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Unidirectional water transport performance is vital for maintaining human thermal and wet comfort in the field of garment materials. In this work, a 3D orthogonal woven fabric (3DOWF) with excellent one‐way transport capacity and mechanical properties is developed via 3D weaving and plasma treatment. The 3DOWF consists of polyester yarns (first layer), cotton yarns (second layer), and viscose yarns (third layer) with successively enhanced water absorption capacity. This allows droplets to penetrate spontaneously from the hydrophobic layer to the hydrophilic layer but not vice versa. Moreover, the Coolmax yarn with the core suction effect in the Z‐direction and the plasma‐treated polyester of the 3DOWF are shown to efficiently speed up the water transport process. In particular, the water penetration rate of the 3DOWF reaches 25 µl s‐1. In turn, the surface temperature after water absorption is increased by 2.6 °C compared with the cotton fabric, while the tensile strengths in the weft and warp directions of the 3DOWF are 49.62 and 18 MPa, respectively. These values represent the best insulation and mechanical characteristics thus far reported among unidirectional water transport fabrics. Therefore, the 3DOWF has great potential for use in watchbands, backpack belts, insoles, and other functional textiles.

Fibers are raw materials used for manufacturing yarns and fabrics, and their properties are closely related to the performances of their derivatives. It is indispensable to implement fiber identification in analyzing textile raw materials. In this paper, seven common fibers, including cotton, tencel, wool, cashmere, polyethylene terephthalate (PET), polylactic acid (PLA), and polypropylene (PP), were prepared. After analyzing the merits and demerits of the current methods used to identify fibers, near-infrared (NIR) spectroscopy was used owing to its significant superiorities, the foremost of which is it can capture the tiny information differences in chemical compositions and morphological features to display the characteristic spectral curve of each fiber. First, the fibers’ spectra were collected, and then, the relationships between the vibrations of characteristic chemical groups and the corresponding wavelengths were researched to organize a spectral information library that would be beneficial to achieve quick identification and classification. Finally, to achieve intelligent detection, pattern recognition approaches, including principal component analysis (PCA) (used to extract information of interest), soft independent modeling of class analogy (SIMCA), and linear discrimination analysis (LDA) (defined using two classifiers), assisted in accomplishing fiber identification. The experimental results – obtained by combining PCA and SIMCA – displayed that five of seven target fibers, namely, cotton, tencel, PP, PLA, and PET, were distributed with 100% recognition rate and 100% rejection rate, but wool and cashmere fibers yielded confusing results and led to relatively low recognition rate because of the high proportion of similarities between these two fibers. Therefore, the six spectral bands of interest unique to wool and cashmere fibers were selected, and the absorbance intensities were imported into the classifier LDA, where wool and cashmere were group-distributed in two different regions with 100% recognition rate. Consequently, the seven target fibers were accurately and quickly distinguished by the NIR method to guide the fiber identification of textile materials.

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A microclimate with ventilation and proper wettability near the wound is vital for wound healing. In the case of pressure or absorption of large amounts of wound exudate, maintaining air circulation around the wound is currently a challenge for wound dressings. In this study, a novel self-pumping dressing (FAED) with multiple liquid transport channels is designed by combining a 3D spacer fabric, sodium alginate aerogel, and electrospun membrane. This unique structural design allows FAED to unidirectionally rapidly remove excess biofluid from the wound and transfer it through a special liquid transport channel to a liquid storage layer with a high absorption ratio. Importantly, the air circulation layer of FAED composed of liquid transport channels and spacer yarns provides excellent air permeability in both the horizontal (12.3 L min −1 ) and vertical (272.02 mm s −1 ) directions. Additionally, a lower compression modulus (0.14 MPa) and higher compression strength (0.15 MPa) enable the novel dressing to adapt to body contours and provide good supporting performance, as compared to foam dressings. Combined with its high biocompatibility, this unique dressing has significant potential for wound treatment and intensive care.

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A new approach for extracting the hairiness from fabric based on the predicted fabric surface plane is presented in this paper to extract the hairiness from the depth image. The depth from focus (DFF) technique is utilized in this study to establish the depth image of the pilled fabrics by using a series of image layers captured under a microscope. A pilled fabric depth image provides information on the hairiness and the fabric surface, and the hairiness is located above the fabric surface. However, the depth value of the fabric surface covered with hairiness cannot be directly obtained. Therefore, for hairiness extraction, a predicted plane of the fabric surface is fitted by selecting several base points on the fabric surface. The target above the predicted plane will be considered as hairiness and will be extracted. The oversegmentation method based on the mean shift algorithm is used in the study to select the base points of the fabric surface. First, several seed points are marked along the Sobel edges; then, several oversegmented areas are formed after the growth of the seed points, which are called split pieces in this paper. The split pieces of the fabric surfaces are selected as the base points according to the depth value as well as the spatial direction of each split piece. Finally, the predicted plane of the fabric surface is established using these base points. The results of significance testing show that is it reasonable to assume that the fabric surface can be expressed as a plane. The results of the residual examination show that the predicted plane can correctly calculate the depth value (z) of the fabric surface at any plane position (x, y). The extracted hairiness images show that hairiness can be correctly and completely obtained through the predicted plane.

To enhance the compressive properties of syntactic foams, a new type of ternary composite named 3D spacer fabric/hollow microspheres reinforced composite (3DSMRC) was designed by adding warp-knitted space fabric (WKSF) into traditional syntactic foam. In order deeply understand the meso-mechanical properties of 3DSMRC composites, the compression tests of 3DSMRC were carried out and the quasi-static compression finite element models were established based on COMSOL Multiphysics. The results show that the compression properties of 3DSMRC were obviously controlled by structures of WKSF. To be specific, the 3DSMRC composites with more spacer yarns per unit area could withstand higher critical load, and with denser surface layers and larger spacer yarns inclination-angle could gain better compression capacities. Meanwhile, different types of microspheres also had important impact on the compression capacities of specimens, which could be improved by using smaller radius ratio (higher strength) microspheres. In addition, the finite element model can accurately reproduce the compression process and stress-strain curves of representative 3DSMRC samples, and then accurately simulate the values of compressive modulus and yield strength. The simulation and experimental studies of 3DSMRC can help to obtain a better and deeper understanding of the compression properties of this new type of composite, and finally provide a useful theoretical reference for the optimization design of 3DSMRC.