Enhanced CO 2 Reduction on a Cu-Decorated Single-Atom Catalyst via an Inverse Sandwich M-Graphene-Cu Structure.
Jingnan SuLinke YuBing HanFengyu LiZhongfang ChenXiao Cheng ZengPublished in: The journal of physical chemistry letters (2024)
The highly active and selective electrochemical CO 2 reduction reaction (CO 2 RR) can be exploited to produce valuable chemicals and fuels and is also crucial for achieving clean energy goals and environmental remediation. Decorated single-atom catalysts (D-SACs), which feature synergistic interactions between the active metal site (M) and an axially decorated ligand, have been extensively explored for the CO 2 RR. Very recently, novel double-atom catalysts (DACs) featuring inverse sandwich structures were theoretically proposed and identified as promising CO 2 RR electrocatalysts. However, the experimental synthesis of DACs remains a challenge. To facilitate the fabrication and to realize the potential of these novel DACs, we designed a D-SAC system, denoted as M 1 @gra+Cu slab . This system features a graphene layer with a vacancy-anchored SAC, all stacked on a Cu(111) surface, thereby embodying a Cu slab-supported inverse sandwich M-graphene-Cu structure. Using density functional theory calculations, we evaluated the stability, selectivity, and activity of 27 M 1 @gra+Cu slab systems (M = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Y, Zr, Nb, Mo, Ru, Rh, Pd, Ag, Cd, Hf, Ta, W, Re, Os, Ir, Pt, or Au) and showed five M 1 @gra+Cu slab (M = Co, Ni, Cu, Rh, or Pd) systems exhibit optimal characteristics for the CO 2 RR and can potentially outperform their SAC and DAC counterparts. This study offers a new strategy for developing highly efficient CO 2 RR D-SACs with an inverse sandwich structural moiety.
Keyphrases
- metal organic framework
- highly efficient
- aqueous solution
- density functional theory
- molecular dynamics
- reduced graphene oxide
- room temperature
- quantum dots
- gold nanoparticles
- public health
- heart failure
- multidrug resistant
- sensitive detection
- cancer therapy
- deep learning
- climate change
- electron transfer
- label free
- pet imaging