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fiber-based devices . Further research by Yu et al. showed that this technique successfully combines the
[31]
highly conductive network of CNTs with the pseudocapacitive characteristics of MXene to produce fiber
electrodes with exceptional performance [Figure 12K and L] [153] . Double-wound yarns have a wide range of
potential applications in fuel cell electrodes, artificial muscles, and energy storage devices due to their
distinctive structure and high loading capacity .
[154]

However, there are intrinsic obstacles to this technology’s further development. First, its effectiveness is
largely dependent on expensive, high-quality CNT scaffold materials (such as oriented arrays or films).
Additionally, the size of the prefabricated CNT bodies limits the continuous length of the final yarn, which
prevents large-scale, inexpensive manufacture. Second, more thorough and methodical research and
assessment is needed to determine the biocompatibility and long-term biosafety of CNT materials for
wearable textile applications in direct skin contact . Therefore, even though the double-winding method is
[155]
indispensable for producing ultra-high nanomaterial loading and creating specialized fiber structures, its
future development is still dependent on advances in low-cost CNT preparation methods, process
integration and optimization, and additional clarification of biosafety issues.

A methodical comparison of MXene and graphene fibre preparation techniques
Although there are several fabrication methods for both graphene and MXene fibers, there are notable
variations in each method’s scalability, cost, fiber characteristics, and applicability for a certain application.
Wet spinning, coating, electrospinning, dry/dry jet spinning, and dual-winding/template-assisted chemical
vapour deposition are the five most representative preparation techniques. Table 3 provides a thorough
side-by-side comparison of these techniques along important practical dimensions.

Smart textile applications
With their one-dimensional macroscopic morphology, high conductivity, adjustable surface chemistry, and
exceptional mechanical flexibility, MXene-based conductive fibers have become a crucial material for
overcoming the drawbacks of conventional flexible electronic devices in terms of lightweight, weavable, and
wearable comfort. The inherent characteristics of MXene nanosheets, such as their high volumetric
capacitance and metallic-like conductivity, are successfully transferred to the fiber scale by production
processes like wet-spinning, coating, and electrospinning. This offers the material basis for textile
processing-based integration of smart textiles. At the moment, MXene-based conductive fibers show great
promise for use in a variety of industries, such as wearable energy storage, flexible sensing and health
monitoring, smart thermal management, electromagnetic shielding, and signal transmission. They provide
an essential basis for creating next-generation smart textiles that are multifunctional and high-performing.

Biosensing and healthcare monitoring
A crucial material basis for building flexible sensing platforms that can continuously and in real time
monitor environmental and human physiological signals is provided by the integration of MXene-based
conductive fibers into textiles. Physical, chemical, and biological signals are captured via its sensing method,
which is based on reversible changes in conductive pathways within the fiber network in response to external
stimuli (such as strain, pressure, or molecule adsorption).

MXene fibers use their high conductivity and strain sensitivity in physiological and motion signal
monitoring to monitor a variety of activities, including joint movements and breathing. For example, Cheng
and Wu used wet spinning to create Kevlar/MXene composite fibers that were incorporated into smart
masks for high-sensitivity respiratory monitoring . Seyedin et al. used MXene/PU core-sheath fibers made
[143]
by coaxial wet spinning to detect large-range movements such as limb joints [143] . These fibers’ exceptional