Item No. 1 of 1
ACCESSION NO: 1030017 [Full Record]
PROJ NO: TEXW-2023-00988 AGENCY: NIFA TEXK
PROJ TYPE: SMALL BUSINESS GRANT PROJ STATUS: EXTENDED
CONTRACT/GRANT/AGREEMENT NO: 2023-33530-39341 PROPOSAL NO: 2023-00988
START: 01 JUL 2023 TERM: 28 FEB 2025
GRANT AMT: $181,494 GRANT YR: 2023 AWARD TOTAL: $181,494 INITIAL AWARD YEAR: 2023
INVESTIGATOR: Roodenko, E.; McGowen, JO, .; Robbins, DE, .; Daunis, TR, .; Dussor, JE, .; Clark, KE, .
PERFORMING INSTITUTION:
MAX-IR LABS INCORPORATED
17217 WATERVIEW PKWY
DALLAS, TEXAS 752528004
INFRARED BIOCHEMICAL SENSOR FOR ALGAL PRODUCTION EFFICIENCY IMPROVEMENT
NON-TECHNICAL SUMMARY: We propose developement of a dedicated sensor for monitorig of components in algal growth media. Algae are a sustainable source of renewable energy. However, their commercialization is hindered by low yields and high operational costs. In-line monitoring of nutrients and extracellular metabolites will enable consistent growth and increased production due to the possibility of automated process control through adjustments of growth conditions and timely assessment of extracellular chemicals that signal the development of stress in the culture. As a result, we foresee automation as a catalyst for faster adoption of algae-based biofuels and overall faster development of algal products for nutraceuticals, feedstock, and other uses.
OBJECTIVES: This proposal addresses the need for inline monitoring of algae growth medium to enable process control in laboratory, industrial and pond-based bioreactors to improve the efficiency of algal production.We propose the development of infrared-based sensor for integration in laboratory and field-based (pond) bioreactors.The sensor will enable a transition from "blind" operations that rely on technicians' experience to automated system controls.This will lead to scalable algal production, reduced failure events, and optimization of the C/N ratio of nutrient supply required for the production of specific lipid/protein biomass content.The proposed sensor targets applications in research and industrial production settings, enabling efficient scaling, process optimization, and consistent production of biomass content, resulting in reduced production costs.
|