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Ultrasound-assisted Liquid-Liquid Spray Extraction for the Determination of Multi-class Trace Organic Compounds in High-volume Water Samples

Abstract

In order to extract trace organic compounds (TrOCs) from large volumes of water (over 10 L), an ultrasound-assisted liquid-liquid spray extraction (UA-LLSE) technique is proposed. The UA-LLSE integrates liquid-liquid extraction, spray atomisation and ultrasonication into a single step. Under acoustic field, water sample is continuously spray injected into an extracting organic solvent to generate numerous water microdroplets in the organic phase. As a result, the extraction time and efficiency are significantly improved through increasing the interactive surface area, collision probability and mass transfer between two liquid phases. Other major parameters affecting the extraction efficiency such as nozzle type, spray angle, flow rate, nozzle position, ultrasonication energy, and extraction cycle were optimised. This newly-developed technique has been applied to determine the trace organic compounds in real-world environmental matrices, and the results were compared with the conventional liquid-liquid extraction methods. For the analysis of high-volume water samples, the UA-LLSE is a simple, inexpensive, time-saving (20 min for 10 L of water sample) and environmentally-friendly method (reduction of solvent usage), with high extraction efficiency (over 90% average recoveries) and excellent precision (lower than 7% relative standard deviation, RSD). Most importantly, it is an ideal on-site pre-treatment technique for the in situ extraction of trace organic compounds in high-volume water matrices.

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Supplementary files

Publication details

The article was received on 10 Apr 2018, accepted on 26 Jul 2018 and first published on 26 Jul 2018


Article type: Paper
DOI: 10.1039/C8AN00656C
Citation: Analyst, 2018, Accepted Manuscript
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    Ultrasound-assisted Liquid-Liquid Spray Extraction for the Determination of Multi-class Trace Organic Compounds in High-volume Water Samples

    H. Y. Kaw, J. Li, X. Jin, Z. Wang, L. Cong, M. He and D. Li, Analyst, 2018, Accepted Manuscript , DOI: 10.1039/C8AN00656C

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