NanoGeoBio Laboratory

Proposed mechanisms of Mo removal along with associated isotopic fractionations in a continental margin setting with euxinic bottom waters and sediment pore water.

The observed difference in molybdenum (Mo) mobility and isotopic fractionation under oxic versus euxinic (i.e., anoxic and sulfidic) aqueous conditions provides a sound operational basis for the use of Mo geochemical signatures in ancient sedimentary records to infer palaeoceanographic redox conditions. While Mo is known to exist predominantly as molybdate (MoO42-) in oxic waters and convert into thiomolybdate species (MoOxS4-x2-) under euxinic conditions, the pathways that lead to Mo sequestration are highly debated. As mechanistic understanding of Mo sequestration is crucial for accurately reconstructing the chemistry of ancient oceans and constraining the timing and intensity of oxygenation events through Earth’s history using Mo paleoproxies, we have closely examined the current proposed mechanisms for Mo sequestration across a wide range of euxinic conditions. Through compilation and comparison of such information, we aim to provide an integrated view of Mo sequestration processes, to identify the current controversies as well as the roots of such controversies, and importantly, to propose avenues for future research.

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Nanotechnology has enabled new pathways to precision and smart agriculture. There has been a dramatic increase in the research interest and intensity on agriculture-relevant functional nanoparticles (agri-NPs). By contrast, our understanding of the actual efficacy, biosafety, and environmental impacts of agri-NP applications remains limited. Such limitation conveys an urgency in the development of cost-effective analytical tools for screening agri-NPs in relevant matrices, such as plants and soil. Here we propose to develop reliable analytical protocols (with the potential of creating high-throughput and automated analytical procedures) for detection, identification, and quantification of targeted NPs in plant and soil matrices based on dark field hyperspectral microscopy (DFHM) and machine learning techniques.