Table of Content

15 January 2026, Volume 47 Issue 1

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Fiber Materials

Preparation and hemostatic properties of methacryloyl gelatin fiber membranes

KONG Yanhui, ZHANG Linping, MAO Zhiping, XU Hong

Journal of Textile Research. 2026, 47(1):  1-10.  doi:10.13475/j.fzxb.20250400701

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Objective The purpose of this study is to develop a new absorbable hemostatic dressing based on methacryloyl gelatin (GelMA) electrospun fiber membrane, so as to solve the clinical limitations associated with the conventional gelatin-based hemostatic materials (such as sponge and hydrogel). Such limitations include the risk of tissue compression caused by water absorption and swelling, the potential toxicity of chemical crosslinking agent, and the uncontrollable mechanical strength and degradation rate. At the same time, the influence of the degree of substitution on the properties of GelMA fiber membrane was systematically investigated, and the flexible regulation of the mechanical strength, degradation rate and hemostasis effect of the material was realized, thus adapting to the clinical needs of different hemostasis scenarios (such as arterial bleeding and venous oozing).

Method GelMA with different degrees of substitution (18%-69%) was prepared by modifying fish gelatin with methacrylic anhydride (MA), and processing into fiber membrane by electrostatic spinning technology. The comprehensive properties of the fiber membrane were evaluated by microscopic observation, tensile test, enzymatic hydrolysis experiment in vitro and coagulation index in vitro. At the same time, compared with commerical gelatin sponge and gauze, its hemostatic advantage was verified.

Results Firstly, the degree of substitution of GelMA functional groups was successfully regulated by MA. With the increase of MA content, the degree of substitution gradually increased, and the increase slowed down when MA concentration was greater than 3%. With the increase of the degree of substitution, the crosslinking degree of GelMA fiber membrane increased, so the morphology became increasingly stable and the fibers were interconnected, leading to the gradual increase in the tensile strength and elastic modulus and gradual decrease in elongation at break. Secondly, growing the degree of substitution caused the degradation rate to slow down. In the same enzyme solution environment, the mass of GelMA (degree of substitution is 18%) is only about 6.33% after 2 h, while that of GelMA (degree of substitution is 69%) is still about 31% after 60 h, confirming the regulation of the degree of substitution on the degradation rate of GelMA. In terms of hemostasis, the degree of substitution showed no significant effect on hemostasis, so the hemostatic effect of GelMA fiber membrane with different degrees of substitution remained similar, but better than that of commercial gelatin sponge. In addition, GelMA (degree of substitution is 38%) hydrogel and GelMA (degree of substitution is 38%) sponge were prepared and compared. The results of blood coagulation index showed that the coagulation effect of hydrogel and sponge was not as good as that of GelMA fiber membrane, which proved the superiority of fiber membrane.

Conclusion GelMA fiber membrane hemostatic dressing with adjustable performance was successfully developed and the conventional chemical crosslinking agent was replaced by photocrosslinking system to avoid toxicity risk. The shape of fiber membrane was found to significantly reduce the expansion rate (compared with sponge) eliminating the risk of tissue compression. Control over the degree of substitution to facilitated precise adjustment of degradation time period (2-60 h) and tensile strength (0.13-3.25 MPa), which are applicable to different hemostasis scenarios. In vitro experiments show that the coagulation time of fiber membrane is shortened to 66.49% of that of sponge, and blood congulation index is reduced to 30.69% of that of sponge, and its anti-swelling property is better than hydrogel and sponge. In the future, it is necessary to verify the in vivo performance through animal experiments and optimize the materials to cope with the bleeding scene with strong adhesion or incompressible.

Preparation and properties of calcium alginate freeze dried three-dimensional porous materials

YU Qiuyu, WU Jiang, TAN Yanjun, SHAN Wenxi, DENG Yuntao, LI Zongquan

Journal of Textile Research. 2026, 47(1):  11-19.  doi:10.13475/j.fzxb.20250404001

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Objective This study aims to use sodium alginate to prepare a three-dimensional porous material with stable structure and excellent liquid absorption performance so as to solve problems such as poor liquid absorption performance and complex preparation process for conventional medical dressings (such as cotton yarn). During the preparation process, the porous structure of the calcium alginate material is uneven, which causes low absorption to liquid and poor structural stability, limiting its application in the field of medical dressings.

