The keyboard is one of the most essential human-computer interaction devices in daily life. However, conventional keyboards often suffer from issues such as poor ergonomic fit and inconvenient usability. Textile-based triboelectric nanogenerators (t-TENGs) offer a promising solution to these challenges. Nevertheless, the development of t-TENGs has been significantly constrained by the complexity of signal transmission circuits. In this study, we introduce a knitted interlock derivative fabric triboelectric nanogenerator (KIDF-TENG) with an arched surface, fabricated using advanced knitting technology. The KIDF-TENG exhibits highly stable electrical output, demonstrating minimal dependence on pressure and frequency, and maintains consistent performance after 10 000 operational cycles and multiple washing tests. Furthermore, we developed a multi-channel circuit simplification system (MCSS) that enables the transmission of multiple sensor unit signals through a single signal transmission channel by combining KIDF-TENG arrays of different materials based on their dielectric properties. The resulting flexible fabric keyboard, based on the MCSS, can transmit signals from ten sensing units using only three signal transmission channels. Additionally, the keyboard offers antibacterial properties, silent operation, and washability. This research presents a novel approach for developing flexible fabric keyboards and simplifying circuits for multi-channel flexible sensors.

Conductive behavior of braided structure formed with continuous conductive yarns is complex and important for the application design of aviation braided composites under electric fields. Here we report the conductive behavior of braided structure composites via 3-D conductive network modeling, including conductivity anisotropy, internal conduction pathways, electrical current and potential distribution under different loading directions, and braiding angle effects. The 3-D conductive network model integrates actual yarn spatial paths and inter-yarn contact behavior. We found that the composite longitudinal conductivity exceeds transverse values and the composite conductivity decreases longitudinally but increases transversely with braiding angle. The longitudinal conduction path is all braided yarns, while transverse current conduction involves inter-yarn contact conduction and yarn conduction. The proposed connected yarn concept elucidates the transverse conductivity's dependence on longitudinal dimensions. This work provides fundamental insights for designing braided composites with tailored current management capabilities.

The clinical efficacy of extracorporeal membrane oxygenation (ECMO) is largely determined by the mechanical properties and gas permeance of membrane materials. Current warp knitting methods can harm poly(4-methyl-1-pentene) (PMP) hollow fiber membranes because the tension from the yarns affects their ability to provide oxygen. This study systematically investigates the influence of three representative warp knitting structures—pillar stitch, tricot stitch, and cord stitch—on PMP membrane integrity using a design-based approach. A novel multiparameter model, the Membrane Damage Index (MDI), is proposed to quantify membrane damage across structural variants. Experimental results reveal that the pillar stitch configuration significantly reduces fracture strength loss (7.8%), CO 2 flux reduction (12.7%), and outer diameter compression (7.56%) by optimizing stress distribution and minimizing the underlap length (0.97 mm). Nonlinear regression analysis of the data led to the development of an MDI model ( R 2 = 0.992), highlighting the importance of the number of needle back traversal stitches ( n ) in minimizing membrane damage. This study offers a theoretical framework and optimization strategy for developing low-damage ECMO membranes, ultimately enhancing clinical efficacy and the long-term durability of ECMO technology.

Flexible puncture-resistant materials need to balance wearability with reliable protection, but this remains difficult to achieve. Inspired by the hardness-gradient structure of arapaima scales, a biomimetic multilayer knitted fabric was developed. A dense UHMWPE middle layer and a buffer layer were formed by one-piece knitting, while a hard SiC/PU coating was added to the protective surface. Differential yarn shrinkage was also used to form radial grooves. Compression tests confirmed a stepwise modulus gradient through the fabric thickness. The material showed a quasi-static puncture peak force of 418.3 N and a radial equivalent flexural modulus of 15.7 MPa. As the hardness gradient increased, the puncture resistance continued to improve, reaching a maximum peak force of 654.5 N. The bending modulus increased by only 25.43%. During bending, the radial grooves opened along the loading direction and provided extra space for geometric deformation. Dynamic impact tests showed that the material resisted penetration by the D2 blade and D3 spike at 32 J, and by the D1 blade at 16 J. Finite element and layer-combination analyses further explained the role of each layer. The hard layer mainly resisted initial indentation. The middle layer carried most of the tensile load through yarn stretching and sliding. The buffer layer reduced back-face stress and deformation through compression and structural redistribution. This hardness-gradient knitted structure offers a practical design strategy for lightweight, flexible, and wearable puncture-resistant materials.

