Analytical Community Rewards Postnova System with 2012 Innovations Award


Postnova Analytics GmbH, Landsberg-am-Lech, Germany: The new CF2000 Series for high-resolution separation and fractionation of nanoparticles, from Postnova Analytics, has won the prestigious GIT Innovations Award 2012 in the category 'Analytical Instrumentation and Software'. The Postnova team are the inventors of the Field-Flow Fractionation (FFF) technique, and developed the CF2000 to be the first Centrifugal FFF system for routine use.

Dr Thorsten Klein, Founder and CEO, Postnova, said: "It is a great honour to receive this award. To be acknowledged by scientists working at the bench is particularly important for us as it shows that our innovative products are recognized as powerful practical tools for daily analysis, exactly what we designed them to be. I would like to thank the publishers at G.I.T. Verlag for sponsoring the award, the independent jury who placed the CF2000 on the shortlist and, most of all, everyone who took the time to vote for us online, at Analytica, and at Achema."

The CF2000 is modular, allowing for a range of different detectors: Multi-Angle Light Scattering (MALS) , Dynamic Light Scattering (DLS) and Refractive Index (RI), for example, to be integrated. Whatever the configuration, everything from autosampler to detector is fully controlled by the NovaFFF software platform.

Importantly, by using a centrifugal field as the controlling force for the separation, particles are separated by Dynamic Diffusion on the basis of size and density. This novel approach allows discrimination of materials that have the same particle size but different densities.

The CF2000 has a wide operation range and is able to separate smaller species, such as proteins and polymers, from larger particles in one run with high resolution. The system is ideal for nanoparticles and works up into the microparticle size range too. No special sample treatment is necessary, allowing challenging biopharmaceutical, food-cosmetics, nanomaterial and environmental samples to be characterized.

For more information on the complete range of Postnova FFF systems, visit: www.postnova.com www.postnova.com/cf2000-overview.html

About Postnova Analytics

Postnova Analytics are the experts in Field-Flow Fractionation (FFF) and Light Scattering technologies - the modern alternative to chromatography. Found in 1997 by Dr. Thorsten Klein in Munich, Germany, the company introduced the first commercial Asymmetrical Flow FFF (AF4) instrument. This system brought a new level of automation and separation performance to the market.

In 2001 Postnova Analytics acquired FFFractionation - founded by Prof. Giddings the inventor of FFF, a company based in Salt Lake City, Utah that had been a pioneer for the commercialization of FFF technology, and Postnova Analytics USA was formed. The new company became the sole provider of all variants of Field-Flow Fractionation. Following the acquisition, in 2003, Postnova moved into new headquarters in Landsberg am Lech, which is located in the Greater Munich area, in southern Germany.

Postnova Analytics continues to innovate, and now offers 'the FFF-Platform' a broad range of Field-Flow Fractionation and Light Scattering solutions, which are ideally suited for gentle, high resolution and matrix-free separations of large molecules and particles, which traditional chromatographic techniques cannot perform. FFF coupled to Multi-Angle Light Scattering (MALS) is the modern alternative to liquid chromatography (LC) and size exclusion SEC techniques, especially when it comes to separation, characterization and fractionation of nanoparticles, proteins, antibodies, biomacromolecules, polymers, adhesives, elastomers, rubbers and other large molecules or particles.

For press information, please contact:

Rainer Jünger, Marketing Director, Postnova Analytics

Max-Planck-Str. 14, 86899 Landsberg, Germany

Tel: +49 8191 985688-0 rainer.juenger@postnova.com

Kapler Communications,

Phoenix House Phoenix Park,

Eaton Socon,

Cambs PE19 8EP United Kingdom

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