Molecular legos

A pair of Missouri S&T scientists are drawing inspiration from toy building blocks to create fixed molecular units used to speed up the material discovery process known as rational design. They’ll use these “molecular blocks” to discover materials that could be used to make highly sought solid-state lithium batteries.

“In the materials and solid-state chemistry community, there’s always a desire to make materials in a more rational, predictable way,” says Amitava Choudhury, associate professor of chemistry. “And the all-solid-state battery is a hot research area right now — it’s the holy grail of lithium batteries. The right discovery could enable the use of solid-state batteries in hybrid or full-electric vehicles, or anywhere safety predominates, because the all-solid-state versions will be less flammable than current lithium batteries.” Today’s lithium batteries are made with electrolytes composed of combustible solvents, he adds.

The discovery of new materials with the optimum chemical properties is a slow, tedious process driven by intuition and painstaking trial-and-error experiments. With funding from a $411,000 grant from the National Science Foundation’s Solid State and Materials Chemistry Program, Choudhury and Aleksandr Chernatynskiy, assistant professor of physics, hope to improve the material invention process.

The researchers plan to accelerate the discovery process by combining experiments with a theoretical modeling approach that uses fixed molecular units, which function like toy building blocks that come in various shapes and sizes and can be connected in different but predictable ways.

“Instead of using direct chemical elements in our experiments, which are very reactive at high temperatures and can result in undesired products, we’ll use pre-determined molecular building blocks, which can be connected only in certain ways that allow us to direct our results,” says Choudhury.

Chernatynskiy, a theoretical physicist, will calculate the interactions of the different molecular building blocks to predict the most stable outcomes and determine where adjustments need to be made. This will reduce repetitive experiments and save time and money in the research and development phase.

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