Data Analysis

The development of mass spectrometry is one of science’s great technology stories.

In this application note we will look at two examples where the retention volume of a sample could not be used to obtain an accurate measure of the sample MW. We will also describe the additional information that can be obtained using these advanced detectors. This work has been performed in conjunction with the Institut Pasteur, Paris, France

The Zetasizer µV (MicroV) is a dual purpose light scattering instrument designed specifically for the analysis of proteins. Firstly, it is a highly sensitive cuvette based dynamic light scattering instrument with the same great performance as the market leading Zetasizer Nano. Secondly, by simply changing the cell, it becomes an absolute molecular weight and size detector that can be added to any Size Exclusion Chromatography system (such as the Viscotek TDAmax).

The Zetasizer Nano is the world's most widely used light scattering system, for measuring size, zeta potential and molecular weight. Applications range from characterizing high concentration colloids and nanoparticles, through to measurement of proteins and macromolecules in their native state, requiring as little as 12 microlitres of sample. The Zetasizer Nano can be used for the comprehensive characterization of proteins, colloids and nanoparticles, and is simple to use, highly sensitive, versatile and fast! This videos shows how quickly and easily a measurement can be made on the Zetasizer Nano.

Polymers can be loosely grouped into two structural categories – linear and branched.

In August, The Analytical Scientist will publish a special issue celebrating the very best images in the field of analytical science. And you have the chance to submit an image that defines your work.

Accurate determination of persistent organic pollutants has come a long way in twenty years, but we should not rest on our laurels: increased speed, improved economics and a reduced environmental footprint are important targets.

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Gamers are better at identifying certain patterns than computer algorithms, so why not use them to pick out breast cancer-related gene profiles?