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Theoretical Simulation Of The Catalytic Reaction Mechanism Of Graphene Catalyst Surface In Lithium Air Battery

Posted on:2023-12-10Degree:MasterType:Thesis
Country:ChinaCandidate:L ZhouFull Text:PDF
GTID:2531307103983409Subject:Chemistry
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As society progresses,the demand for high-capacity energy storage systems such as electric vehicles is increasing,and non-aqueous lithium-air batteries are considered to have very high potential application prospects due to their extremely high energy density.However,there are still a large number of problems that need to be solved,such as the inability to achieve the theoretical specific capacity,low charge and discharge rates and poor cycle life of lithium-air batteries.In recent years,researchers have realised that the large overpotential caused by the slow OER and ORR kinetics of this battery during charge and discharge is one of the main causes of these problems.In order to find a cathode catalyst that can effectively reduce the overpotential,this paper adopts first-principles calculations and develops theoretical simulations to investigate the mechanism of the initial ORR reaction on the surface of transition metal and nitrogen atom-doped graphene and the factors influencing the catalytic activity.The first part of the research:The MN4-Gr(M=Mn,Fe,Co,Ni,Cu,Zn)model was constructed as a cathode catalyst for Li-air batteries and the ORR reaction mechanism and pathway were investigated.Based on the magnitude of the overpotential,we obtained that Fe and ZnN4-Gr favoured the 2 e-mechanism;Ni and Cu N4-Gr favoured the 4 e-mechanism and had high catalytic activity;while Mn and Co N4-Gr had no significant preference for either mechanism.By analysing the adsorption energy of the intermediate,it was found that Ni and Cu N4-Gr favoured the4 e-mechanism because the adsorption energy of Li O2is relatively small,and the smaller the electron absorption capacity of O in the adsorbate,the smaller the adsorption energy.It is therefore hypothesised that the adsorption energy of the intermediate Li O2has an influence on which mechanism the ORR process follows for this catalyst.The second part of the research:The ORR processes for the generation of two molecules of Li2O2on the surfaces of graphene,nitrogen-doped graphene and Cu N2C2graphene catalysts were first calculated and it was found that the O-O bonds in the adsorbates were all broken and the magnitude of the overpotential did not coincide with the experimental values.The ORR process was then repeated to produce two molecules of Li2O2on the first lithium oxide layer,and it was found that none of the O-O bonds were broken during this process,and there was also a significant reduction in the cell charging and sparing potential under all three catalysts,i.e.demonstrating that the experimentally observed Li2O2was at least in the second layer on the catalyst surface,rather than acting directly on the catalyst surface.The third part of the research:A TM1-TM2-N6defect was constructed in graphene as the active centre of the catalyst.The binding energies of the selected diatomic metal combinations were first calculated and kinetic simulations were done for four of the groups;then the 2 e-mechanism was followed to simulate the ORR process and 39 groups of diatomic metal energies were calculated.A linear relationship between the adsorption energy and the catalyst performance was found to exist.A gradient boosting tree algorithm was also used to obtain descriptors with a strong influence on the overpotential,which was then used as a screening condition to validate two diatomic metal combinations to demonstrate the accuracy of the descriptors.This resulted in the selection of diatomic metal catalysts that were effective in reducing the charging potential.This paper investigates the ORR reaction mechanism on the surface of single/double transition metal-nitrogen-doped graphene and screens cathode catalysts that can reduce the overpotential,which will help to design and develop lithium-air batteries with better performance.
Keywords/Search Tags:Lithium-air batteries, Graphene, First principles, Oxygen reduction reactions, Machine learning
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