Abstract
Carbon nanotube (CNT) fibers, renowned for their theoretically high tensile strength, low density, and outstanding electrical conductivity, are promising candidates for cutting-edge applications in wearable electronics, bioengineering, and aerospace engineering. Despite their immense potential, the widespread adoption of CNT fibers faces critical barriers, including the challenge of enhancing macroscopic fiber performance and achieving scalable, consistent production. The extraordinary intrinsic properties of individual CNTs are not fully transferred to macroscopic fibers due to weak intertube interactions, misalignment, and structural defects. Among the available production methods, floating catalyst chemical vapor deposition stands out for its promise to enable the large-scale synthesis of CNT fibers. However, achieving consistent quality and scalability via this technique remains a significant obstacle. This outlook highlights the importance of innovative strategies for multiscale performance optimization and advances in scalable fabrication methodologies. A distinctive perspective on CNT fiber production is provided, emphasizing the integration of machine learning with process optimization strategies to enhance the uniformity and efficiency. The outlook systematically discusses these challenges, exploring strategies for multiscale performance optimization, scaled-up fabrication methodologies, and efficient manufacturing processes. Additionally, it examines the transformative applications of CNT fibers across diverse industries, underscoring their potential to revolutionize next-generation technologies.