Strain and defect engineering of graphene for hydrogen storage via atomistic modelling

Kag, Deepak; Luhadiya, Nitin; Patil, Nagesh D.; Kundalwal, S. I.

doi:10.1016/j.ijhydene.2021.04.098

Cited by 39 publications

(9 citation statements)

References 61 publications

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“…AIREBO potential has been widely used to study the mechanical behavior of defectcontaining graphene [18,[33][34][35]. A timestep of 1 femtosecond was used.…”

Section: Molecular Dynamics Simulation Of Defect-containing Graphenementioning

confidence: 99%

“…where E is the total system energy; E REBO , E LJ , and E tors are energy components corresponding to the REBO (short-ranged), Lennard-Jones (long-ranged), and torsional potentials. AIREBO potential has been widely used to study the mechanical behavior of defectcontaining graphene [18,[33][34][35]. A timestep of 1 femtosecond was used.…”

Section: Molecular Dynamics Simulation Of Defect-containing Graphenementioning

confidence: 99%

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Scalable Graphene Defect Prediction Using Transferable Learning

Zheng

2021

Nanomaterials

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Notably known for its extraordinary thermal and mechanical properties, graphene is a favorable building block in various cutting-edge technologies such as flexible electronics and supercapacitors. However, the almost inevitable existence of defects severely compromises the properties of graphene, and defect prediction is a difficult, yet important, task. Emerging machine learning approaches offer opportunities to predict target properties such as defect distribution by exploiting readily available data, without incurring much experimental cost. Most previous machine learning techniques require the size of training data and predicted material systems of interest to be identical. This limits their broader application, because in practice a newly encountered material system may have a different size compared with the previously observed ones. In this paper, we develop a transferable learning approach for graphene defect prediction, which can be used on graphene with various sizes or shapes not seen in the training data. The proposed approach employs logistic regression and utilizes data on local vibrational energy distributions of small graphene from molecular dynamics simulations, in the hopes that vibrational energy distributions can reflect local structural anomalies. The results show that our machine learning model, trained only with data on smaller graphene, can achieve up to 80% prediction accuracy of defects in larger graphene under different practical metrics. The present research sheds light on scalable graphene defect prediction and opens doors for data-driven defect detection for a broad range of two-dimensional materials.

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“…AIREBO potential has been widely used to study the mechanical behavior of defectcontaining graphene [18,[33][34][35]. A timestep of 1 femtosecond was used.…”

Section: Molecular Dynamics Simulation Of Defect-containing Graphenementioning

confidence: 99%

Section: Molecular Dynamics Simulation Of Defect-containing Graphenementioning

confidence: 99%

Scalable Graphene Defect Prediction Using Transferable Learning

Zheng

2021

Nanomaterials

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“…However, due to the small size and gaseous nature of H 2 , efficient storage of H 2 is the main bottleneck for its practical applications. Recently, H 2 storage on surfaces of nanostructures such as graphene was suggested by adsorption of H 2 on their surfaces, which improves the quality by overcoming a high operating temperature and slow kinetics. − It has been found that transition metal atoms are decorated on carbon nanomaterials to store H 2 on a molecular form of H 2 because H 2 can be adsorbed on the metal atoms through the hybridization of metal “d” orbitals with H 2 “σ” and “σ*” orbitals, which is called Kubas-type H 2 storage materials. − …”

Section: Introductionmentioning

confidence: 99%

Calcium-Decorated Polygon-Graphenes for Hydrogen Storage

Kim

et al. 2023

ACS Appl. Energy Mater.

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We performed first-principles calculations to investigate hydrogen (H2) storage properties of bare and calcium (Ca)-decorated polygon-graphenes, i.e., biphenylene and ψ-graphene monolayers consisting of polygons, from tetragons to octagons. In pristine forms, both biphenylene and ψ-graphene bind H2 weakly. However, upon Ca doping, biphenylene adsorbed up to five H2 molecules regardless of polygonal sites, whereas ψ-graphene anchored up to six and five H2 molecules to pentagonal and heptagonal sites, respectively. In all the cases, the H2 binding energy was ∼0.30 eV, enabling reversible room-temperature H2 storage. The H2 storage capacity can reach ∼6.8 and ∼4.2 wt % for Ca-decorated biphenylene and ψ-graphene, respectively. Using equilibrium thermodynamics, we showed the adsorption and desorption of H2 at 300 and 380 K under ambient pressure, respectively. This clearly indicates that Ca-decorated 2D sp2 carbon sheets with polygons (biphenylene, ψ-graphene) could be used as promising H2 storage materials.

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“…3 The resulting material may be suitable for catalytic reactions, 4,5 and turns out to be a good candidate for hydrogen storage. [6][7][8][9] Experimental and theoretical work indicates that a defect-free graphene sheet is impermeable to gases, even to small atoms such as He or H. [10][11][12] For atomic H, in particular, the energy barrier for crossing a perfect graphene layer is about 4 eV, 11,13 which makes permeation highly unlikely. Sun et al 12 have recently suggested that the effective barrier for this dynamic process could be lower, thus favoring jumping of atomic hydrogen from one side of the sheet to the other.…”

Section: Introductionmentioning

confidence: 99%

Hydrogen dynamics on defective monolayer graphene

Herrero,

Verges,

Ramirez

2022

Preprint

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The hydrogen dynamics on a graphene sheet is studied in the presence of carbon vacancies. We analyze the motion of atomic H by means of molecular dynamics (MD) simulations, using a tightbinding Hamiltonian fitted to density-functional calculations. Hydrogen passivates the dangling bonds of C atoms close to a vacancy, forming C-H bonds with H located at one or the other side of the layer plane. The hydrogen dynamics has been studied from statistical analysis of MD trajectories, along with the autocorrelation function of the atomic coordinates. For a single H atom, we find an effective barrier of 0.40 eV for crossing the graphene layer, with a jump rate ν = 2 × 10 6 s −1 at 300 K. The atomic jumps behave as stochastic events, and their number for a given temperature and time interval follows a Poisson probability distribution. For two H atoms close to a vacancy, strong correlations in the atomic dynamics are found, with a lower jump frequency ν = 7 × 10 2 s −1 at room temperature. These results provide insight into the diffusion mechanisms of hydrogen on graphene, paving the way for a complete understanding of its motion through defective crystalline membranes.

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Strain and defect engineering of graphene for hydrogen storage via atomistic modelling

Cited by 39 publications

References 61 publications

Scalable Graphene Defect Prediction Using Transferable Learning

Scalable Graphene Defect Prediction Using Transferable Learning

Calcium-Decorated Polygon-Graphenes for Hydrogen Storage

Hydrogen dynamics on defective monolayer graphene

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