High performance counter current chromatography for the separation of a complex environmental matrix: dissolved organic matter (dom) — ASN Events

High performance counter current chromatography for the separation of a complex environmental matrix: dissolved organic matter (dom) (#192)

Sara Sandron , Brian Kelleher , Noel Davies , Richard Wilson , Pavel Nesterenko , Brett Paull

Dissolved organic matter (DOM) in sea and freshwater represents a carbon reservoir comparable to atmospheric CO2 (respectively 624 and 750 Gigatonnes). CO2 is a primary product of DOM mineralisation, therefore an intimate link exists between this dissolved pool of carbon and the atmosphere. The complexity of DOM inhibits conventional chromatographic analysis (LC and GC) with common detectors, as little structural information can be obtained due to extensive co-elution. The chemical composition of DOM is extremely complex, containing various classes of compounds. These are polyfunctional, heterogeneous (amino acids, organic acids, lipids, phosphonates, carboxyl-rich alicyclic molecules (CRAM) and carbohydrate like precursors), polyelectrolytic, polydisperse in molecular weight (300-7000 Da) and in concentrations ranging from picomolar to micromolar.
In an effort to improve the current level of understanding of the complex nature of DOM, a multi-dimensional approach was employed: normal-phase high-performance counter current chromatography (HPCCC) as the first chromatographic dimension, with reversed-phase liquid chromatography (RPLC) coupled with high resolution positive and negative mode mass spectrometry (RP-MS/MS), as the second.
After optimisation, HPCCC was able to show, for the first time, the fractionation of the most characteristic compounds present within DOM according to their partition coefficient within the solvent system. The use of this chromatographic approach allowed the simplification of this very complex mixture to an extent that single compounds were isolated and understood in terms of fragmentation patterns and molecular formulae, providing crucial information towards the identification of the main components characterising the analysed DOM sample.