To enhance 3D simulation efficiency for large-scale patterned fabric structures, a GPU-based yarn-level parallel computing framework is developed. individual yarns are adopted as the fundamental units of parallelization. Two key algorithms are devised within this framework: a parallel spline trajectory fitting algorithm and a parallel yarn mesh generation algorithm. Comparative experiments with conventional serial methods show that a maximum speedup of up to 10-fold is achieved by the proposed parallel strategy. Moreover, the speed imbalance across different stages during fabric simulation is effectively alleviated by the method. The algorithms are implemented in OpenCL, demonstrating robust performance across different GPU hardware. The applicability of the method to a wide range of fabric types, including both woven and knitted large-patterned fabrics, is confirmed through simulation case studies, underscoring its substantial potential for real-world deployment.

Synthetic woven fabrics need both structural accuracy and visual realism. A single method cannot achieve both. We decouple the problem. A parametric renderer builds a yarn-level skeleton from process parameters. An SDXL-based diffusion module refines appearance with depth-conditioned ControlNet and IP-Adapter style transfer. An FFT-based directional texture calibration maps real-fabric power spectra to material parameters. Spectral matching improves from 0.883 to 0.959. Structure-aware auxiliary learning uses only synthetic warp--weft masks. It cuts yarn density prediction MAE by 19.9\% with zero inference overhead. We evaluate on 815 real fabric products. The full pipeline achieves 90.3\% weave classification accuracy. That is 5.8 points above real-only training. Structure-aware learning reduces the overall MAE to 2.78 yarns. Warp $R^{2}$ is 0.958 and weft $R^{2}$ is 0.937. CycleGAN, Pix2Pix, and SDEdit can distort the second-order weave topology. Our process maintains the structural correspondence.

The 3D weft-knitted inlay (3DWKI) fabric structure enhances the mechanical properties of fabrics and their composites, while preserving the formation advantages of the weft knitting process. This study is a comparison of the impact resistance of 3DWKI composites and 2D plain woven (2DPW) laminated composites under identical impact energy conditions. Additionally, the impact response and damage mechanisms of 3DWKI composites under varying impact energies are analyzed. The results show that the energy absorption rate of the 3DWKI composite is 2.95% higher than that of the 2DPW composite for the same impact energy, 20 J. However, the dent depth of the 3DWKI composite is 1.65× that of the 2DPW composite, and the damaged area of the 3DWKI composite is only 20% of that of the 2DPW composite. The inlay yarn facilitates the propagation of stress waves in the horizontal direction, while the knitted yarn helps to reduce delamination damage and enhance the impact resistance of the composite. Furthermore, when the impact energy increases to 20 J, fiber breakage occurs in the interlock yarns, whereas only fiber tow splitting is observed in the inlay yarns, with no significant failure damage. Consequently, 3DWKI composites are promising for the development of full-form knitted composites that require enhanced impact resistance.

