Computational Study on Two-Dimensional Transition Metal Borides for Enhanced Lithium-Sulfur Battery Performance: Insights on Anchoring, Catalytic Activity, and Solvation Effects
JOURNAL OF COLLOID AND INTERFACE SCIENCE（2025）
Wuhan Univ Technol
Abstract
The controlled modulation of surface functional groups, in conjunction with the intrinsic structural characteristics of MXene materials, shows great potential in alleviating the shuttle effect and improving the sluggish reaction kinetics in lithium-sulfur batteries (LSBs). This study delves into the impact of surface functional groups (T = O, S, F, and Cl) on V2B2 MBene concerning sulfur immobilization and kinetic catalytic properties through meticulous first-principles calculations. The results reveal that the establishment of T-Li bonds within V2B2T2 (T = O, S, F, and Cl) enhances the adsorption of lithium polysulfides (LiPSs). Moreover, the robust interactions between the T_p and V_d orbitals play a pivotal role in strengthening the T-V bond and reducing the energy barrier for Li2S decomposition. Comparative analyses underscore the outstanding performance of V2B2O2, showcasing a moderate adsorption strength for LiPSs, remarkable electrocatalytic activity for Li2S decomposition (with an energy barrier of 0.42 eV), and a low Li2S diffusion barrier (0.16 eV). These attributes facilitate effective anchoring and expedite reaction kinetics for LiPSs. Furthermore, the influences of solvation and temperature were found to have substantial impacts on the anchoring capability of V2B2T2 except for V2B2O2. This study establishes a critical theoretical framework and serves as a valuable reference for advancing MBene materials as cathodes for LSBs.
MoreTranslated text
Key words
MXenes,Lithium-sulfur batteries,Adsorption,Catalysis,Solvent effects,Temperature correction
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- 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
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined