Jan 6 2017
Postnova Analytics reports on how the inorganic analysis team within LGC (Teddington, UK) has been using the AF2000 Field Flow Fractionation system coupled to Inductively Coupled Plasma Mass Spectrometry (ICP-MS) to characterise nanomaterials in complex sample matrices for clinical, cosmetic and food use.
Nanomaterials are known to be present in over 1300 commercial products. As a result of the European Union (EU) recently providing a precise definition of a nanomaterial for regulatory purposes, it has become important to be able to characterise nanomaterials reliably to understand their behaviour in contact with humans and the environment.
LGC is an international life sciences measurement and testing company with a history stretching back 175 years, providing reference materials, genomics solutions and analytical testing products and services. It is home to several national government roles, including the UK National Measurement Laboratory, the Designated Institute for Chemical and Bio-measurement and the Government Chemist.
The inorganic analysis team at LGC - led by Principal Scientist and Science Fellow, Dr Heidi Goenaga-Infante - has established world class expertise in size-based and number concentration analysis of nanomaterials using hyphenated techniques to support the development of reference methods and materials, with field flow fractionation coupled to ICP-MS (FFF-ICP-MS) being the centrepiece of their multi-modal 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. 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.
Dr Goenaga-Infante
Dr Goenaga-Infante added “Having decided that FFF was the technique for us, we approached the two leading FFF manufacturers. 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. The Postnova AF2000 system works robustly online when coupled with ICP-MS if a systematic approach is undertaken. We very much look forward to extending this collaboration into a partnership for life.”
A notable project** where the FFF- ICP-MS has demonstrated its worth has been 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. 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 on-line PTA detection, LGC were able to accurately 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 is a high performance Flow Field-Flow Fractionation (FFF) platform for separation of proteins, macromolecules and nanoparticles. Modular in design, the AF2000 incorporates the combined experience, expertise and technological advances from Postnova Analytics' nearly two decades of leadership in FFF. Incorporating a range of FFF modules in a single integrated system to provide universal separation, the AF2000 offers more flexibility, better performance and more robust results than any system before.
For further information on Field Flow Fractionation please visit www.postnova.com/af2000-overview.html or contact Postnova Analytics on +44-1885-475007 / [email protected]. For further information on the work being undertaken by the inorganic analysis team at LGC, please visit www.lgcgroup.com/nmi.