Method This study transformed sodium alginate (SA) colloidal crystals into three-dimensional porous materials with uniform pores through stepwise cooling and freeze drying techniques. On this basis, the preparation process of calcium alginate (CA) three-dimensional porous materials was optimized by the Box-Behnken response surface method, and the influence of SA mass fraction, CaCl₂ mass fraction and treatment time on the liquid absorption performance of the material was studied. The optimal preparation process was obtained, and physical and chemical properties of the CA three-dimensional porous material were characterized.

Results During the preparation process, the method of stepwise cooling (freezing from -5 ℃ to -10 ℃ for 6 h, and finally freezing at -20 ℃ for 12 h) was found effective in solving the bulging and streak problems of SA colloid during the freezing process. This stepwise freezing process ensured uniform heat transfer from the inside to the outside of the SA crystal, making a smooth surface of the final prepared SA crystal. The further optimized freeze drying time was 36 h, not only ensuring uniformity of pore size, but also imparting excellent physical and chemical properties to the SA three-dimensional porous freeze dried material. The preparation process of CA three-dimensional porous materials was optimized by the Box-Behnken response surface method. The prepared CA three-dimensional porous materials showed excellent performance under the conditions of SA mass fraction of 1.75%, CaCl₂ mass fraction of 3.5%, and the freeze drying time of 9 h. Its liquid absorption rate was as high as 3 064%, showing a high liquid absorption capacity. In a wet state, the material's tensile breaking strength was 0.42 MPa, the elongation of breaking was 45%, and the compression rebound rate was 100%, showing good liquid absorption performance and structural stability, which meets the requirements of medical dressings. Fourier infrared spectroscopy (FT-IR) analysis showed that the absorption peak of CA at 3 367 cm^-1 was enhanced, indicating that Ca²⁺ reacted chemically with SA. Energy spectrum analysis (ESC) showed that the calcium content in CA increased by 8.08% compared with SA, and the sodium content decreased by 8.25% compared with SA, further confirming the replacement reaction between Ca²⁺ and SA. Scanning electron microscopy (SEM) observations indicated that CA three-dimensional porous material had a uniform network interpenetrating porous structure inside, which helps improve the material's liquid absorption performance and mechanical stability. Thermogravimetric analysis (TG) showed that the thermal decomposition temperature of CA was 70 ℃ higher than that of SA, demonstrating better thermal stability, which indicated that the introduction of Ca²⁺ enhanced the structural stability of the material.

Conclusion A CA three-dimensional porous material with uniform pores, stable structure and excellent liquid absorption performance was successfully prepared by stepwise freezing process and response surface optimization method. This material has significant advantages in liquid absorption, mechanical properties and thermal stability, meets the standard requirements of medical dressings, and has broad application prospects. The research results provide important theoretical basis and technical support for the development of new high-performance medical dressings, and also lay the foundation for the further application of calcium alginate materials in the field of biomedical science.

Preparation and properties of polyacrylonitrile-Prussian blue/lactic acid/ciprofloxacin photothermal responsive antibacterial dressings

ZHAO Jingwen, YUAN Xiangnan, GAO Jing, WANG Lu

Journal of Textile Research. 2026, 47(1):  20-28.  doi:10.13475/j.fzxb.20250404201

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Objective In order to address the clinical challenges of increasing antibiotic resistance and the growing demand for precise antibacterial strategies in infected wound treatment, an intelligent dressing with high antibacterial efficacy and low resistance risk was developed. By integrating photothermal therapy (PTT) with controlled drug release technology, a photothermally responsive antibacterial nanofiber membrane was constructed. Near-infrared (NIR) light was utilized to trigger localized hyperthermia and synergistic drug release, achieving efficient antibacterial action while minimizing damage to healthy tissues.

Method The polyacrylonitrile-Prussian blue/lactic acid/ciprofloxacin (PAN-PB/LA/CIP) nanofiber membrane was prepared by electrospinning PAN with PB, LA, and CIP. A three-factor and three-level orthogonal experiment was adopted to optimize spinning parameters (concentration, voltage, distance). Morphology and photothermal properties were analyzed by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), and thermal imaging, while drug release profiles were obtained by measuring cumulative release rates. Antibacterial activity against S.aureus and E.coli with/without NIR irradiation was tested. Cytotoxicity was assessed via CCK-8.