This study investigates the coupling relationship between yarn consumption and dynamic tension in a weft-insertion warp knitting machine, with the aim of clarifying how yarn tension fluctuations affect fabric accuracy and production efficiency during the warp knitting process. An instantaneous tension model was established based on Hooke's law, and a quantitative coupling framework was developed by integrating geometric modelling with the speed-difference effect. On this basis, the influence of yarn consumption on tension fluctuation was systematically analyzed. The results show that the gradient of yarn consumption, the lag effect of tension compensation, and abrupt changes in the yarn path are the dominant factors governing tension fluctuations. In particular, the positive and negative gradients of yarn consumption directly determine the amplitude and trend of tension variation. In addition, the retraction effect and sudden geometric path changes further intensify the nonlinear characteristics of tension fluctuation. Numerical calculations and experimental validation consistently reveal the intrinsic relationship between yarn consumption and dynamic tension, providing a theoretical basis for optimizing tension control in warp knitting.

Extracorporeal Membrane Oxygenation (ECMO) relies critically on the gas exchange performance of poly-4-methyl-1-pentene (PMP) membrane fabrics, which can be significantly affected by mechanical damage during the knitting process. However, the damage mechanisms induced by yarn tension during membrane fabric formation remain insufficiently understood. In this study, a combined geometrical modeling, finite element simulation, and experimental approach was employed to systematically investigate the mechanical deformation and functional degradation of PMP membranes during warp-knitting. A three-dimensional stitch geometry model of ECMO membrane fabrics was constructed and integrated into a finite element framework to simulate yarn tightening and contact-induced compression under different yarn tensions. The simulation results revealed a distinct elastic–plastic transition in the PMP membrane when yarn tension exceeded approximately 0.2 N, characterized by pronounced logarithmic and equivalent plastic strain localization. Experimental validation through outer diameter measurements, cross-sectional SEM observation, and porosity analysis demonstrated strong agreement with the simulation predictions. Below 0.2 N, membrane deformation was predominantly elastic and reversible, whereas higher tensions led to irreversible plastic flow, lumen collapse, wall folding, and pore closure. Quantitative pore structure analysis showed that exceeding the critical tension resulted in a significant reduction in open porosity and a marked increase in closed pore and wall volume fraction, directly impairing gas exchange capability. These results collectively establish 0.2 N as a critical yarn tension threshold for maintaining the structural integrity and functional performance of PMP-based ECMO membrane fabrics. This work elucidates the knitting-induced damage mechanism of ECMO membrane fabrics and provides a quantitative theoretical basis for tension control and low-damage manufacturing in large-scale industrial production.

Wearable electronics require compact, reliable, and sustainable power sources. Fabric-based triboelectric nanogenerators (TENGs) offer a promising solution by combining energy harvesting with the inherent softness and breathability of textiles. However, conventional functionalization methods, such as surface coatings or multilayer structures, inevitably diminish these essential properties. To address this issue, we developed a knitted fabric-based direct current triboelectric nanogenerator (KF DC-TENG) using whole-garment knitting technology and the air breakdown effect. This design allows direct application without complex post-processing and eliminates the need for external rectification. A single unit of KF DC-TENG (8 & times; 2.5 cm(2)), after structural optimization, is capable of lighting 744 series-connected Light Emitting Diodes (LEDs) when manually rubbed against polytetrafluoroethylene (PTFE) fabric. Integration with a low-cost power management circuit (PMC, similar to 1.5 CNY) further enhances the output power by 290 times. Asa result, 17 s of friction can power an electronic watch for up to 8 min. Moreover, continuous sliding can sustain a 1.5 W LED without noticeable flickering. This work presents the first demonstration of integrating whole-garment knitting technology with DC-TENGs, offering a scalable and cost-effective strategy for self-powered wearable electronics.

