Increasing the Sodium Metal Electrode Compatibility with the Na₃PS₄ Solid‐State Electrolyte through Heteroatom Substitution

Abstract

Rechargeable batteries are essential to the global shift towards renewable energy sources and their storage. At present, improvements in their safety and sustainability are of great importance as part of global sustainable development goals. A major contender in this shift are rechargeable solid‐state sodium batteries, as a low‐cost, safe, and sustainable alternative to conventional lithium‐ion batteries. Recently, solid‐state electrolytes with a high ionic conductivity and low flammability have been developed. However, these still face challenges with the highly reactive sodium metal electrode. The study of these electrolyte‐electrode interfaces is challenging from a computational and experimental point of view, but recent advances in molecular dynamics neural‐network potentials are finally enabling access to these environments compared to more computationally expensive conventional ab‐initio techniques. In this study, heteroatom‐substituted Na3PS3X1 analogues, where X is sulfur, oxygen, selenium, tellurium, nitrogen, chlorine, and fluorine, are investigated using total‐trajectory analysis and neural‐network molecular dynamics. It was found that inductive electron‐withdrawing and electron‐donating effects, alongside differences in heteroatom atomic radius, electronegativity, and valency, influenced the electrolyte reactivity. The Na3PS3O1 oxygen analogue was found to have superior chemical stability against the sodium metal electrode, paving the way towards high‐performance, long lifetime and reliable rechargeable solid‐state sodium batteries.

What drives the heterogeneous interdiffusion in the Li-Si interfacial region of Si anodes: the Li flux or the Si flux?

“…Machine learning methods have been widely applied to investigate the thermodynamic and kinetic processes of battery materials [26][27][28][29][30][31][32][33][34] , including kinetic migrations, diffusion coefficients, and stabilities of crystalline and amorphous structures. Here, we employ deep potential molecular dynamic (DeePMD) simulations 35,36 to observe dynamics of Li-Si interdiffusion in different Li/Si interface models with aSi and cSi phases, and for the cSi case, we study (100), ( 110) and (111) facets exposed to the Li side.…”