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DOI 10.1039/d1ra03227e

Медицинская библиотека · RSC advances · 2021

Determination of halogenated hydrocarbons in urine samples using a needle trap device packed with Ni/Zn-BTC bi-MMOF via the dynamic headspace method.

Rahimpoor Razzagh, Firoozichahak Ali, Nematollahi Davood, Alizadeh Saber, Alizadeh Parsa Mohammad, Alinaghi Langari Ali Akbar

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Аннотация

In this study, a nickel/zinc-BTC bi-metallic metal-organic framework (bi-MMOF) was employed as a new and efficient adsorbent in a needle trap device (NTD) for headspace (HS) sampling, extraction and analysis of halogenated hydrocarbons (trichloroethylene, tetrachloroethylene, chloroform, and tetrachloroethylene) from spiked and real urine samples. Characterization of the prepared adsorbent was accomplished by FT-IR, PXRD, EDX, elemental mapping, and FE-SEM techniques. According to experimental results, the optimal temperature and extraction time, salt content, temperature and desorption time of the response surface methodology (RSM) and Box-Behnken design (BBD) were determined to be 56 °C and 30 min, 5.5%, 350 °C and 8 min for the studied halogenated hydrocarbons, respectively. The calculated values of detection limit and quantitation limit parameters were in the range of 1.02-1.10 and 2.01-2.4.0 ng L-1, respectively. Moreover, intermediate precision and repeatability of the method were in the range of 4.90-8.20% and 1.50-4.80%, respectively. The recovery percentages of analytes were obtained to be in the range of 95.0-97.0% 10 days after the sampling and storage at 4 °C. This study showed that the proposed HS-NTD:Ni/Zn-BTC method coupled with a GC-FID can be employed as a simple, fast, and sensitive procedure for non-metabolized halogenated hydrocarbons from urine samples in biological monitoring.

Для цитирования: Rahimpoor Razzagh, Firoozichahak Ali, Nematollahi Davood, Alizadeh Saber, Alizadeh Parsa Mohammad, Alinaghi Langari Ali Akbar. Determination of halogenated hydrocarbons in urine samples using a needle trap device packed with Ni/Zn-BTC bi-MMOF via the dynamic headspace method.. RSC advances. 2021. DOI: 10.1039/d1ra03227e.
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