A wide variety of unique chemosynthetic microbes live in the steep thermal and geochemical gradients present in seafloor hydrothermal vent chimney walls, some of which have the potential for producing unique biochemicals that can be used for various health and pharmachological purposes. These microbes and others survive in hydrothermal systems without the benefit of light from the sun and feed off hot, reduced fluids spewing out of the subsurface on mid-ocean ridges. The thermal gradients in waters coming from these vents change from temperatures up to 400C to 2C on the order of a few centimeters. Due to these steep gradients, it is not presently possible to make quantitative physical measurements of the geochemical characteristics of these resulting microhabitats. This in turn makes it difficult to understand how the various unique microbial life forms that live in these extreme environments carry out their metabolic processes and interact with one another and their changing geochemical environment. This research solves the problem by creating a user-friendly modeling code, based on reversible and irreversible thermodynamics and incorporating the effects of advection and diffusion, that can be used to determine specific geochemical characteristics of hydrothermal fluids in vent chimneys given various chemical, thermal, and physical boundary conditions. The code will be tested using the characteristics and measurements that have been made at five well known hydrothermal vent sites. Broader impacts of the work include building infrastructure for science through the creation of a user-friendly bioenergetics code with a companion user manual that will be made publicly available on the Internet. The code has crossover potential into the field of microbiology and fields associated with examining life in extreme environments and bio-prospecting, both of which have implications for pharmacology and discovering new compounds to improve human health. In addition, the work also supports a female research scientist as well as trains a female graduate student in computational geoscience.
