alexa Graphene coated silica applied for high ionization matrix assisted laser desorption ionization mass spectrometry: A novel approach for environmental and biomolecule analysis.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Data Mining in Genomics & Proteomics

Author(s): Nasser Abdelhamid H, Wu BS, Wu HF, Nasser Abdelhamid H, Wu BS, Wu HF, Nasser Abdelhamid H, Wu BS, Wu HF, Nasser Abdelhamid H, Wu BS, Wu HF

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Abstract The integration of nanotechnology with mass spectrometry for sensitive and selective detection of molecules is a hot/important field of research. Synthesis of graphene (G) coated with mesoporous silica (SiO2, [email protected]) for mass spectrometric application has been demonstrated. For the first time, we proposed the significant role of surfactant that used during the synthesis of mesorporous silicate (SiO2) in mass spectrometry. It was noticed that G could initiate SiO2 via surfactants which work as initiators for further ionization. The porosity of SiO2 trapped the analytes that was released and ionized with the surfactant fragments. Undoubtedly, strong background interferences were present in the case of organic matrix, which greatly obscured the detection of low molecular weight compounds. [email protected] nanocomposite affords several advantages, such as the ability to detect small molecules (<500Da), high sample localization through silica mesoporosity, and high ionization efficiency over than G or conventional matrices. The high performance of [email protected] is not only due to the large surface area but also due to high desorption/ionization efficiency of inevitably surfactant (cetyltrimethylammonium chloride, CATB). Unlike the conventional MALDI-MS, the [email protected] is capable of generating multiply charged polysaccharides. The present method was validated to detect surfactants with low limits of detection. Copyright © 2014 Elsevier B.V. All rights reserved. This article was published in Talanta and referenced in Journal of Data Mining in Genomics & Proteomics

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