pith. sign in

arxiv: 2501.05294 · v1 · pith:ESIITKXLnew · submitted 2025-01-09 · ❄️ cond-mat.mtrl-sci · cond-mat.mes-hall

First-Principles and Machine Learning Insights into the Design of DOTT-Carbon and its Lithium-Ion Storage Capacity

classification ❄️ cond-mat.mtrl-sci cond-mat.mes-hall
keywords lithium-ionstoragedott-carboncapacitycarbondiffusionefficientlearning
0
0 comments X
read the original abstract

Two-dimensional (2D) carbon-based materials are promising candidates for developing more efficient green energy conversion and storage technologies. This study presents a new 2D carbon allotrope, DOTT-Carbon, characterized by its distinctive and multi-ring structure featuring 12-, 8-, 4-, and 3-membered rings of carbon atoms. We explore its structural, mechanical, and lithium-ion storage properties by employing density functional theory and machine learning simulations. Phonon calculations confirm its structural stability and ab initio molecular dynamics simulations demonstrate its thermal resilience at elevated temperatures. The material exhibits anisotropic mechanical properties, with Young's modulus values varying between 280-330 GPa. DOTT-Carbon displays a lithium-ion storage capacity of 446.28 mAh/g, complemented by a low diffusion barrier (0.2-0.9 eV) and a high diffusion coefficient ($ > 1.0 \times 10^{-6}$ cm$^{2}$/s), possibly facilitating efficient lithium-ion transport. The stable open circuit voltage of 0.28 V also indicates its suitability as an anode material.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.