SBIR-STTR Award

No commercial technologies exist to image the transfer of charge in real-time through plant/microbial interactions. It has been previously recognized that microbial biofilms may represent a parallel network for the control of charge through the environment including soils and sediments. This scope of work will extend this recognition of the control of charge by biofilms to the growth and development of plants. The previously developed microbial sensor system developed for subsurface environments will be applied to biofilm interactions with plants both above and below ground. Large arrays of potentiometric sensors will deployed both above and below ground surface for imaging real-time charge transfer in 3-D. A portable, laboratory-grade instrument will be designed, fabricated and tested to bioimage the charge transfer through a plant roots, stem, leaves). The design of the system will allow both laboratory and field investigations at a final cost structure to be affordable of many researchers in the field. The final system will allow researchers to study variable the inputs to plants sun,exposure, moisture, nutrient concentrations) to determine the impact to plant growth and development with the primary goal of optimizing the biomass produced for the given inputs.

Bioimaging of plant/microbial interactions was identified as an important tool in understanding the factors controlling the production of biomass. Presently, no field- deployable research instrumentation exists for long-term monitoring of plant/microbial interactions. Potentiometric (open-circuit voltage) measurements have been developed as an analytical technique for understanding plant/microbial interactions. The simplicity of the analytical technique allows the deployment of large arrays of sensors throughout the environment (plants, roots, soils).Instrumentation and methods were developed using large arrays of sensors (>200 sensors) to measure the potentials generated by biofilms throughout the structures of a plant (rhizosphere, stems and leaves) over a period of several months. The data generated by the instrumentation was used to create visualizations (3D and 4D) of the charge transfer between the structures of a plant, and its surrounding soils. Refinements will be made to the instrumentation and visualization capability combined with machine learning algorithms to allow the modeling the factors impacting the plant/microbial interactions. It is anticipated the models will lead to predictive tools capable of optimizing the production of biomass. The primary deliverables from this project will be a cost-effective, research instrument as well as a field-deployable instrument allowing workers to understand and optimize biomass production.