Results The optimum electrospinning process parameters for preparing PAN-PB/LA/CIP fiber film were determined by three-factor and three-level orthogonal experiment, which are 14% spinning solution concentration, 15 cm receiving distance and 16 kV voltage. The fiber membrane prepared under these conditions showed a uniform structure, the average fiber diameter was stable at 270 nm without obvious beading phenomenon, and good air permeability. The SEM and FT-IR characterization results revealed that the average particle size of the prepared PB nanoparticles was (137.5±24.6) nm, which was consistent with the document. FT-IR analysis confirmed that all components of PAN-PB/LA/CIP fiber membrane were successfully prepared, PB, CIP, and PAN were physically mixed with each other and LA with chemical bonding reaction, ensuring the functional stability and better quality of the material. The fiber membrane showed good photothermal synergistic antibacterial properties: under the near-infrared light irradiation of 0.5 W/cm², the fiber membrane temperature rapidly rose to 48.5 ℃ within 20 min and remained stable, which was significantly better than the pure PAN fiber membrane in the control group (the temperature rose by only 1.9 ℃), and had good photothermal activity. The photothermal conversion performance of the fiber membrane remained stable after three light cycles. When triggered by near-infrared light, the drug release behavior of the fiber membrane changed significantly, and the cumulative drug release at 24 h and 72 h reached 12.15% and 17.83%, respectively, compared to the non-light group (4.95% and 5.67%), indicating that the increase rate was more than three times, and the drug release rate was significantly accelerated and that the photothermal therapy dominated by the fiber membrane can significantly improve the therapeutic efficiency. The results of antibacterial experiment further confirmed its excellent antibacterial performance: under near-infrared light irradiation, the clearance rate of PAN-PB/LA/CIP fiber membrane against S.aureus and E.coli reached 100%. Multiple comparison results demonstrated that the incorporation of CIP significantly enhanced the antibacterial efficacy of the fiber membrane against both S.aureus and E.coli, with a statistically significant difference (P<0.05) between the NIR-irradiated group and the control group. The PAN-PB/LA/CIP nanofiber membrane, which combines photothermal and antibacterial effects, exhibited outstanding antibacterial performance under NIR irradiation. Moreover, cytotoxicity assessment (cell viability > 90%) confirmed its excellent biocompatibility.

Conclusion A photothermally responsive PAN-PB/LA/CIP composite antibacterial dressing was successfully developed, which achieves synergistic antibacterial action through localized hyperthermia and controlled drug release. Optimized spinning parameters ensured structural stability and air permeability. NIR-triggered heating (48.5 ℃) combined with rapid drug release significantly enhanced antibacterial efficiency (100% clearance) while avoiding thermal damage to tissues. The dressing demonstrates potential for constructing antibacterial microenvironments in infected wounds, providing a foundation for self-adaptive wound treatment systems.In the future, it is necessary to further optimize the ratio of materials, explore in vivo experiments and long-term biocompatibility, and promote its clinical development.

Conformational transitions and kinetics of silk fibroin by controlling solution concentration

GONG Weilong, YANG Yuhui, ZUO Biao

Journal of Textile Research. 2026, 47(1):  29-37.  doi:10.13475/j.fzxb.20250800501

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Objective Silk fibroin is a prominent textile material, and solution-based processing is central to its diverse functional applications. The properties of the resulting products are largely determined by the conformation of silk fibroin in solution. However, the inherent instability of silk fibroin in solution leads to time-dependent conformational changes, making it essential to clarify the underlying mechanism of these transitions.

Method Aqueous silk fibroin solutions with different concentrations were prepared by dissolving freeze dried powder in deionized water. Conformational transitions were monitored using fluorescence spectroscopy, while morphological evolution of aggregates during transition was characterized by atomic force microscopy (AFM). The solution viscosity was determined over a range of concentrations with a rheometer.

Results It was found that increasing the concentration of silk fibroin solution resulted in higher viscosity, a lower initial proportion of β-sheet structures, and a higher content of random coils. Beyond a threshold concentration (1 mg/mL), the proportions of β-sheet and random coil structures were stabilized. Spin-coated film morphology transitioned from fibrous to smooth with increasing concentration. At low concentrations, silk fibroin transitions from random coil to β-sheet over time, where β-sheet content increased initially and then plateaued followed by aggregation into fibers characterized by homogeneous nucleation and one-dimensional growth. In contrast, at high concentrations, a lag phase in conformational transition was observed, during which the structure initially remained unchanged. Subsequently, β-sheet content increased until an equilibrium was reached. Resultant β-sheet aggregates displayed three-dimensional network growth.

Conclusion This study demonstrates that solution concentration critically governs silk fibroin conformation and transition kinetics. At low concentrations, β-sheet formation is initially favored, proceeding via homogeneous nucleation and one-dimensional growth. High concentrations favor random coils, where reduced intermolecular distances promote interactions that require overcoming an initial kinetic barrier (manifested as a lag phase) followed by sigmoidal transition kinetics. The growth of β-sheet aggregates exhibits three-dimensional network characteristics. These findings provide insight into the molecular mechanisms of concentration-dependent conformational transitions and kinetics in silk fibroin solutions, offering a theoretical basis for designing high-performance silk-based materials by aqueous processing.

