Abstract
Advances in solid-state batteries have primarily been driven by the discovery of superionic conducting structural frameworks that function as solid electrolytes. We demonstrate the ability of high-entropy metal cation mixes to improve ionic conductivity in a compound, which leads to less reliance on specific chemistries and enhanced synthesizability. The local distortions introduced into high-entropy materials give rise to an overlapping distribution of site energies for the alkali ions so that they can percolate with low activation energy. Experiments verify that high entropy leads to orders-of-magnitude higher ionic conductivities in lithium (Li)–sodium (Na) superionic conductor (Li-NASICON), sodium NASICON (Na-NASICON), and Li-garnet structures, even at fixed alkali content. We provide insight into selecting the optimal distortion and designing high-entropy superionic conductors across the vast compositional space.
Original language | English |
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Pages (from-to) | 1320-1324 |
Number of pages | 5 |
Journal | Science |
Volume | 378 |
Issue number | 6626 |
DOIs | |
State | Published - Dec 23 2022 |
Funding
This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Vehicle Technologies Office, of the US Department of Energy under contract no. DE-AC02-05CH11231. The computational analysis was performed using Eagle and Swift at the National Renewable Energy Laboratory, Extreme Science and Engineering Discovery Environment, supported by NSF ACI1053575; and the National Energy Research Scientific Computing Center. Neutron diffraction was conducted at the NOMAD beamlines at Oak Ridge National Laboratory’s Spallation Neutron Source, which was sponsored by the Scientific User Facilities Division, whereas STEM was performed at the National Center for Electron Microscopy (NCEM) at Lawrence Berkeley National Laboratory (LBNL). Both facilities are supported by the Office of Science of the US Department of Energy. We also appreciate K. Bustillo from NCEM at LBNL for her technical assistance in STEM.