Using single-atom catalysts for the
electrochemical reduction of
carbon dioxide is a promising method for excess renewable electricity
as chemical energy in fuels. In this study, we have investigated single
non-noble metal atoms supported on nitrogen-doped graphene (M-N4@Gr, where M = Fe, Co, Ni) as catalysts for the electrocatalytic
reduction of CO2 using first-principles density functional
theory and the computational hydrogen electrode model. The results
show that HCOOH is the preferred product of CO2 reduction
on the Ni-N4@Gr catalyst with an overpotential of 1.511
V, while Fe-N4@Gr and Co-N4@Gr prefer to reduce
CO2 to CH4 with the overpotential of 0.877 and
0.687 V, respectively. The computational results revealed that the
coordination environment affects d orbital occupations, leading to
a difference in the spin polarization of the systems and thus affecting
the performance and selectivity of catalysts. Our work may pave the
way for extending single non-noble atom catalysts, which consist of
earth-abundant elements, toward electrocatalytic CO2 reduction
reaction by regulating coordination environments.
Electrochemical reduction of CO2 to high-energy
chemicals
is a promising strategy for achieving carbon-neutral energy circulation.
However, designing high-performance electrocatalysts for the CO2 reduction reaction (CO2RR) remains a great challenge.
In this work, by means of density functional theory calculations,
we systematically investigate the transition metal (TM) anchored on
the nitrogen-doped graphene/graphdiyne heterostructure (TM-N4@GRA/GDY) as a single-atom catalyst for CO2 electroreduction
applications. The computational results show that Co–N4@GRA/GDY exhibits remarkable activity with a low limiting
potential of −0.567 V for the reduction of CO2 to
CH4. When the charged Co-N4@GRA/GDY system is
immersed in a continuum solvent, the reaction barrier decreases to
0.366 eV, which is ascribed to stronger electron transfer between
GDY and transition metal atoms in the GRA/GDY heterostructure. In
addition, the GRA/GDY heterostructure system significantly weakens
the linear scaling relationship between the adsorption free energy
of key CO2 reduction intermediates, which leads to a catalytic
activity that is higher than that of the single-GRA system and thus
greatly accelerates the CO2RR. The electronic structure
analysis reveals that the appropriate d−π interaction
will affect the d orbital electron distribution, which is directly
relevant to the selectivity and activity of catalysis. We hope these
computational results not only provide a potential electrocatalyst
candidate but also open up an avenue for improving the catalytic performance
for efficient electrochemical CO2RR.
Salp swarm algorithm (SSA) is a relatively new and straightforward swarm-based meta-heuristic optimization algorithm, which is inspired by the flocking behavior of salps when foraging and navigating in oceans. Although SSA is very competitive, it suffers from some limitations including unbalanced exploration and exploitation operation, slow convergence. Therefore, this study presents an improved version of SSA, called OOSSA, to enhance the comprehensive performance of the basic method. In preference, a new opposition-based learning strategy based on optical lens imaging principle is proposed, and combined with the orthogonal experimental design, an orthogonal lens opposition-based learning technique is designed to help the population jump out of a local optimum. Next, the scheme of adaptively adjusting the number of leaders is embraced to boost the global exploration capability and improve the convergence speed. Also, a dynamic learning strategy is applied to the canonical methodology to improve the exploitation capability. To confirm the efficacy of the proposed OOSSA, this paper uses 26 standard mathematical optimization functions with various features to test the method. Alongside, the performance of the proposed methodology is validated by Wilcoxon signed-rank and Friedman statistical tests. Additionally, three well-known engineering optimization problems and unknown parameters extraction issue of photovoltaic model are applied to check the ability of the OOSA algorithm to obtain solutions to intractable real-world problems. The experimental results reveal that the developed OOSSA is significantly superior to the standard SSA, currently popular SSA-based algorithms, and other state-of-the-artmetaheuristic algorithms for solving numerical optimization, real-world engineering optimization, and photovoltaic model parameter extraction problems. Finally, an OOSSA-based path planning approach is developed for creating the shortest obstacle-free route for autonomous mobile robots. Our introduced method is compared with several successful swarm-based metaheuristic techniques in five maps, and the comparative results indicate that the suggested approach can generate the shortest collision-free trajectory as compared to other peers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.