Preparation and properties of styrene-ethylene-butene-styrene/fluorinated polyimide waterproof and moisture permeable fibrous membranes

LUO Jiajun, HE Yaoquan, ZHAO Zhenhong, LI Jindao, ZHAO Jing, HUANG Gang, WANG Xianfeng

Journal of Textile Research. 2026, 47(1):  38-45.  doi:10.13475/j.fzxb.20250602001

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Objective Driven by strong market growth in functional textiles and rising demand for waterproofness and moisture permeability, current fabrication of waterproof and moisture permeable materials face key certain challenges, in that single-polymer systems cannot simultaneously achieve water resistance and moisture permeability, that multi-step post-treatments to fabric substrates are complex and costly, and that the mechanistic insights into composites remain limited. In order to address these issues, a SEBS/FPI composite was developed by a one-step electrospinning process to produce high-performance waterproof and moisture permeable fibrous membranes. Elucidating the relationships between structure and property in the composite provides theoretical guidance and technical support for next-generation functional textile design.

Method Styrene-ethylene-butene-styrene (SEBS) was selected for its excellent flexibility and hydrophobicity, and fluorinated polyimide (FPI) for its ultra-low surface energy and superior chemical stability. Using tetrahydrofuran (THF) as the solvent, SEBS/FPI membranes were fabricated by a single-step electrospinning process. The influence of SEBS/FPI ratio on the membranes' microstructure, pore morphology, waterproof and moistave permeable properties and mechanical properties was systematically investigated, and the synergistic mechanisms arising from the interactions between the two polymer components were analyzed.

Results With FPI fixed at 1%, SEBS was varied at 10%, 12%, 14%, and 16%, it was found that as SEBS content increased, the solution viscosity rose, causing the average fiber diameter to increase from 739 nm to 1 269 nm. Concurrently, fiber fusion intensified, leading to the reduction of membrane porosity from 75.47% to 63.18%, maximum pore size from 4.3 μm to 2.8 μm, and mean pore size from 1.8 μm to 0.6 μm. The decrease in relative FPI content also led to a reduction in water contact angle from 134.7° to 108.4°. Smaller pores and lower porosity resulted in a drop in air permeability from 13.12 mm/s to 6.24 mm/s and a decrease in water vapor transmission rate from 15.55 kg/(m²·d) to 12.42 kg/(m²·d), while hydrostatic pressure increased from 18.1 kPa to 27.0 kPa. The optimal balance of waterproofness and moistnre permeability was achieved at 12% SEBS, where the membrane exhibited a water contact angle of 132.5°, hydrostatic pressure of 21.8 kPa, and a water vapor transmission of 14.53 kg/(m²·d). Next, with SEBS fixed at 12%, FPI content was varied at 2%, 5%, 8%, and 11%, increasing FPI content reduced solution viscosity and average fiber diameter from 636 nm to 460 nm. While fiber fusion decreased, membrane porosity was increased from 57% to 72%, maximum pore size from 1.8 μm to 3.2 μm, and mean pore size from 0.61 μm to 1.3 μm. Higher fluorine content raised the water contact angle from 132.2° to 138°. The enlarged pore structure enhanced air permeability from 2.59 mm/s to 7.31 mm/s, and water vapor transmission rate from 9.08 kg/(m²·d) to 18.08 kg/(m²·d), but reduced hydrostatic pressure from 53.8 kPa to 11.0 kPa. The composite membrane reached its best overall performance at 5% FPI, exhibiting a water contact angle of 132.9°, hydrostatic pressure of 53.4 kPa, water vapor transmission rate of 9.71 kg/(m²·d), tensile strength of 4.6 MPa, and elongation at break of 90.6%.

