Stable Crystalline Forms of Na Polysulfides: Experiment versus Ab Initio Computational Prediction

Area of Science:

• Materials Science
• Electrochemistry
• Computational Chemistry

Background:

• Stable crystalline polysulfides are crucial for understanding and designing light-metal-sulfur batteries.
• Accurate structural and stability data of sodium polysulfides are needed.

Purpose of the Study:

• To investigate the stability of sodium polysulfides using experimental and computational methods.
• To evaluate the effectiveness of ab initio crystal structure prediction and NMR spectroscopy for analyzing these materials.

Main Methods:

• Ab initio crystal structure prediction using an evolutionary algorithm.
• Experimental studies employing X-ray diffraction and 23Na solid-state NMR spectroscopy.
• Calculation of isotropic chemical shifts and quadrupolar coupling constants for NMR data interpretation.

Main Results:

• The evolutionary algorithm successfully predicted thermodynamically stable crystalline forms of sodium polysulfides with small unit cells.
• For larger unit cells, the algorithm identified favorable structural motifs (short, unbranched polysulfide chains) but struggled with complex 3D structures.
• X-ray diffraction and NMR spectroscopy provided experimental validation and detailed analysis of sodium polysulfide samples.
• NMR spectroscopy, supported by theoretical calculations, proved adept at distinguishing and quantifying different components.

Conclusions:

• Computational structure prediction is a valuable tool for identifying stable sodium polysulfide structures, particularly for simpler systems.
• NMR spectroscopy is an excellent method for the characterization and quantification of sodium polysulfides in battery materials.
• Combined experimental and computational approaches enhance the understanding of materials critical for advanced battery technologies.