This study addresses the technical challenges in fabricating oxygenated membrane fabrics for extracorporeal membrane pulmonary oxygenation (ECMO) systems, specifically focusing on the development of a low-tension weft insertion technique for polymethylpentene (PMP) membrane knitting. In the research, we utilized a weft insertion warp knitting machine for fabric preparation and conducted a comprehensive analysis of weft yarn tension dynamics. Initially, tension variations during yarn consumption were investigated using a conventional negative weft insertion method. Subsequently, considering the unique properties of PMP membranes—characterized by low strength and high elongation—an innovative positive weft insertion method was developed and evaluated against the traditional negative approach. Experimental findings demonstrated that the proposed positive lay-up method significantly enhanced the weft insertion process by substantially reducing tension during PMP membrane unwinding and insertion. Specifically, in this novel approach, a minimum 90% reduction in tension amplitude and a 92.5% decrease in mean tension values was achieved, compared with the negative method. Furthermore, the positive insertion technique maintained stable tension control during PMP membrane unwinding and weft-laying at operational speeds up to 600 r/min, meeting ECMO membrane fabric manufacturing standards. In this research, we present an optimized technical solution for ECMO membrane fabric production, making a significant contribution to tension reduction in weft insertion warp knitting machinery.

Purpose Research on simulating shaped shoe uppers is still in its early stages. Most studies focus on simpler cut uppers, using texture mapping principles to directly map loop patterns onto curved surfaces. Although this method simplifies the simulation process to some extent, it fails to fully capture the complex details and realistic textures of the shoe uppers. For applications requiring high-precision simulations, the current technology clearly has limitations. Therefore, developing more refined and accurate methods for simulating shaped shoe uppers becomes particularly important. Design/methodology/approach This study focuses on flat-knitted shaped shoe uppers, delving into the knitting processes and the selection of different organizational structures for various regions of the shoe upper. It also comprehensively examines the basic principles of optimizing and reconstructing DXF files, as well as the three-dimensional (3D) simulation workflow. Findings The study addresses the issue of 3D surface control points exceeding OBJ boundaries, proposing a rational and efficient surface triangulation indexing method that successfully resolves this challenge. Originality/value This study helps in enabling the rapid transformation of planar loop meshes and shape value points onto curved surfaces. It also allows for the precise acquisition of the spatial coordinates of the shape value points for each loop in the fully-fashioned shoe upper and also helps in achieving the digital design and 3D simulation of flat-knitted shaped uppers through the implementation of loop structures.

As a critical through-thickness reinforcement and joining process for high-performance fiber fabrics and their composites, stitching can significantly enhance interlaminar fracture toughness, delamination resistance and impact damage tolerance, yet it also induces manufacturing-induced defects including fiber breakage, needle hole enlargement, voids and resin-rich zones, which threaten the structural service safety. This paper systematically reviews the research progress in stitching manufacturing, mechanical responses, damage mechanisms and intelligent monitoring of high-performance fiber stitched composites. The technical status of stitching processes, material systems and robotic intelligent sewing is summarized. The effects of stitching on in-plane and out-of-plane mechanical properties, load-bearing capacity of critical joints, and environmental service behavior are elucidated. The formation mechanisms of primary stitching-induced damage (introduced during the manufacturing stage) and secondary damage (evolved under service loads), as well as the coupling effects of process parameters, are revealed from a multi-scale perspective. The applications of non-destructive testing, structural health monitoring, and multi-scale modeling in damage characterization and prediction are reviewed. Special attention is paid to the advantages and challenges of artificial intelligence and data-driven methods in damage identification, health assessment, and life prediction. This review further identifies current gaps in damage mechanism, environmental-coupling performance, standardized evaluation systems, and integration of intelligent monitoring. Future efforts should be directed toward synergistic manufacturing-characterization-service life-cycle studies to enable low-damage, intelligent, and high-reliability engineering applications of high-performance fiber stitched composites.

