Diverse nitrogen-doped 2D layered mesoporous MoS2/reduced graphene oxide composites with superior structural features for enhancing the performance of lithium ion batteries
Source abstract
Abstract
Abstract In this paper, we report the construction of nitrogen-doped 2D layered mesoporous molybdenum disulfide/reduced graphene oxide (meso-N-MoS2/rGO) hybrids via a nanocasting method using ammonium molybdate as molybdenum source, thiourea as sulfur source, and nitrogen modified mesoporous KIT-6/rGO as template. The as-prepared meso-N-MoS2/rGO is constituted of homogeneously distributed ultrasmall few-layer MoS2 nanoparticles composited with N-modified graphene nanosheets and possesses a large surface area with high porosity. When being used as anode materials for Li ion batteries (LIBs), the obtained meso-N-MoS2/rGO hybrid exhibits prominent electrochemical properties in terms of high specific capacity (1195.1 mA h g−1 at 100 mA g−1) and superior rate performance (606.2 mA h g−1 at 1000 mA g−1). The discharge capacity still maintains 872.8 mA h g−1 even after 50 cycles. Such an intriguing electrochemical performance is mainly attributed to its high porosity, large surface area, strong coupling effect between uniform N-doped MoS2 and N-doped graphene since the N-doping and small size of MoS2 nanoparticles can set up conductive bridges, abundant pyridine nitrogen, and excellent conductivity. In addition, the layered graphene provides conductive networks, which ensure that lithium ions and electrons have a shorter transmission distance, thereby increasing their ion diffusion rate and ease the charge and discharge process in the volume effect. This work provides an effective strategy to synthesize N-doped meso-MoS2/rGO hybrids to achieve significantly enhanced electrochemical performance.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advancements in Battery Materials · Supercapacitor Materials and Fabrication · MXene and MAX Phase Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (69)
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