Collaborative Research: DMREF: Accelerated Design of Redox-Active Polymers for Metal-Free Batteries
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abstract
Growth in electrified transportation and grid-scale energy storage has been accompanied by increased demand for lithium-ion (Li-ion) batteries. This has caused an increase in world-wide demand of strategic metals such as cobalt and lithium, for which the US does not have deep reserves. To address this challenge, this project envisions metal-free, recyclable, organic batteries based upon redox-active, radical-containing polymers using a multi-disciplinary data-centric approach. The project also bears impact on other potential application areas opening the door to metal-free electronics, memory storage, and spintronics. This project will provide educational training opportunities in synthetic chemistry, polymer science, electrochemistry, and computational chemistry and physics specific to organic batteries. Education and outreach activities will be jointly developed and deployed at The University Chicago?s No Small Matter Molecular Engineering Fair, Texas A&M University?s Physics & Engineering Festival, and other venues. Workforce development is planned through participant mentoring and workshops targeted to industrial, academic, and government researchers.
Radical-containing polymers are promising as redox-active materials, but their current performance remains inferior to current Li-ion battery materials. This project will address the specific challenges of improving the electrochemical performance of the radical-containing polymers as both cathodes and anodes. Specifically, the new knowledge to be gained regarding the cathode and anode include: (1) which chemical modifications adjust the redox potential of the redox active group; (2) which co-monomer patterns promote charge transport; and (3) and which electrolyte promotes electrochemical stability of the polymer. This project brings together researchers from Texas A&M University and The University of Chicago, who will integrate their expertise in machine-learning screening and optimization, computational materials chemistry, high-throughput synthesis, and characterization. In alignment with the Materials Genome Initiative (MGI), this project will culminate in the production and dissemination of a searchable polymer property database on redox-active polymers, polymer-specific inverse design machine learning tools, multiscale models for the redox kinetics, and a proof-of-concept all-organic battery.