Janus fiber membranes enable directional liquid transport (DLT) for oil-water separation and moisture management, yet conventional pore-channel designs offer limited efficiency. Herein, we have developed a groundbreaking Janus nanofiber structure inspired by the structural characteristics of plant leaves, specifically the pore gradient and liquid transport channels within leaves. An innovative intermediate buffer layer composed of a three-dimensional helical nanofiber membrane was introduced to boost porosity and horizontal interconnectivity. A dopamine-controlled regulation mechanism synergistically optimized the pore structure and wettability of this layer. The resulting Janus membrane exhibits a remarkable unidirectional transport index (1250%), a high oil-water separation efficiency (98.92%), and an ultra-high flux (13860.77 L·m⁻²·h⁻¹). Its integration with textiles demonstrates superior moisture and thermal management, confirming its versatility for applications in oil-water separation, industrial wastewater treatment, and high-performance functional garments.

Composite structured yarn materials are essential in the stab-resistant field. However, the microscopic and complex nature of the material presents a challenge in investigating the puncture performance and mechanism of diverse composite yarns (CY). This study investigates four types of CY materials, which are integrated with highperformance fibers and metal wires (H/MCY). The preparation process was adjusted to produce these materials with five distinct twist lengths (h1-h5), progressively increasing from small to large. Then, the research evaluates their tensile properties, cut resistance, and corresponding finite element simulation outcomes. The braided structure (B-H/MCY) significantly improves mechanical performance, with a 25.9 % increase in maximum tensile stress and a 44.2 % enhancement in cut resistance, compared to the UHMWPE with similar thickness. Nevertheless, at smaller twist lengths (h1), all H/MCY structures exhibit excessive structural distortion, which compromises stress transfer efficiency. Conversely, excessively large twist lengths (h5) increase yarn thickness and fluffiness, thereby degrading structural integrity and ultimately reducing the tensile and cutting performance of materials. Notably, the bearing stress of the B-H/MCY material is distributed into multiple segments, exhibiting superior stress propagation and energy dissipation. In contrast, the wrapped, double-wrapped, and core-wrapped structures generate single-segment stress. Moreover, the interlocking layered design of B-H/MCY materials provides multi-tiered protective capabilities. This approach provides some insights into studying the influence of the H/MCY structure on the mechanical properties of stab-resistant materials.

Flexible wearable sensors are rapidly developing to meet the urgent need of the e-market. However, research related to stretchable yarn-based triboelectric nanogenerators (TENGs) with high-efficient manufacturing techniques is limited. Here, by using the mature high-speed spiral braiding technique, a bionic double-helix braided yarn-based TENG (DHBY-TENG) is designed. The DHBY-TENG is qualified with lightweight, flexibility, washability, excellent mechanical stability, and superelastic deformation up to 500 %. Besides, the unique structure endows the smart yarn with a large contacting-separating area during stretching-releasing motion, which is suitable for electricity generation without contacting other triboelectric materials. Due to its high detection precision, DHBY-TENG can be used as a self-powered lamp cord, a real-time crib pre-warning system, and a selfcounting yoga elastic cord. Furthermore, it can be woven into a fabric to light up LEDs. This work provides a promising direction toward textile-based TENG as the power source and multifunctional stretchable sensors with excellent elasticity and practicability.

Using quadratic B-spline curves to generate 3D fiber paths, this study proposes a novel approach to fiber-level modeling based on the matrix transformation framework. This framework ensures a unified form and explicit rules, enabling convenient, flexible and efficient operations with excellent computability and parameter scalability. The braided yarn configuration is accurately described by an innovatively introduced fiber distribution function accounting for fiber count, internal or external transfer and distribution in the cross-section of bundle. Adding twisting parameters such as twisting degree, twist angle and twist direction significantly improves the texture and 3D visualization effect of twisted braided yarn. Net-shaped or curved tubular fiber-level braided structures are flexibly modeled using tape and strand units. Parameter optimization enhances computational efficiency and geometric adaptability. The simulation results demonstrate that the framework enable accurate simulation of various braided configurations and achieve fully parameteric modeling and flexible changes of 2D circular braided fabrics, producing geometric models with excellent structural continuity, suitable for fiber-scale mechanical analysis and providing an effective architectural support for digital modeling and multi-scale performance prediction of braided composites.

