Solar-driven interface evaporation offers a viable sustainable solution to worldwide freshwater shortages. However, its practical application is constrained by diurnal cycles and variations in the solar incident angle. To overcome these limitations, we developed an innovative spiral solar evaporator fabricated via three-dimensional weaving. This design leverages the self-supporting properties of spacer fabrics to construct a height-adjustable structure capable of active solar tracking. Simultaneously, the coating of hydrophobic spacer yarns with a hydrophilic aerogel creates highly efficient directional water channels within the fabric matrix. This interface engineering strategy optimizes the spatial congruence of water supply and heat distribution, establishing a foundation for high-performance evaporation. Furthermore, phase-change microcapsules incorporated into the fabric's middle layer stabilize the process by dynamically storing and releasing thermal energy to compensate for fluctuations in light intensity. The spiral architecture itself enhances lateral light absorption and establishes interlayer temperature gradients, thereby improving light capture and facilitating waste heat recovery while minimizing thermal loss. Owing to this synergistic design, the evaporator achieved a high evaporation rate of 2.16 kg·m-2·h-1 (at 97.62% efficiency) under one sun and maintained a rate of 0.45 kg·m-2·h-1 in darkness. During a 10-h solar tracking test, the total water output reached 11.31 kg·m-2, representing a 21% increase over a static system. This work provides valuable design strategies for developing high-performance, all-weather solar evaporators for efficient freshwater production.

The sensing performance of carbon-based flexible strain sensors is largely governed by processing parameters. Therefore, the exploration of optimal processing parameters is of great significance to the enhancement of material properties. However, the complex coupling among multiple variables renders conventional trial-and-error optimization inefficient. Here, a data-driven prediction–optimization strategy integrating design of experiments (DoE) with machine learning (ML) is established to accelerate the regulation of sensing performance in a thermoplastic polyurethane/carbon nanotube-carbon fiber/thermoplastic polyurethane (TPU/CNTs-CF/TPU) strain sensor. Uniform and information-rich experimental datasets are constructed within a high-dimensional parameter space, and a multilayer perceptron (MLP) model is trained to characterize the relationship between processing parameters and sensing performance. SHAP-based interpretability analysis is further employed to quantitatively elucidate the contribution mechanisms of key factors, including carbon loading and carbon nanotube ratio, thereby guiding targeted experiments in high-sensitivity regions and enabling iterative optimization through dataset expansion. As a result, the sensor sensitivity is markedly improved, with the gauge factor (GF) increasing from 15 to 105 while maintaining high predictive accuracy (R2 = 0.96). The trained model is subsequently used to screen 600 randomly sampled parameter combinations, from which two optimal candidates are identified and experimentally validated, yielding relative prediction errors as low as 2.3% and 3.1% at 80% strain. Moreover, the optimized sensors exhibit excellent stability under cyclic loading and human-motion monitoring. This work provides an efficient and interpretable paradigm for regulating material–process–performance relationships in flexible electronics, substantially reducing experimental cost and offering broad methodological guidance for high-performance flexible sensing systems.

Interfacial solar steam generators (ISSGs) can concentrate photothermal conversion and water evaporation at the liquid-vapor interface, thereby achieving highly efficient water evaporation. Nevertheless, conventional ISSGs are plagued by complex fabrication, severe salt accumulation, and lack of autonomous floating. Herein, a special-shaped knitted structural interfacial solar steam generator (SKSSG) was fabricated via two-dimensional weaving and 3D knitting technologies, integrating high evaporation performance, rapid water delivery, excellent salt tolerance, and self-floating capability. The spacer yarn (SY) with a directional vertical structure enables efficient water transport, while the bottom surface woven from polyethylene filaments endows the device with self-floating ability. The water transport rate of the edge yarn (EY) is lower than that of SY, resulting in salt enrichment in EY and the formation of a concentration gradient with the evaporation interface, thereby triggering the Marangoni effect to achieve rapid salt migration and exchange. Under 1 kW m-2 illumination, SKSSG achieves an evaporation flux of 1.91 kg m-2 h-1 (with an efficiency of 96.2%), and no surface salt crystallization is observed after 6 h of continuous operation in a 25 wt% NaCl solution. Additionally, techno-economic analysis confirms its promising commercial potential.

