Solid State Sciences, Journal Year: 2024, Volume and Issue: 157, P. 107718 - 107718
Published: Oct. 9, 2024
Language: Английский
Solid State Sciences, Journal Year: 2024, Volume and Issue: 157, P. 107718 - 107718
Published: Oct. 9, 2024
Language: Английский
Materials Today Chemistry, Journal Year: 2025, Volume and Issue: 46, P. 102708 - 102708
Published: April 21, 2025
Language: Английский
Citations
0Journal of Materials Science, Journal Year: 2025, Volume and Issue: unknown
Published: May 7, 2025
Language: Английский
Citations
0International Materials Reviews, Journal Year: 2025, Volume and Issue: unknown
Published: May 21, 2025
Polyacrylonitrile-based carbon fibers (PANCFs) have revolutionized industries since the 1960s due to their superior properties and applications. However, a significant gap remains between performance theoretical potential, highlighting urgent need enhance our understanding of process-structure-performance relationship. Computational simulations, with ability provide analysis from atomic level higher-scale, are essential for bridging this gap. This review provides comprehensive overview advancements in computational simulation techniques produce high-performance PANCFs by optimizing process parameters through simulations. Furthermore, reactive molecular dynamics, density functional theory, atomistic modelling, finite element methods manufacturing systematically evaluated. Simulations play an important role developing identifying novel comonomers PAN precursors, evaluating different solvents during spinning, precise tracking cyclization dehydrogenation mechanisms stabilization, predicting mechanical property losses defects. Moreover, it is demonstrated that how kinetics-driven frameworks accelerate carbonization simulations combining atomic-scale interactions such as ring formation graphitic growth macroscale like temperature pressure. certain limitations remain: unresolved heterogeneous microstructure representation, multiscale disconnects bond-breaking macroscopic fiber evolution, validation barriers oversimplified quasi-2D models. To overcome these problems, possible future directions including advanced force fields, integration, AI-driven modeling could PANCFs.
Language: Английский
Citations
0Solid State Sciences, Journal Year: 2024, Volume and Issue: 157, P. 107718 - 107718
Published: Oct. 9, 2024
Language: Английский
Citations
0