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NSFDirectorate for Technology, Innovation, and Partnerships

I-Corps: Translation potential of a metal-free, membrane-free long duration energy storage technology

Active
Institution
University of California-Berkeley, CA
Principal investigator
Dean Toste
Funding
$50K
Duration
2026–2027
Open portfolio

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Abstract

This I-Corps project is based on the development of energy storage technology for long duration electrical power grid applications. The electric power grid increasingly requires storage that can shift large amounts of energy over many hours, a need that conventional batteries cannot economically or safely meet. Existing alternatives often rely on metals whose supply chains are concentrated abroad, creating cost, safety, and security vulnerabilities. This technology uses a lower cost battery built from abundant, non-flammable organic materials that can be sourced domestically, paired with intelligent software for managing battery operation and maximizing economic value. In addition, the battery is combined with a software-based analytics service, which may lower overall energy storage costs. This may provide a safer, more affordable, and more secure supply-chain foundation for a resilient and modern electric grid. This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of a symmetric, membrane-free flow battery based on compatible metal-free active materials. The core innovation places chemically compatible positive and negative species within a single electrolyte. This eliminates the high-cost membrane, reduces internal resistance, and removes crossover, which is a principal failure mode of conventional flow batteries, while sustaining stable, high-energy efficiency over extended cycling. In addition, the system tolerates air exposure and operates in both aqueous and non-aqueous media, offering expanded design flexibility, improved deployability, and potential pathways to higher performance than standard chemistries permit. Combining this battery with a machine learning-driven battery management approach exploits the material's intrinsic chemical signals, which are measured with low-cost sensors, to assess battery state and forecast performance decline in advance. This may benefit users through optimized operation, extended material lifetime, more reliable battery health metrics, and improved profitability. 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.

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