Interfacial solar evaporation is an effective seawater desalination approach, yet developing fabric-based interfacial solar evaporators (ISEs) with integrated light absorption, thermal management, directional water transport, salt resistance, and self-floating remains challenging. In this study, a warp-knitted spacer fabric (SF) was fabricated by 3D knitting, and a Janus structure was constructed on its upper/lower layers via electrostatic flocking and hydrophobic finishing, respectively (upper layer: light harvesting, salt resistance, antifouling; lower layer: self-floating). Moreover, utilizing the Z-axis structure of SF spacer filaments, sodium alginate (SA) aerogels were loaded to form independent directional water transport channels, ultimately yielding a novel dual Janus interfacial solar evaporator (HCNT-SF-SA). Its unique structure endows excellent performance: light absorption rate of 98.43%, evaporation rate of 2.15 kg m-2 center dot h-1 and efficiency of 91.95% under 1 kW m-2, 3.67 kg m-2 center dot h under 0.5 kW m-2 (low-light) with 1 m/s wind, and no salt precipitation after 9 h of continuous operation 7.5% NaCl solution. Industrial knitting enables its large-scale preparation and easy use, offering innovative insights for designing efficient, convenient solar evaporators.

Accurate prediction of fabric mechanical behavior constitutes a fundamental challenge in textile material design and optimization. As the primary mechanical property, tensile failure determines fabric durability and fastness, making its reliable prediction essential for quality assessment. To address the high cost and low efficiency issues arose from empirical fabric design, this study aims to establish a hybrid finite modeling-machine learning (FEM-ML) framework for predicting and inversely designing woven fabric tensile properties based on structural parameters. The FEM was firstly conducted to generate simulation datasets for ML training; then DNN ML models, were adopted to predict the tensile properties; An inverse design model was further established to directly determine structural parameters from the targeted tensile properties. Experimental results demonstrate that the optimal model achieved a coefficient of determination (R2) of 0.913 and a root mean square error (RMSE) of 0.032, with mean prediction errors 8.3 %. In addition, the inverse design model designed six sets of structural parameters, which were practically fabricated and tested. The average deviation between the measured and target tensile values remained 7.8 %, confirming the model's predictive accuracy and practical applicability. Overall, the proposed FEM-ML hybrid approach enables both forward prediction and inverse optimization of fabric structures, offering a prospective data-driven strategy for intelligent textile design.

The detection of fabric defects is a critical step in ensuring product quality within the textile industry. However, existing object detection methods often struggle to effectively capture the subtle features of narrow fabric defects (such as narrow yarn defects), resulting in decreased detection accuracy. To address this challenge, an improved object detection method is proposed in this paper, achieved by embedding a Convolutional Block Attention Module (CBAM) into an advanced Faster R-CNN network, thereby enhancing the ability to capture the characteristics of narrow defects. Furthermore, the impact of different CBAM embedding positions was investigated in order to optimize the maximum contribution of CBAM to fabric defect detection. Experimental results, based on a 6317-sample fabric defect dataset, demonstrate that the proposed method achieved a maximum improvement of 2.6% in mean Average Precision (mAP), and a maximum improvement of 5.97% in AP for narrow yarn defects. The findings of this research are promising in offering a possible solution for efficient defect detection within textile manufacturing.

For automatic fabric defect detection with deep learning, diverse textures and defect forms are often required for a large training set. However, the computation cost of convolution neural networks (CNNs)-based models is very high. This research proposed an involution-enabled Faster R-CNN network by using the bottleneck structure of the residual network. The involution has two advantages over convolution: first, it can capture a larger range of receptive fields in the spatial dimension; then, parameters are shared in the channel dimension to reduce information redundancy, thus reducing parameters and computation. The detection performance is evaluated by Params, floating-point operations per second (FLOPs), and average precision (AP) in the collected dataset containing 6308 defective fabric images. The experiment results demonstrate that the proposed involution-based network achieves a lighter model, with Params reduced to 31.21 M and FLOPs decreased to 176.19 G, compared to the Faster R-CNN’s 41.14 M Params and 206.68 G FLOPs. Additionally, it slightly improves the detection effect of large defects, increasing the AP value from 50.5% to 51.1%. The findings of this research could offer a promising solution for efficient fabric defect detection in practical textile manufacturing.

