journal article·2022-11

High synergetic transition metal phosphides@ nitrogen doped porous carbon nanosheets hybrids derived from silk fibroin and phytic acid self-assembly for ultra-high performance lithium storage

Zhoujia Liu, Peng Yan, Yixuan Bai, Zhiqian Zhang, Xiaogang Li, Weiwei Xiong, Junhao Zhang, Aihua Yuan, Fenfen Zheng

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Abstract

Transition metal phosphides (TMPs) have received widespread attention due to their high theoretical specific capacity and fast reaction kinetics. However, their poor conductivity and volume changes during the cycle limited their applications. Herein, we explore a facile and cost-effective way to prepare core–shell-like transition metal phosphides (TMPs: CoP, Ni 2P, Cu 3 P) nanoparticles embedded into nitrogen doped porous carbon nanosheets (denoted as CoP@NDPCS, Ni 2P@NDPCS, Cu 3 P@NDPCS) via biological material-driven assembly and self-template strategy. By self-assembled process, the amino and carbonyl groups in silk fibroin can chelate with metal ions, and then phytic acid added undergoes a coordination reaction with the metal components. Subsequently, core-shell TMPs nanoparticles supported by nitrogen doped porous carbon nanosheets were prepared by a one-step high-temperature calcination process. The obtained unique hybrid structure material with enough vacancy space to alleviate volume expansion exhibits ultra-high specific capacity and excellent cycling performance for the anode of lithium-ion batteries. The CoP@NDPCS, Ni 2P@NDPCS and Cu 3P@NDPCS electrodes exhibit the reversible capacities of 1014.2, 486.5 and 877 mAh g −1 at the current density of 500 mA g -1, respectively. The remarkable lithium storage performance of TMP@NDPCS indicates that a reasonable structure designed by biological material-driven is an effective and promising method to enhance the lithium storage of transition metal phosphides.
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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.

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