Russian scientists have concluded that most of the expensive transition metal compounds used in the production of ion batteries can be replaced by cheaper alternatives.
These alternative materials are based on polycyclic aromatic hydrocarbons (PAHs), without negatively impacting battery performance. These organic materials are abundant in natural environments and fossil hydrocarbon sources, and are also easy to process and recycle.
This was announced by the press service of the Russian Skoltech Institute of Innovative Technologies.
"We have shown that common aromatic compounds such as naphthalene or anthracene can perform at least as well as, and sometimes even better than, expensive synthetic materials," said Ilya Chepkasov, senior researcher at the School of Materials Technologies. "This opens the way for the manufacture of batteries whose main components are produced from renewable raw materials."
As Chepkasov and his colleagues explained, the battery industry, including lithium batteries, currently relies on a large number of complex and expensive synthetic materials used in the manufacture of the cathode (positive electrode) and other components. These materials are often derived from transition metal compounds such as nickel and cobalt, which are characterized by their high cost and limited availability.
For the first time, Russian researchers have conducted a comprehensive study of the possibility of replacing these metals with more environmentally safe and less expensive materials based on polycyclic aromatic hydrocarbons.
Analysis by the Scholtech research team has shown that many of these molecules possess properties comparable to their nickel- and cobalt-based counterparts. Furthermore, their electronic properties can be easily modified by introducing atoms of other elements or new functional groups.
In theory, this approach allows for the adaptation of these compounds to meet specific battery design requirements, potentially improving efficiency or reducing production costs. Furthermore, these molecules can be designed and studied using machine learning and virtual simulation techniques, enabling the selection of optimal materials for each battery component with minimal laboratory experimentation.
