Interlayer vacancy defects in AA-stacked bilayer graphene: density functional theory predictions

Vuong, Amanda; Trevethan, Thomas; Latham, Chris; Ewels, Christopher P.; Erbahar, Doğan; Briddon, P.R.; Rayson, Mark; Heggie, M. I.

doi:10.1088/1361-648x/aa5f93

Cited by 14 publications

(10 citation statements)

References 54 publications

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“…According to TST, Li migrates from an initial site to its adjacent vacancy by experiencing a transition state, which has an energy barrier E m separating the two corresponding states before and after migration. Li migration follows the general KMC procedures: (1) Determine all possible migration sites. (2) Identify a series of the transition rates (p i ) for all possible migration states according to the transition rate defined as [55,56], 𝑝 i = 𝑣 * exp(−𝐸 m /𝑘 B 𝑇), where 𝑣 * is the jump attempt frequency, which is taken to be 1 × 10 13 s −1 [12,33,57].…”

Section: ⅱ Methodsmentioning

confidence: 99%

Adsorption and ultrafast diffusion of lithium in bilayer graphene:Ab initioand kinetic Monte Carlo simulation study

Zhong

et al. 2019

Phys. Rev. B

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In this work, we adopt first-principle calculations based on density functional theory and Kinetic Monte Carlo simulations to investigate the adsorption and diffusion of lithium in bilayer graphene (BLG) as anodes in lithium-ion batteries. Based on energy barriers directly obtained from firstprinciple calculations for single-Li and two-Li intercalated BLG, a new equation was deduced for predicting energy barriers considering Lis interactions for multi-Li intercalated BLG. Our calculated results indicate that Li energetically prefers to intercalate within rather than adsorb outside the bilayer graphene. Additionally, lithium exists in cationic state in the bilayer graphene.More excitingly, ultrafast Li diffusion coefficient (~10 −5 cm 2 s −1 ) within AB-stacked BLG near room temperature was obtained, which reproduces the ultrafast Li diffusion coefficient measured in recent experiment. However, ultrafast Li diffusion was not found within AA-stacked BLG near room temperature. The analyses of potential distribution indicate that the stacking structure of BLG greatly affects its height of potential well within BLG, which directly leads to the large difference in Li diffusion. Furthermore, it is found that both the interaction among Li ions and the stacking structure cause Li diffusion within AB-stacked BLG to exhibit directional preference. Finally, the temperature dependence of Li diffusion is described by the Arrhenius law. These findings suggest that the stacking structure of BLG has an important influence on Li diffusion within BLG, and changing the stacking structure of BLG is one possible way to greatly improve Li diffusion rate within BLG. At last, it is suggested that AB-stacked BLG can be an excellent candidate for anode material in Lithium-ion batteries. Ⅰ. INTRODUCTIONThe performance of Lithium-ion batteries (LIBs) relies predominantly on the material properties of the electrodes. Advanced electrodes not only require high storage capacity but also require ultrafast charge/discharge rates, which are characterized by Lithium-ion diffusion coefficient. Carbon-based materials are at present the most commonly used negative electrode in LIBs. There are many studies on the structure of carbon atoms layer [1][2][3], and lithium adsorption in carbon-based materials [4][5][6][7][8].Experimental results have demonstrated that graphene nanosheets have a good cyclic performance, and possess capacity up to 460 mA h g -1 after 100 cycles [9]. Due to its high lithium storage capacity, high conductivity, and good mechanical flexibility, graphene has been regarded as a suitable candidate for electrode in LIBs [10,11]. However, the reported experimental Li diffusion coefficients span a very wide range, for example, from 10 −16 cm 2 s −1 to 10 −6 cm 2 s −1 for a variety of compositegraphite electrode architectures [12][13][14][15][16][17][18][19][20]. What affects Li diffusion? How to accelerate Li diffusion?These issues are not currently addressed. Therefore, in order to optimize the performance of negative electrode for LI...

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Section: ⅱ Methodsmentioning

confidence: 99%

Adsorption and ultrafast diffusion of lithium in bilayer graphene:Ab initioand kinetic Monte Carlo simulation study

Zhong

et al. 2019

Phys. Rev. B

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“…[111] Apart from predicting metal storage capacity, DFT simulations have also been applied to understand the intercalation mechanism, and stability of the carbon structures upon intercalation, using periodic graphite models with two (bilayer model) to four graphene layers. [182][183][184][185][186] Metal intercalation mechanism in graphite (or few-layer graphene model) have been studied for various metal concentrations (Figure 4). Computational studies of GICs have been widely focused on the difference in intercalation mechanism and stability of Li-GICs, Na-GICs, and K-GICs.…”

