研究目的
The main aim of this work was to develop an effective, low solvent consumptive, simple and easy to use analytical method based on ultrasound-assisted surfactant-based dispersive liquid–liquid microextraction (UAS-DLLME) for the simultaneous preconcentration, extraction and quantification of TiO2 and ZnO ENPs from industrial wastewater samples.
研究成果
The developed UAS-DLLME method combined with ICP-OES was effective for the simultaneous extraction, preconcentration, and quantification of TiO2 and ZnO nanoparticles in industrial wastewater. The method demonstrated good precision, high sensitivity, and acceptable recoveries, making it suitable for environmental monitoring of engineered nanoparticles.
研究不足
The study focused on TiO2 and ZnO nanoparticles in wastewater, and the method's applicability to other types of nanoparticles or matrices was not explored. The optimization was based on a limited set of variables, and potential interferences from complex wastewater matrices were not fully investigated.
1：Experimental Design and Method Selection:
The study utilized ultrasound-assisted surfactant-based dispersive liquid–liquid microextraction (UAS-DLLME) for the preconcentration and extraction of TiO2 and ZnO nanoparticles from wastewater samples, followed by quantification using inductively coupled plasma optical emission spectrometry (ICP-OES).
2：Sample Selection and Data Sources:
Wastewater samples were collected from textile, cosmetic, and paint industries.
3：List of Experimental Equipment and Materials:
Instruments used included an ICP-OES instrument (iCAP 6500 Duo, Thermo Scientific), an ultrasonic bath (model 5800, Branson), and a centrifuge (model 5702, Eppendorf). Chemicals included Triton X-114, n-butyl acetate, and ultrapure nitric acid.
4：Experimental Procedures and Operational Workflow:
The extraction and preconcentration procedure involved the use of UAS-DLLME under optimized conditions, followed by centrifugation and analysis using ICP-OES.
5：Data Analysis Methods:
The data were analyzed using response surface methodology (RSM) based on Box–Behnken design for optimization.
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