The development of comprehensive multiphase NMR spectroscopy to understand environmental contamination at the molecular level

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The proposal aims to develop a new field of nuclear magnetic resonance (NMR) spectroscopy to directly assess how and why contaminants become sequestered in soils, and thus are hard to remediate in the environment. The new technology termed "Comprehensive Multiphase" NMR (CMP-NMR) will be novel and globally unique. The approach will be developed specifically to study molecular interactions in unaltered multiphase samples (i.e. samples containing solid, liquid, semi-solid/gel components) such as soil and sediments. The approach will not only identify the components in soils to which contaminants bind (i.e. lignin, protein, cuticle etc.) and the specific molecular bonds/interactions/domains (in the soil and on the contaminant) responsible for these associations but when combined with modern in-situ toxicity methods, provides a framework, to assess the complex relationship between soil-sorption and bioavailability. CMP-NMR will provide a detailed insight at the molecular-level of the processes that cause contaminants to be entrapped in the soil matrix and describes the complete binding environment of the contaminant in unaltered (not even dried) natural samples. As such it represents a powerful tool to assess the efficiency and effectively of existing and novel remediation approaches The project will provide a new understanding of current problems as well as providing a new suite of molecular tools that can evaluate new remediation strategies such as the use of new surfactants or new microbial strains for bioremediation. The molecular information will not only identify if the remediation is effective but describes which contaminant-soil bonds/associations remain intact, thus explaining why a specific strategy is not effective. With knowledge as to the exact soil components (for example protein) and facets of the contaminant that cause binding it should be relatively easy using physical-chemical predictions to select the most effective agents for their release.