient in extracting succinic acid from aqueous environments while adhering to the first-order kinetics. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
publisher: Taylor and Francis Ltd.
date: 2023
type: Article
type: PeerReviewed
identifier:   Khan, H.W. and Zailan, A.A. and Bhaskar Reddy, A.V. and Goto, M. and Moniruzzaman, M.  (2023) Ionic liquid-based dispersive liquid�liquid microextraction of succinic acid from aqueous streams: COSMO-RS screening and experimental verification.  Environmental Technology (United Kingdom).   ISSN 09593330     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165501640&doi=10.1080%2f09593330.2023.2234669&partnerID=40&md5=d49db3e6cfdfdeda59ca349281062a82
relation: 10.1080/09593330.2023.2234669
identifier: 10.1080/09593330.2023.2234669