Better batteries for electric cars and cellphones and better batteries for industrial use that can store excess power generated by wind and solar farms and then supply it to the grid at times of peak demand are greatly needed. The ability to efficiently store energy generated from renewable sources would quite literally be a game changer in the quest to build a clean energy future and reduce the impact of climate change. George Crabtree, director of the Joint Center for Energy Storage Research, discusses the importance of next-generation batteries with Paris Schutz on Chicago Tonight.
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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
