JCESR has had a very successful first five years. The personal relationships we’ve formed now enable us to move forward with even more momentum. Recently, the team of more than 150 came together for its first full program meeting since renewal. As stated by the team, there is no replacement for direct human interaction. It is easy to get excited when you see the entire JCESR community working toward a common goal. The momentum created when we all come together will have a positive impact on the team for several months as we continue to work more closely together.
Latest Updates
See All-
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
-
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
-
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
-
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
-
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
