Deciphering the vacancy-driven charge storage mechanism in dual-anion-modulated MoS(2−x)Sex/N-doped carbon microsphere for potassium-ion storage

초록

Multi-anionic transition metal dichalcogenides (TMDs) have emerged as promising electrode materials due to their tunable electronic structures and defect engineering capabilities. However, the limited morphological control and insufficient understanding of storage mechanisms in bulk-type TMDs have hindered their practical application. Herein, we report a rational design of Mo-based multi-anion TMDs featuring anion-vacancy-rich MoS(2−x)Sex nanoparticles embedded within sea-urchin-like N-doped carbon (NC) microspheres. A controlled colloidal assembly followed by a single-step thermal treatment with simultaneous S/Se incorporation enables the formation of abundant anion vacancies and a mechanically resilient, conductive architecture. Systematic characterizations confirm the nanoscale homogeneity and defect-rich nature of the composite. Electrochemical evaluations demonstrate that the MoS(2−x)Sex/NC anode delivers an outstanding reversible capacity of 338 mA h g−1 after 500 cycles at 0.5 A g−1, significantly outperforming its single-anion counterparts due to enhanced ion diffusion and structural stability. Notably, comprehensive in-situ and ex-situ X-ray-based analyses, including X-ray diffraction (XRD) and X-ray absorption spectroscopy (XAS), reveal a unique reaction mechanism characterized by vacancy-assisted potassium-ion diffusion through the basal planes. This unconventional pathway bypasses the sluggish, edge-dominated intercalation typically observed in bulk TMDs, thereby dramatically accelerating electrochemical reaction kinetics. This study underscores the synergistic effects of multi-anionic composition and defect engineering, providing a versatile design principle for high-performance potassium-ion battery anodes. © 2026 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by Elsevier B.V. and Science Press. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

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