Electrically conductive fibers are required for various applications in modern textile technology, e.g., the manufacturing of smart textiles and fiber composite systems with textile-based sensor and actuator systems. According to the state of the art, fine copper wires, carbon rovings, or metallized filament yarns, which offer very good electrical conductivity but low mechanical elongation capabilities, are primarily used for this purpose. However, for applications requiring highly flexible textile structures, as, for example, in the case of wearable smart textiles and fiber elastomer composites, the development of electrically conductive, elastic yarns is of great importance. Therefore, highly stretchable thermoplastic polyurethane (TPU) was compounded with electrically conductive carbon nanotubes (CNTs) and subsequently melt spun. The melt spinning technology had to be modified for the processing of highly viscous TPU–CNT compounds with fill levels of up to 6 wt.% CNT. The optimal configuration was achieved at a CNT content of 5 wt.%, providing an electrical resistance of 110 Ωcm and an elongation at break of 400%.

Carbon particle‐filled polymers are frequently used as stretchable conductors and strain sensors. Many of the proposed resistance‐based stretchable strain sensors show non‐monotonic strain response, especially under dynamic conditions. This is commonly attributed to the competing destruction and reformation of the conductive network, but the underlying mechanism is still unknown. Therefore, systematic cyclic tensile tests are performed with carbon black‐filled silicone yarns, which show that the non‐monotonic behavior found depends on strain rate, strain history, and maximum applied strain. Based on the experimental results, a novel model is developed that incorporates the visco‐elastic nature of the polymer and representations of the percolative networks in strain direction as well as the transversal direction. It is shown that the non‐monotonic behavior is a result of the combination of visco‐elasticity and transversal contraction of the percolative network. The model is an equivalent circuit model and its simulation results are in good agreement with the experimental results. The model can be used to further understand the strain sensing behavior of conductive polymers and optimize sensor systems.

Wound assessment has become an important issue in the wound treatment procedure. One important indicator of the wound status is the pH value. Our approach to assess this quantity is through use of a fiber sensor coated with a pH-responsive hydrogel, which functions as a sensitive layer for impedance measurements. An advantage of this is its integratability into wound dressings using standard textile technologies. The pH characteristic shows a pH-dependent behavior of the absolute impedance at certain frequencies. The fabrication technology and sensor characteristics are discussed. The values of almost 14% impedance change demonstrate the potential for improvement by optimizing fabrication technologies. The presented sensor meets all requirements necessary for wound pH assessment

Recently, there has been remarkable progress in the development of smart textiles, especially knitted strain sensors, to achieve reliable sensor signals. Stable and reliable electro-mechanical properties of sensors are essential for using textile-based sensors in medical applications. However, the challenges associated with significant hysteresis and low gauge factor (GF) values remain for using strain sensors for motion capture. To evaluate these issues, a comprehensive investigation of the cyclic electro-mechanical properties of weft-knitted strain sensors was conducted in the present study to develop a drift-free elastic strain sensor with a robust sensor signal for motion capture for medical devices. Several variables are considered in the study, including the variation of the basic knit pattern, the incorporation of the electrically conductive yarn, and the size of the strain sensor. The effectiveness and feasibility of the developed knitted strain sensors are demonstrated through an experimental evaluation, by determining the gauge factor, its nonlinearity, hysteresis, and drift. The developed knitted piezoresistive strain sensors have a GF of 2.4, a calculated drift of 50%, 12.5% hysteresis, and 0.3% nonlinearity in parts.

Textile-based lightweight structures offer various possibilities for the design of tailored structures by the selective choice of materials and their processing into textile semi-finished products and fiber-reinforced plastics. Lightweight structures with a high mechanical load capacity are feasible by developing fiber-reinforced plastics with adaptive properties that are able to adapt their characteristics, e.g. geometry or stiffness, to external influences. Thus, the application potential of fiber-reinforced plastics can be further expanded. In this paper, we present novel adaptive fiber-reinforced plastics based on textile semi-finished products with integrated shape memory alloys and their mechanical characterization. The shape memory alloy is textile technically integrated and converted into friction spun hybrid yarn. Next, the produced hybrid yarn is integrated with plain, twill and satin woven reinforcement fabric in the weft direction during the shedding operation in weaving. Adaptive fiber-reinforced plastics are developed by infusing textile semi-finished products. Subsequently, the mechanical characterization of the adaptive fiber-reinforced plastics is carried out. Results show that, by integrating shape memory alloys into adaptive reinforced fabrics, the mechanical performance of fiber-reinforced plastics can be tailored.

Soft actuators are a promising option for the advancing fields of human-machine interaction and dexterous robots in complex environments. Shape memory alloy wire actuators can be integrated into fiber rubber composites for highly deformable structures. For autonomous, closed-loop control of such systems, additional integrated sensors are necessary. In this work, a soft actuator is presented that incorporates fiber-based actuators and sensors to monitor both deformation and temperature. The soft actuator showed considerable deformation around two solid body joints, which was then compared to the sensor signals, and their correlation was analyzed. Both, the actuator as well as the sensor materials were processed by braiding and tailored fiber placement before molding with silicone rubber. Finally, the novel fiber-rubber composite material was used to implement closed-loop control of the actuator with a maximum error of 0.5°.