Section: Metal-ion Intercalation and Storage In Graphite Anodesmentioning

confidence: 99%

Atomic‐Scale Design of Anode Materials for Alkali Metal (Li/Na/K)‐Ion Batteries: Progress and Perspectives

Olsson

Zhang

et al. 2022

Advanced Energy Materials

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a variety of renewable energy technologies (based on solar, hydro, wind, geothermal power, etc.), 2) provide power sources for electric and hybrid vehicles for low-carbon or zero-carbon emissions of transportation systems, [3,4] and 3) provide power sources for various portable and wearable electronic devices. Alkali metal (AM) ion batteries (AMIBs) including lithium (Li)-ion batteries (LIBs), sodium (Na)-ion batteries (NIBs), and potassium (K)-ion batteries (KIBs) are important rechargeable battery technologies to support the decarbonization of both electricity supply and transportation systems. Currently dominating the rechargeable battery market is LIBs. NIBs and KIBs are developed as more sustainable alternatives and indeed complements, to mitigate challenges associated with the limited and geopolitically isolated Li resources. [1] Post-alkali metal ion batteries are also pursued such as multivalent ion batteries and lithium sulfur batteries. These battery technologies will not be covered in this review, but have been reviewed elsewhere. [5][6][7][8][9][10][11] The three AMIBs follow the same general cell operation, with variations in material selections (Figure 1). [12][13][14] During charge/discharge the AM ions move via an electrolyte between the cathode (positive electrode) and the anode (negative The development and optimization of high-performance anode materials for alkali metal ion batteries is crucial for the green energy evolution. Atomic scale computational modeling such as density functional theory and molecular dynamics allows for efficient and adventurous materials design from the nanoscale, and have emerged as invaluable tools. Computational modeling cannot only provide fundamental insight, but also present input for multiscale models and experimental synthesis, often where quantities cannot readily be obtained by other means. In this review, an overview of three main anode classes; alloying, conversion, and intercalation-type anodes, is provided and how atomic scale modeling is used to understand and optimize these materials for applications in lithium-, sodium-, and potassium-ion batteries. In the last part of this review, a novel type of anode materials that are largely predicted from density functional theory simulations is presented. These 2D materials are currently in their early stages of development and are only expected to gain in importance in the years to come, both within the battery field and beyond, highlighting the ability of atomic scale materials design.

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“…For instance, a moiré pattern has been observed due to the interaction between the layers for twisted BLG [18] with extraordinary optical properties found [19,20,21]. Furthermore, a relatively weak strength of interlayer interaction, and low energetic cost of relative translation of the layers, has been seen for AA-stacking BLG [22]. Compared with other stacking structures, AA-and twistedstacking, very strong coupling exists between the two layers of AB-stacked BLG, which has the lowest energy and the most stable structure among the different orientations [23].…”

Section: Introductionmentioning

confidence: 99%

Conversion of the stacking orientation of bilayer graphene due to \break the interaction of BN-dopants

Abdullah,

Rashid,

Tang

et al. 2021

Preprint

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A conversion of AA-to AB-stacking bilayer graphene (BLG) due to interlayer interaction is demonstrated. Two types of interlayer interactions, an attractive and a repulsive, between the Boron and Nitrogen dopant atoms in BLG are found. In the presence of the attractive interaction, an AA-stacking of BN-codoped BLG is formed with a less stable structure leading to weak mechanical properties of the system. Low values of the Young modulus, the ultimate strength and stress, and the fracture strength are observed comparing to a pure BLG. In addition, the attractive interaction induces a small bandgap that deteriorates the thermal and optical properties of the system. In contrast, in the presence of a repulsive interaction between the B and N atoms, the AA-stacking is converted to a AB-stacking with a more stable structure. Improved mechanical properties such as higher Young modulus, the ultimate strength and stress, fracture strength are obtained comparing to the AA-stacked BN-codoped BLG. Furthermore, a larger bandgap of the AB-stacked bilayer enhances the thermal and the optical characteristics of the system.

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Interlayer vacancy defects in AA-stacked bilayer graphene: density functional theory predictions

Cited by 14 publications

References 54 publications

Adsorption and ultrafast diffusion of lithium in bilayer graphene:Ab initioand kinetic Monte Carlo simulation study

Adsorption and ultrafast diffusion of lithium in bilayer graphene:Ab initioand kinetic Monte Carlo simulation study

Atomic‐Scale Design of Anode Materials for Alkali Metal (Li/Na/K)‐Ion Batteries: Progress and Perspectives

Conversion of the stacking orientation of bilayer graphene due to \break the interaction of BN-dopants

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