Issue 32, 2022

Fabrication of Ce-doped DUT-52 as a sorbent for dispersive solid phase extraction of estrogens in human urine samples

Abstract

A cerium (Ce)-doped metal–organic framework composite (Ce/DUT-52) was prepared by using a solvothermal method and was explored as a sorbent for dispersive solid phase extraction (DSPE) of three estrogens (α-estradiol, estrone, and hexestrol) in human urine samples. After doping with Ce(III), Ce/DUT-52 exhibited more attractive features involving a higher specific surface area (774.7 m2 g−1) and zeta potential (31.4 mV), which made it an efficient adsorbent for the separation and enrichment of estrogens. The factors influencing DSPE efficiency such as the adsorbent amount, extraction time, pH, NaCl concentration, elution solvent and elution volume were investigated in detail. Under the evaluated conditions, Ce/DUT-52 showed good reusability (n = 6, RSDs ≤ 4.8%). Notably, the cofunction of electrostatic interaction, hydrophobic interaction, hydrogen bonding and π–π interaction might play major roles between estrogens and Ce/DUT-52. Finally, coupled with high-performance liquid chromatography (HPLC), a fast and sensitive method was established, which provided low limits of detection (1.5–2.0 ng mL−1), wide linear ranges (3–500 ng mL−1) and satisfactory recoveries (79.8–96.1%). The results demonstrated that Ce/DUT-52 had excellent adsorption ability to the targets and the developed method provided an alternative strategy for the determination of trace estrogens or other compounds with similar chemical structures in urine samples.

Graphical abstract: Fabrication of Ce-doped DUT-52 as a sorbent for dispersive solid phase extraction of estrogens in human urine samples

Supplementary files

Article information

Article type
Paper
Submitted
16 May 2022
Accepted
30 Jun 2022
First published
02 Aug 2022

Anal. Methods, 2022,14, 3094-3102

Fabrication of Ce-doped DUT-52 as a sorbent for dispersive solid phase extraction of estrogens in human urine samples

L. Guo, Y. Wang, M. Yan, X. Li, X. Jiang, M. Wang, Q. Wang, X. Wang and Y. Hao, Anal. Methods, 2022, 14, 3094 DOI: 10.1039/D2AY00795A

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