Identification and Analysis of Two Critical Structural Parameters Governing the Strength of Carbon Nanotube Fibers.

Despite significant advancements in theoretical and experimental research, the strength of state-of-the-art carbon nanotube fibers (CNTFs) still falls short of their theoretical limits. To bridge this gap, a detailed understanding of the structure-strength relationships of CNTFs is urgently needed to guide enhancement of the fiber strength. In this work, a mesoscale quantitative model employing coarse-grained molecular dynamics simulations was proposed to investigate the relationship between the conformation of CNTFs and fiber strength. Two structural features were identified to affect the overall fiber strength by machine learning: the length-pore ratio (α) and porosity (β). An in-depth analysis of various postprocessing techniques reveals that the objective of process optimization is to maximize α while simultaneously minimizing β. When these two factors are fine-tuned, it is possible to significantly enhance the mechanical performance of CNTFs, bringing their strength closer to the theoretical predictions.