Polyurethane (PU) foam is widely used in various industries due to its advantages in heat preservation, heat insulation, sound absorption, etc., but this kind of material is extremely flammable and has fire hazards in use. Furthermore, the material compressive properties are insufficient, and the sound absorption performance of medium and low frequencies at low thickness also needs to be improved. Based on the above, in order to comprehensively improve the properties of polyurethane, a ternary layer-stacked composite consisting of spacer fabric/polyurethane/silica aerogel (FPS composite) was innovatively designed and prepared in this study. In the FPS composite, the spacer fabric surface layer functions similarly to the wall mesh fabric, which successfully solves the cracking problem of SiO 2 aerogel layer. In addition, the FPS composite reinforced by the spacer fabric with chain + inlay and hexagonal mesh surface structure has compressive modulus of 1.25 MPa and 0.8 MPa, respectively. The thermal conductivity of the two FPS composites is 0.0479 and 0.0452 (W m −1 K −1 ), respectively, and the FPS composite can achieve flame self-extinguishing within 40 s. In addition, the “filled microperforated plate-like” structure implemented by the introduction of spacer yarns gives the material resonance sound absorption characteristics, the FPS composite has a noise reduction coefficient value of 0.26 at a low thickness of 7.5 mm. To sum up, the composite materials prepared in this study can expand the application field of PU foam and adapt to industries that have requirements for heat preservation, heat insulation, fire prevention, sound absorption, and other aspects.

Currently, the development of wound dressings that combine liquid management, multidirectional breathability, self-supporting properties, hemostasis, and antibacterial characteristics presents challenges. Based on this issue, this study employs carboxymethyl modification, two-dimensional braiding, three-dimensional weft-knitting technology, and methylene blue (MB) solution impregnation to fabricate a specialized structured spacer fabric dressing (SFD-MB dressing). The successful modification gives the SFD-MB dressing with higher absorbency and water-absorbing gelation properties, enabling it to exhibit the ability to manage exudate and an absorption ratio of up to 376.47%, as well as meet the humidity requirements for wound healing. The three-dimensional knitted structure of the SFD-MB dressing ensures excellent breathability (maintaining a vertical airflow rate of 234.71 mm/s even when saturated with water), with a water vapor transmission ratio of 1204.16 g/m2/day. The compressive strength (11.76 kPa) and compressive modulus (17.2 kPa) of this dressing provide wound protection against external forces. Antiadhesion tests reveal that the hydrophobic surface created by polyethylene filament (PE filament) helps prevent the dressing from sticking to the wound and causing secondary injury. Coagulation, antibacterial, and cytotoxicity experiments confirm the hemostatic, antibacterial properties, and nontoxicity of the SFD-MB dressing. Better wound closure and tissue regeneration in full-thickness wound healing models prove the potential of the SFD-MB dressing.

Matching energy input to water supply is key to efficient solar‐driven interfacial water evaporation, but conventional interfacial solar steam generators (ISSGs) fail to adapt to diurnal solar flux fluctuations, thus hindering the achievement of dynamic hydrothermal balance. Inspired by marine octopuses, a fabric‐based dual‐interface solar evaporator (PDMS‐CFs‐CFF‐SF) is developed by integrating 3D knitting and electrostatic flocking, enabling adaptation to light intensity changes. When light intensity exceeds the upper interface's water supply capacity and heat accumulates, hydrophobic spacer yarns facilitate the directional transfer of excess heat to the lower interface, thereby triggering dual‐interface evaporation. The lower interface, leveraging its large‐pore structure, regulates moisture content to match the transferred excess heat. Moreover, the octopus‐inspired structure increases the evaporation area, enriches vapor escape channels, enhances thermal insulation performance, and adapts to sunlight incident at different angles. Under 1 kW m −2 irradiation, the evaporator achieves an evaporation rate of 3.14 kg m −2 h −1 and an efficiency of 129.32%. This work provides a novel structural strategy for developing ISSGs with dynamic hydrothermal balance capabilities.

The characterization of non-acoustic parameters is critically important for understanding the acoustic property and structural design of polyurethane (PU) foams. However, inverse characterization of acoustic PU foams through experiments and simulations often results in prolonged cycles and high resource wastage. To address the above issue, an innovative approach based on the Auto-encoder (AE) was proposed in this paper. In the AE approach, the decoder module was utilized for the forward prediction part, while the encoder was used for the inverse characterization. A sample database of 96,730 data sets covering PU foams' sound absorption coefficients at 500-6000 Hz was established to train the AE model. To verify the effectiveness of the trained model, a comparative experiment with numerical simulations was firstly conducted. The results revealed that the coefficient of determination (R2) of forward prediction module surpasses 0.99, while the prediction time is significantly rapid, averaging 0.0005 s per sample, which is 1/22,000 of numerical simulation time. Another comparative experiment was conducted between the inverse characterization results of the machine learning model and the experimental data from real samples. The results showed that the average error of the characterization parameters (non-acoustic parameters and material thickness) is about 8.70 %. In summary, this study provides an intelligent inverse characterization method for targeted sound absorption of PU foams, with potential extensions to the inverse characterization of other acoustic porous materials.

