SBIR Fast-Track: Electroanalytical State-of-charge and Chemical Composition Sensors for Redox Flow Batteries

Award ID: 2604898

The broader/commercial impact of this Small Business Innovation Research Phase I (Fast-Track)project will be the development of a low-cost diagnostics platform that improves the reliability, safety, and lifetime of flow batteries for long-duration energy storage. This project will address gaps by developing sensors that monitor electrolyte composition in real time so operators can detect degradation before major performance loss or failure occurs. Earlier detection of imbalance, side reactions, and active-material loss will reduce maintenance costs, extend battery lifetime, and improve compliance with emerging monitoring requirements. This Small Business Innovation Research Phase I project will investigate an electrochemical sensing platform that translates laboratory-grade flow battery electroanalysis into a low-cost sensor architecture, where the high risk element lies in developing durable sensor electrode materials which contribute stable chemical composition data in corrosive flowing electrolytes while remaining cost-effective at scale. Existing battery monitoring methods either infer battery condition indirectly from external potential signals or rely on environmental sensors that cannot directly quantify redox-active species in solution. This project will develop thin-film electrochemical sensors that measure the ratio and concentration of oxidized and reduced species in flowing battery electrolytes using controlled potential sweeps and current response analysis. The research scope includes sensor design, electrochemical benchmarking, stability testing, and integration with embedded electronics. The methodology will compare multiple new working-electrode materials, reference-electrode chemistries, and thin-film designs across iron, vanadium, and zinc electrolytes under controlled flow, temperature, and state-of-charge conditions. Measurements will be evaluated for concentration sensitivity, signal drift, repeatability, and anomaly-detection performance. The project will also develop firmware and software that collect current and potential signals, apply calibration routines, and identify sensor malfunction or battery degradation from changes in electrochemical response. The expected outcome is a scalable sensing platform capable of millimolar-level species detection and stable operation over extended cycling, establishing the technical basis for predictive diagnostics in commercial flow battery systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Ela Mirowski