AF4-UV-MALS-ICP-MS/MS, spICP-MS, and STEM-EDX for the Characterization of Metal-Containing Nanoparticles in Gas Condensates from Petroleum Hydrocarbon Samples

Daniel Ruhland* (Corresponding Author), Kenneth Nwoko, Magali Perez, Joerg Feldmann, Eva M. Krupp

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)
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The coupling of flow field flow fractionation (FlFFF) with ICP-MS/MS for the fractionation and analysis of natural nanoparticles in environmental samples is becoming more popular. However, the applicability of this technique to non-aqueous samples such as gas condensates from petroleum hydrocarbon samples has not been reported yet. In this study, an asymmetric flow-field flow fractionation (AF4) system coupled with UV and MALS detectors has been optimized to perform the fractionation of natural nanoparticles present in a gas condensate sample, using THF as the carrier liquid. Prior to this, STEM images indicated the presence of both large (200 nm and more) and smaller (50 nm and less) particles, whose irregular shape is probably due to agglomeration. AF4-UV-MALS-ICP-MS/MS confirmed the presence of various nanoparticles and colloids, some containing aromatic compounds as well as various metals including Hg. The recovery against an injection without crossflow is around 75% for most metals. The presence of Hgcontaining nanoparticles was confirmed with offline single particle ICP-MS (spICP-MS), using THF as a solvent. These NPs were identified as HgS using STEM-EDX. These results highlight for the first time that particulate matter may contaminate gas condensates with a series of elements (Al, P, S, Ti, V, Mn, Fe, Co, Cu, Zn, As, Se, Cd, Hg, Pb) which can make the upstream use problematic, especially for mercury.
Original languageEnglish
Pages (from-to)1164-1170
Number of pages7
JournalAnalytical Chemistry
Issue number1
Early online date5 Dec 2018
Publication statusPublished - 2019

Bibliographical note

Dr. Andrea Raab (University of Aberdeen, U.K.) is thanked for her advice on ICP-MS, which has helped to achieve this work. D.R. thanks Johnson Matthey, U.K. for the provided studentship; special thanks go to Colin Baptist, Lucy Barrass, Matt Lunn, Stefano Martinuzzi, Katie Smart and Bradley Waldron for the fruitful discussions and support; as well as Emily Brooke for her help with the STEM-EDX analyses. K.N. acknowledges the support provided by the University of Aberdeen through the Elphinstone scholarship. Authors also gratefully acknowledge Postnova Analytics UK for the loan of the AF4 system together with training, support and advice on the technique. Special thanks go to Dr. Bassem Sabagh for his help.


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