This study designed a novel multifunctional Janus structure dressing (DNCD dressing) composed of spacer fabric, agar/sodium alginate/calcium ion dual-network aerogel, methylene blue, and AgNO3-added thermoplastic polyurethane nanofiber membrane. The unidirectional liquid transport and absorbency tests prove that the DNCD dressing can unidirectionally transport liquids within just two seconds and possesses a liquid absorption ratio of 875.3 %. The unique open structure formed by the spacer fabric and liquid transport channels provides excellent air permeability as well as a suitable water vapor transmission rate, reaching 584.96 mm/s, 10.3 L/min, and 1104.82 g/m2/24 h, respectively. The exceptional compressive strength (216.78 kPa) and compressive modulus (515.23 kPa) of the dressing can provide protection for the wound. Antibacterial tests demonstrate that the silver ion-added DNCD dressing can eradicate >99 % of Escherichia coli and Staphylococcus aureus, while the added methylene blue can effectively monitor the survival status of bacteria. The low BCI value and the hemolysis ratio of <5 % indicate that the DNCD dressing has a certain hemostatic ability and does not cause hemolysis. The results of cytotoxicity tests and full-thickness skin defect models show that the DNCD dressing has good cytocompatibility and the potential to promote wound healing.

With the Fourth Industrial Revolution (Industry 4.0) and advances in digital technology, zero-defect manufacturing (ZDM) has become a transformative and attractive concept that has the potential to reshape the manufacturing landscape. In this structured literature review, recent developments in ZDM in the textile industry from 2004 to 2023 are examined, with a focus on detection, repair, prediction, and prevention. Through bibliometrics analysis and evaluation of the current situation of ZDM technology, four main shortcomings are highlighted, to be specific, limitations in automated defect detection, incomplete artificial intelligence (AI)-based repair strategies, restricted predictive research, and focused prevention mechanism. Meanwhile, open challenges that require urgent attention are explored, that is systematic ZDM strategy integration, data management complexity, and demand for flexible ZDM frameworks. To address these shortcomings and challenges, three further prospectives are proposed, including addressing research imbalance, vision for an integrated ZDM system, and evolutionary predictive models. These prospectives aim to advance the field and drive the holistic development of ZDM technologies in the textile industry by promoting a more intelligent production strategy with higher quality and less waste.

Benefitting from the interlaced networking structure of carbon nanotubes(CNTs),the composites of CNTs/poly-dimethylsiloxane(PDMS)have found extensive applications in wearable electronics.While hierarchical multiscale simulation frameworks exist to optimize the structure parameters,their wide applications were hindered by the high computational cost.In this study,a machine learning model based on the artificial neural networks(ANN)embedded graph attention network,termed as AGAT,was proposed.The datasets collected from the micro-scale and the macro-scale simulations are utilized to train the model.The ANN layer within the model framework is trained to pass the information from micro-scale to macro-scale,while the whole model is aimed to predict the electro-mechanical behavior of the CNTs/PDMS composites.By comparing the AGAT model with the original multiscale simulation results,the data-driven strategy is shown to be promising with high accuracy,demonstrating the potential of the machine-learning-enabled approach for the structure optimization of CNT-based composites.

Against the background that noise pollution has become a global problem, it is a challenge to prepare acoustic functional materials that combine strong low-frequency sound absorption at low thicknesses with excellent mechanical and thermal insulation properties. Inspired by natural reed, a unique microcolumn array was three-dimensional printed by stereolithography (SLA) and combined with sodium alginate aerogel (SA) and polyurethane (PU) foam to design a highly efficient acoustic composite (PC-FMPPL composite), featuring both "cavity-like" and "filled microperforated plate-like" structures. The combination of multiple sound-absorption mechanisms including resonance and porous sound absorption, along with the cavity-like structure, contributes to the excellent sound-absorption performance of this composite material, even at low thickness. Specifically, the noise reduction coefficient per unit thickness of the PC-FMPPL composite exceeds that of most reported acoustic materials. Furthermore, the PC-FMPPL composite exhibits a low thermal conductivity of 0.036 W m(-1)K-1 due to their intricate porous structure. Moreover, the microcolumn array provides support and resilience, resulting in excellent recovery and stability of the PC-FMPPL composite after 50 compression cycles. These favorable properties suggest promising applications for this highly efficient low-frequency acoustic composite in various fields, including architecture, transportation, and engineering. In addition, the proposed machine-learning-based sound-pressure prediction method for laminated composite offers the significant advantage of fast prediction speed (the trained machine-learning model predicts sound-pressure distribution of materials with different thickness ratios in just 0.4 s) while ensuring high accuracy, providing empirical support for predicting the acoustic performance of various types of laminated materials.

