Characterising nanomaterials in commercial products

LGC, an international life sciences measurement and testing company, has been using Postnova Analytics’ AF2000 field-flow fractionation (FFF) system coupled to inductively coupled plasma mass spectrometry (ICP-MS) to characterise nanomaterials in complex sample matrices for clinical, cosmetic and food use.

With nanomaterials present in over 1300 commercial products, it is important to be able to characterise nanomaterials to understand their behaviour in contact with humans and the environment. The inorganic analysis team at LGC has expertise in size-based and number concentration analysis of nanomaterials using hyphenated techniques to support the development of reference methods and materials, with FFF coupled to ICP-MS (FFF-ICP-MS) being the centrepiece of their multimodal analytical approach.

“Over the last 15 years, field-flow fractionation (FFF) coupled to ICP-MS and other sizing detectors has proven itself a powerful tool for the characterisation of nanomaterials,” said LGC Principal Scientist Dr Heidi Goenaga-Infante. “For complex samples FFF seemed the ideal choice for matrix separation/sample fractionation, enabling us to achieve selective detection and characterisation of nanomaterials that otherwise would have been hampered by the matrix components.”

The team recently utilised FFF-ICP-MS for the development of a methodology for the determination of number-based concentration of silica nanoparticles with a diameter of approximately 80 nm in a complex serum sample. Their work made use of the AF2000 — a high-performance FFF platform for separation of proteins, macromolecules and nanoparticles — from Postnova Analytics.

“We selected Postnova Analytics as our vendor of choice on the basis of their fast response to queries, scientific credibility and knowledgeable technical research assistance,” said Dr Goenaga-Infante.

By itself, single-particle ICP-MS failed to detect silica nanoparticles due to the high procedural blank for Si with the instrumentation available at the time. In addition, particle tracking analysis (PTA) failed to provide accurate number concentration data with a reasonable measurement uncertainty due to matrix interferences. By using flow FFF to separate the particles from the matrix with online PTA detection, LGC was able to determine number-based concentration for silica nanoparticles of d <100 nm in a complex biological matrix, with no requirement for chemical pre-treatment.

“The Postnova AF2000 system works robustly online when coupled with ICP-MS if a systematic approach is undertaken,” said Dr Goenaga-Infante. “We very much look forward to extending this collaboration into a partnership for life.”