2026-10-14T04:30:03Z https://www.scidb.cn/oai

10.57760/sciencedb.j00213.00063 2024-12-16T14:07:00Z

2024-12-16 Theoretical prediction of nitrogen-oxygen-anchored monatomic copper-doped graphene as an anode for alkaline ion batteries doi:10.57760/sciencedb.j00213.00063 en This dataset, derived from Density Functional Theory (DFT) calculations, presents a comprehensive theoretical analysis of the potential of nitrogen and oxygen anchored single-atom copper-doped graphene (Cu/NO2G) as an anode material for alkali-ion batteries. Calculations encompass geometric optimization of physical configurations and their energy storage properties, including geometric and electronic structures, adsorption energies, average open-circuit voltages, theoretical capacities, and diffusion barriers. All calculations were performed using the Vienna Ab initio Simulation Package (VASP) software (licensed), employing the Local Density Approximation (LDA) with the Ceperly-Alder functional parameterized by Perdew and Zunger for the exchange-correlation potential. To ensure accuracy, the plane-wave cutoff energy (Ecut) was set to 520 eV, and Brillouin zone sampling was conducted using the Monkhorst-Pack scheme with a 3×3×1 k-point grid for structural optimization and a 5×5×1 grid for electronic structure calculations. Convergence criteria for total energy and ionic forces were 10-5 eV and 10-3 eV/Å, respectively. Phonon dispersion curves were calculated using the phonopy software package, and migration barriers were determined using the Climbing Image Nudged Elastic Band (CI-NEB) method. While inherent limitations of DFT calculations preclude precise error quantification, the employed methodologies are widely accepted in the field and provide valuable qualitative and semi-quantitative insights.The dataset comprises the following files: Figure 1(a)(b).txt contains the optimized crystal structure data for Cu/NO2G; Figure 1(c)-Phonon dispersion curves.csv contains the phonon dispersion data; Figure 1(d)-AIMD.csv contains the Ab Initio Molecular Dynamics (AIMD) simulation data; Figure 2(a).pdf illustrates the adsorption sites on Cu/NO2G and the most stable adsorption configurations of metal atoms on A and B surfaces; Figures 2(b).vasp, 2(c).vasp, and 2(d).vasp represent the most stable adsorption configurations of Li, Na, and K on the A surface, respectively; Figures 2(e).vasp, 2(f).vasp, and 2(g).vasp represent the most stable adsorption configurations of Li, Na, and K on the B surface, respectively; Figures 3(a)-CHGDIFF-Li.vasp, 3(b)-CHGDIFF-Na.vasp, and 3(c)-CHGDIFF-K.vasp represent the charge difference density data for Li, Na, and K adsorbed on the B surface of Cu/NO2G (viewable with VESTA); Figure 4(a).csv contains the density of states (DOS) data for Cu/NO2G; Figure 4(b).csv contains the projected density of states (PDOS) data for C and Cu in Cu/NO2G; Figure 4(c).csv contains the PDOS data for N and O in Cu/NO2G; Figures 4(d).csv, 4(e).csv, and 4(f).csv contain the DOS data for Cu/NO2G with adsorbed Li, Na, and K, respectively; Figure 5.csv contains the average open-circuit voltage (Vocv) data for Cu/NO2G with varying concentrations of Li, Na, and K; Figure 6.csv contains the theoretical capacity data for Cu/NO2G compared to other 2D materials; Figure 7(a)(b)-migration path.pdf illustrates the migration pathways of Li, Na, and K on the A and B surfaces of Cu/NO2G; Figure 7(c).csv contains the diffusion barrier data for Li, Na, and K on the A surface of Cu/NO2G; and Figure 7(d).csv contains the diffusion barrier data for Li, Na, and K on the B surface of Cu/NO2G.This dataset offers profound implications for physics research, particularly in condensed matter physics and materials science, by providing atomic-level insights into electrochemical processes within alkali-ion batteries. It furnishes a theoretical foundation for the design and optimization of novel electrode materials, especially 2D materials like Cu/NO2G, by offering key parameters such as adsorption energies, diffusion barriers, open-circuit voltages, and theoretical capacities. Moreover, it serves as a valuable benchmark for validating and refining computational methods like DFT, ultimately advancing the development of next-generation energy storage devices and deepening our understanding of material properties at the atomic scale. Keywords：First-Principles; Ion Batteries; Graphene Hu Jun-Ping Liang Si-Si Duan Hui-Xian Tian Jun-Cheng Chen Shuo Dai Bo-Yang Huang Chun-Lai Liu Yu Lv Ying Wan Li-Jia Ouyang Chu-Ying PUBLIC https://creativecommons.org/licenses/by-nc/4.0/ dataset http://www.doi.org/10.7498/aps.74.20241461 Science Data Bank