Exploring the Potential Application of Silicon Carbide Nanocluster Through Probing the Structural, Physical and Distinctive Electronic Properties in Aluminum, Boron, or Gallium Battery: A Quantum Computational Study

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Authors: Fatemeh Mollaamin, M. Monajjemi
Cited by: 0
References: 29

Abstract

Abstract Group 13 elements-based materials, with impressive capacity utilization and self-healing ability, provide better alternatives for alkali metal ion batteries that exhibit all-round performance with the balance of energy/power density and cycling stability. Silicon carbide (SiC) has been designed and charachterized as an anode electrode for lithium (Li), boron (B), aluminum (Al) and gallium (Ga)-ion batteries due to forming Li2(SiC), B2(SiC), Al2(SiC) and Ga2(SiC) nanoclusters. A vast study on energy-saving by Li2(SiC), B2(SiC), Al2(SiC) and Ga2(SiC) complexes was probed using computational approaches due to density state analysis of charge density differences (CDD), total density of states (TDOS), electron localization function (ELF) for hybrid clusters of Li2(SiC), B2(SiC), Al2(SiC) and Ga2(SiC). A small portion of Li, B, Al or Ga entered the Si–C layer could improve the structural stability of the electrode material at high multiplicity, thereby improving the capacity retention rate. Higher Si/C content can increase battery capacity through Li2(SiC), B2(SiC), Al2(SiC) and Ga2(SiC) nanoclusters for energy storage process and improve the rate performances by enhancing electrical conductivity. Besides, SiC anode material may advance cycling consistency by excluding electrode decline and augments the capacity owing to higher surface capacitive impacts. In this research article, the recent progress of boron, aluminum or gallium -based anodes and their storage mechanism is presented. The current strategies used as engineering solutions to meet the scientific challenges ahead are discussed, in addition to the insightful outlook for possible future study.

Fields of Study & Subjects

Advancements in Battery Materials MXene and MAX Phase Materials Machine Learning in Materials Science

Publication details

Journal / source
Russian Journal of Physical Chemistry B
Volume
20
Issue
2
Pages
139–149
Publication date
1 February 2026
Language
EN