JCESR supplements its traditional project management approach with scientific “Sprints.” The sprint described in this video involved a multidisciplinary team from Argonne, the University of Illinois at Urbana-Champaign, Massachusetts Institute of Technology, and the University of Michigan. As they studied how polymers in solution can react electrochemically to store energy, the team solved a crucial battery problem: “crossover,” which is caused by molecules mixing together when they should not, resulting in reduced performance.
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Architecture-Controlled Ring-Opening Polymerization for Dynamic Covalent Poly(disulfide)s
We reported a strategy to access different topologies of redox-active poly(disulfide)s by ring-opening polymerization. Control over polymerization enables synthesis of high molecular-weight polymers. The polymers undergo catalytic depolymerization to recycle monomer; a promising feature for sustainable flow batteries. Read More
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Adsorption and Thermal Decomposition of Electrolytes on Nanometer Magnesium Oxide: An in Situ 13C MAS NMR Study
The structural and chemical evolution of electrolyte constituents at the nanometric MgO surface were identified, providing a fundamental understanding of heterogeneous interphase evolution. Read More
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Mechanism-Based Design of a High-Potential Catholyte Enables a 3.2 V All-Organic Nonaqueous Redox Flow Battery
Development of an extremely high-potential catholyte leads to the first 3.2 V all-organic flow battery. Read More
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Shedding X‑ray Light on the Interfacial Electrochemistry of Silicon Anodes for Li-Ion Batteries
Our results shed light on the interfacial electrochemistry of silicon anodes for Lithium-ion batteries (LiBs), providing important mechanistic insight into nanometer scale phenomena and how these influence battery performance. Read More
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Accelerating Electrolyte Discovery for Energy Storage through Machine Learning
Utilized high performance computing to generate a database of highly accurate quantum chemical energies of 133 K organic molecules and used machine learning to enable prediction of energies from low fidelity, low cost quantum chemical calculations. Read More