When deep learning is applied to intelligent textile defect detection, the insufficient training data may result in low accuracy and poor adaptability of varying defect types of the trained defect model. To address the above problem, an enhanced generative adversarial network for data augmentation and improved fabric defect detection was proposed. Firstly, the dataset is preprocessed to generate defect localization maps, which are combined with non-defective fabric images and input into the network for training, which helps to better extract defect features. In addition, by utilizing a Double U-Net network, the fusion of defects and textures is enhanced. Next, random noise and the multi-head attention mechanism are introduced to improve the model’s generalization ability and enhance the realism and diversity of the generated images. Finally, we merge the newly generated defect image data with the original defect data to realize the data enhancement. Comparison experiments were performed using the YOLOv3 object detection model on the training data before and after data enhancement. The experimental results show a significant accuracy improvement for five defect types – float, line, knot, hole, and stain – increasing from 41%, 44%, 38%, 42%, and 41% to 78%, 76%, 72%, 67%, and 64%, respectively.

Temperature/humidity dual-responsive fabrics, which can change color when exposed to heat and moisture separately or simultaneously, demonstrate excellent reversibility of color-changing. Herein, thermochromic microcapsules were synthesized and mixed with humidity-sensitive discoloration materials to create dual- responsive fabrics using screen printing technology. As the temperature rises from 30 to 80 degrees C, the fabric shifts from green to magenta. Similarly, as relative humidity drops from 100% to 0%, the fabric transitions from green to purple. The color of the fabric changes in a certain gradient by heat and moisture simultaneously. The fabric showcases a dependable, reversible color change, broadening the spectrum of visual possibilities. After enduring 30 rubs and 30 washes, the fabric's color difference value only slightly decreased, affirming its durability. Compared to untreated fabrics, dual-responsive fabrics exhibit remarkable flexibility and breath- ability. Therefore, this smart fabric was utilized to create fashionable garments, showcasing its visual effects across various temperatures. Simulations further examined its potential applications in monitoring human skin and equipment temperature, detecting humidity levels in storage, and information encryption. With its versatile properties, this intelligent fabric holds highly desirable properties for applications in textile fashion, environmental monitoring, and healthcare.

The textile, printing and dyeing industries are producing wastewater containing hazardous dye contaminants, which require advanced remediation methods to avoid environmental pollution. Here we review graphene oxide-based materials for the removal of dye contaminants in waters and wastewater, with focus on the properties of graphene oxide, adsorption mechanisms, factors controlling the adsorption, and applications. Dye adsorption is controlled by temperature, adsorbent and dye concentrations, and adsorption time. Graphene oxide composites include membranes and aerogels. Graphene oxide displays suitable hydrophilicity, acid‐alkali resistance, and strong adsorption capabilities. Increasing the surface activity and specific surface area of graphene oxide promotes the adsorption of graphene oxide on textile wastewater and dyeing wastewater.

Many conductive fabrics have been widely used as fabric strain sensors in recent years due to their excellent flexibility. In this study, high-tenacity polyester warp yarn and silver-coated nylon weft yarn were used to produce six types of conductive fabrics with different structures. The surface morphology of silver-coated nylon yarn was observed under a microscope, and the yarn was placed in an airtight container to test the effect of different humidity on the resistance of the yarn. DM6500 digital multimeter was used to test the resistance sensitivity, fatigue resistance, repeatability, and antistatic properties of conductive fabric samples. The results showed that the change in yarn surface material, external humidity, and strain affected the change in yarn resistance. Different weave structures affected the sensing performance of the fabric. The tighter the structure, the better the sensitivity and antistatic properties, the looser the structure, the better the fatigue resistance and repeatability. Two types of fabric and nylon gloves with different structural tightness are used to make intelligent data gloves. The results showed that both fabrics had good application prospects in limb movement detection.

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 was designed by combining a three-dimensional spacer fabric, sodium alginate aerogel, and electrospun membrane. This unique structural design allowed 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 provided 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) enabled the novel dressing to adapt to body contours and provided 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. This article is protected by copyright. All rights reserved.