Login / Signup

Engineering Rich Active Sites and Efficient Water Dissociation for Ni-Doped MoS 2 /CoS 2 Hierarchical Structures toward Excellent Alkaline Hydrogen Evolution.

Mian YangYu-Xin GuoZhan LiuXiao-Yun LiQing HuangXiao-Yu YangCui-Fang YeYu LiJin-Ping LiuLi-Hua ChenBao-Lian SuYi-Long Wang
Published in: Langmuir : the ACS journal of surfaces and colloids (2022)
Besides improving charge transfer, there are two key factors, such as increasing active sites and promoting water dissociation, to be deeply investigated to realize high-performance MoS 2 -based electrocatalysts in alkaline hydrogen evolution reaction (HER). Herein, we have demonstrated the synergistic engineering to realize rich unsaturated sulfur atoms and activated O-H bonds toward the water for Ni-doped MoS 2 /CoS 2 hierarchical structures by an approach to Ni doping coupled with in situ sulfurizing for excellent alkaline HER. In this work, the Ni-doped atoms are evolved into Ni(OH) 2 during alkaline HER. Interestingly, the extra unsaturated sulfur atoms will be modulated into MoS 2 nanosheets by breaking Ni-S bonds during the formation of Ni(OH) 2 . On the other hand, the higher the mass of the Ni precursor ( m Ni ) for the fabrication of our samples, the more Ni(OH) 2 is evolved, indicating a stronger ability for water dissociation of our samples during alkaline HER. Our results further reveal that regulating m Ni is crucial to the HER activity of the as-synthesized samples. By regulating m Ni to 0.300 g, a balance between increasing active sites and promoting water dissociation is achieved for the Ni-doped MoS 2 /CoS 2 samples to boost alkaline HER. Consequently, the optimal samples present the highest HER activity among all counterparts, accompanied by reliable long-term stability. This work will promise important applications in the field of electrocatalytic hydrogen evolution in alkaline environments.
Keyphrases
  • transition metal
  • metal organic framework
  • quantum dots
  • highly efficient
  • visible light
  • high resolution
  • machine learning
  • cancer therapy
  • tissue engineering
  • atomic force microscopy