Abstract
Rechargeable batteries based on multivalent working ions are promising candidates for next-generation high-energy-density batteries. Development of these technologies, however, is largely limited by the low diffusion rate of multivalent ions in solid-state materials, thereby necessitating a better understanding of the design principles that control multivalent-ion mobility. Here, we report Ca1.5Ba0.5Si5O3N6 as a potential calcium solid-state conductor and investigate its Ca migration mechanism by means of ab initio computations and neutron diffraction. This compound contains partially occupied Ca sites in close proximity to each other, providing a unique mechanism for Ca migration. Nuclear density maps obtained with the maximum entropy method from neutron powder diffraction data provide strong evidence for low-energy percolating one-dimensional pathways for Ca-ion migration. Ab initio molecular dynamics simulations further support a low Ca-ion migration barrier of ∼400 meV when Ca vacancies are present and reveal a unique "vacancy-adjacent"concerted ion migration mechanism. This work provides a new understanding of solid-state Ca-ion diffusion and insights into the future design of novel cation configurations that utilize the interactions between mobile ions to enable fast multivalent-ion conduction in solid-state materials.
| Original language | English |
|---|---|
| Pages (from-to) | 128-139 |
| Number of pages | 12 |
| Journal | Chemistry of Materials |
| Volume | 34 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 11 2022 |
Funding
This work was supported by the Volkswagen group. Work at the Molecular Foundry, LBNL, was supported by the Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. This work used resources at the Spallation Neutron Source, a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory. This work used computational resources provided by the Extreme Science and Engineering Discovery Environment (XSEDE), supported by National Science Foundation grant No. ACI1053575, as well as the Lawrencium computational cluster resource provided by the IT Division at Lawrence Berkeley National Laboratory (Supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231). The authors would also like to thank Nong Artrith for providing codes to assist in analyzing AIMD results and for helpful discussions.
| Funders | Funder number |
|---|---|
| National Science Foundation | ACI1053575 |
| U.S. Department of Energy | DE-AC02-05CH11231 |
| Office of Science | |
| Basic Energy Sciences | |
| Volkswagen Aktiengesellschaft |