Potential Application of Two-Dimensional PC6 Monolayer As an Anode Material in Alkali Metal-Ion (li, Na, K) Batteries

Minrui Yang,Fan Kong,Lei Chen,Binwei Tian,Jiyuan Guo

Thin Solid Films（2023）SCI 3区SCI 4区

Jiangsu Univ Sci & Technol

Cited 8|Views14

Abstract

The development of two-dimensional (2D) materials in anode has been believed as a significant research di- rection to promote the innovation of alkali metal-ion batteries. Lately, PC6 monolayer, a neoteric material, has stimulated people's research enthusiasm owing to its outstanding thermodynamic and electrical natures. In this paper, by employing the first-principles calculation method based on density function theory, the feasibility of PC6 monolayer acting as an anode in alkali metal-ion batteries has been systematically evaluated. The simulation results indicate that, when acting as anode material, the PC6 monolayer can not only provide effective adsorption interaction for metal ions which is beneficial for inhibiting the formation of clusters, but also can furnish satisfactory storage capacity (1496.23 mA center dot h center dot g-1 for Li, 1301.07 mA center dot h center dot g-1 for Na, and 780.63 mA center dot h center dot g-1 for K). Meanwhile, PC6 monolayer can supply appropriate average open-circuit voltages (0.48/0.40/0.64 V for Li/Na/ K), which are all within the ideal voltage range of anode material. Importantly, the appraised diffusion behaviors further reflect the isotropic property and wonderful migration performance (0.42/0.46/0.29 eV for Li/Na/K) of PC6 monolayer for metal ions. Remarkedly, the ab initio molecular dynamics simulations results illustrate that these saturated adsorption systems have excellent deformation resistance at a high temperature of 400 K. These exciting performances endow the PC6 monolayer with promising application prospects as an anode in alkali metal-ion batteries.

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Key words

PC6 monolayer,Alkali metal ion batteries,High storage capacity,First-principles calculation

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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