The inherent issues of knitted structures, such as large loop porosity and susceptibility to deformation, which may lead to protection failure, have not yet been addressed. In this study, a scalable method is presented for the preparation of knitted stab resistant materials with a locking-ring structure (LRKM). This is achieved by combining thermoplastic polyamide (PA) material with ultra-high molecular weight polyethylene (UHMWPE) to produce braided yarn, which is then formed a locking-ring structure protection module (LRM) through a knitting process. Following the hot-pressing process, due to the hot-melt curing of the PA section, the LRKM with rigid protective modules (H-LRKM) is presented, characterised by the presence of interlocked structure and bonded fixation. Therefore, H-LRKM exhibits high-level cut resistance (3257 gf), excellent abrasion resistance, remarkable softness (45 mN & sdot;cm), exceptional stab resistance (378 N), and outstanding low-speed impact protection (16 J). And, the incorporation of PA and a double-locking structure (D-LRKM) into the materials led to a substantial enhancement in stab resistance, with an observed maximum improvement of 114.6 % (D-LRKM, PA: 70 %). These results indicate that the interlocking and bonded effect of the LRM structure form a synergistic deformation, multilevel resistance puncture protection mechanism, enabling rapid absorption and dispersion of impact energy, resisting knife penetration. Thus, the design technology of H-LRKM introduces innovative strategies for the manufacturing and development of novel knitted protective equipment, optimizing protective performance. It holds significant application potential in the field of protective clothing, particularly in industrial environments that require stringent safety measures.

This research sought to explore the impact of ultrasonic pretreatment on the physicochemical characteristics of proteins derived from eggshell membranes through enzymatic extraction. Response surface methodology (RSM) and Box-Behnken design were employed to identify the ideal conditions for the extraction process. The optimal parameters determined were enzyme usage at 4.2%, pH level at 2.4, a solid-to-solvent ratio of 1:20 g/mL, and an extraction time of 21.5 h. The eggshell membrane was pretreated by ultrasound before pepsin hydrolysis under optimized conditions. The findings indicated that the hydrolyzed products subjected to ultrasonic pretreatment exhibited enhanced solubility, surface hydrophobicity, water and oil retention, foaming characteristics, and emulsifying ability compared to the untreated hydrolyzed products. Furthermore, the piezoelectric properties of the protein with ultrasonic pretreatment were also significantly improved. Additionally, the protein-based piezoelectric device displayed excellent sensing performance and was successfully applied for human motion detection and precise identification of different pressure positions. These findings indicate that ultrasound has great potential to improve the physicochemical quality of eggshell membrane proteins, providing a theoretical basis and research approach for food protein modification and the preparation of green electronic devices.

Electrospun fiber mats, as a class of high-performance nonwoven materials, are widely applied in textiles, filtration, medical, and other fields. However, the precise three-dimensional characterization of their microstructure and quantification of volume fraction face challenges such as low resolution, poor computational efficiency, and reliance on expensive experimental imaging. This study aims to develop a computer modeling method independent of experiments, achieving high-precision reconstruction and performance prediction of fiber mats. Methodologically, by simulating the electrospinning deposition process, a parameterized deposition model is constructed, and a solvent-orientation coupled dynamic contact model is proposed, which integrates solvent residual concentration with von Mises orientation distribution and quantifies fiber cross-penetration behavior through adhesion offset equations. The main work includes developing an efficient voxelization algorithm that analyzes fiber-voxel interactions via multi-level detection (center point-corner point-ray penetration) and tolerance compensation mechanisms, enabling rapid calculation of volume fraction. Experimental results demonstrate that the error rate of this method is below 2%, and it remains robust in high fiber density scenarios. This model not only provides a high-precision tool for studying the relationship between microstructure and performance of electrospun materials but can also be extended to multi-process parameter optimization and multi-scale performance prediction, thereby promoting the intelligent design and application of nonwoven materials.