Conclusion A high-performance waterproof and moisture permeable fibrous membrane was fabricated by a one-step electrospinning process using a SEBS/FPI composite system. Single-factor optimization identified the optimal formulation as 12% SEBS and 5% FPI. The resulting composite membrane exhibited outstanding performance, with a hydrostatic pressure of 53.4 kPa, a water vapor transmission rate of 9.71 kg/(m²·d), a tensile strength of 4.6 MPa, and an elongation at break of 90.6%. Mechanism analysis indicated that the introduction of fluorine-containing groups into the FPI molecular chain endows it with low surface energy characteristics, thereby reducing the overall surface energy of the composite fiber membrane. Additionally, the rigid chain structure of FPI reduces the viscosity of the spinning solution, improves the stability of the jet, and promotes the formation of fine fibers and optimized the membrane's micro-porous structure. This is the first attempt to apply a SEBS/FPI composite system for waterproof and moisture permeable membranes, which expands the range of electrospun raw materials and establishes a clear composition-structure-property relationship. As a polymeric material, FPI is not prone to migration or accumulation in the environment. Moreover, fluorine atoms are stably incorporated into the polymer backbone in the form of C—F covalent bonds, preventing the release of free fluoride ions. The fabricated fibrous membranes show great promise for medical protective clothing, everyday protective gear, and other applications, providing a new technical pathway and theoretical foundation for the industrialization of high-performance waterproof and moisture permeable materials.

Preparation and performance of photochromic fibers based on polyhydroxyalkanoates by microfluidic wet spinning

CHEN Kelin, LI Zhuo, WANG Xiaoge, LI Chengjin, HU Jianchen, ZHANG Keqin

Journal of Textile Research. 2026, 47(1):  46-53.  doi:10.13475/j.fzxb.20250206501

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Objective Photochromic textiles demonstrate changes in color under the irradiation of a specific wavelength of light, that is, through the change of optical conditions in the environment to achieve a reversible change in color. In the situation where there is light or other bands of light irradiation, photochromic textiles can restore the original color. This novel textile meets people's needs for color personalization, uniqueness and other aspects, and how to make textiles both functional and environmentally friendly has always been the goal of researchers.

Method Three photochromic microcapsules with different chromic effects were synthesized by in situ polymerization, which were added to the spinning solution of polyhydroxyalkanoate/polylactic acid(PHA/PLA). PHA/PLA fibers with photochromic function were obtained by microfluidic spinning technique. The influences of PLA concentration and the addition of microcapsules on fiber mechanical properties were investigated. Through the color mixing and color matching of the microcapsules, the fibers obtained more color-changing effect.

Results With spiroxazine as the core material and melamine formaldehyde as the wall material, the photochromic microcapsules that can change from white to blue under a specific wavelength light irradiation were synthesized by in situ polymerization method. Under the conditions that PVA concentration was 0.2%, the core wall volume ratio was 1∶1, and SDS concentration was 0.5%, the synthetic photochromic microcapsules demponstrated the best morphology and color change effect, and the average particle size was smaller. In addition, by replacing the photochromic compounds of the core material, the photochromic materials naphthopyran were used as the core materials, and the other two photochromic microcapsules with different color change effects (from white to yellow and from white to purple) were synthesized, which also showed good color change effect. Spinning was conducted by varying the PLA concentration (10%, 12%, 14%, and 16%). The results showed that the tensile strength of the PHA/PLA fibers was 50.68 MPa at the PLA concentration of 14%. In order to further improve the mechanical properties of the fibers, the fibers were collected on the plastic spool, and soaked in absolute ethanol for 24 h, and the mechanical properties of the treated fiber were significantly improved. With 14% PLA the concentration, the tensile strength of PHA / PLA fiber reached 66.10 MPa, a 30.4% increase compared with the untreated fiber, and the tensile elongation was also significantly increased. When the mass fraction of microcapsules was only 0.5%, the tensile strength of the fiber is 43.91 MPa, which is approximately 33.6% lower than that of the PHA/PLA fiber without microcapsules, and the tensile elongation is reduced by 56.46%. When the mass fraction of microcapsules reached 2%, the tensile strength of the fiber is only 20.65 MPa, and the tensile elongation is merely 11.14%.

Conclusion Three photochromic microcapsules with different color change effects were synthesized by in situ polymerization, using melamine-formaldehyde resin as the wall material and the photochromic compounds spirooxazine, naphthopyran, and spiropyran as the core materials, respectively. These microcapsules were then added to the PHA/PLA spinning solution, and photochromic PHA/PLA fibers were successfully prepared by microfluidic spinning. When the concentrations of PLA and PHA were 14% and 2%, respectively, the tensile strength of PHA/PLA fibers was 50.68 MPa. In addition, the mechanical properties of the PHA/PLA fibers were significantly improved after soaking in ethanol, and the tensile strength reached 66.10 MPa. The mechanical properties characterization found that once the microcapsule is added, the mechanical properties of the fibers will decrease significantly.

Preparation and Cr(Ⅵ) adsorption of polyacrylonitrile/covalent organic framework composite nanofiber membranes

LING Lei, CHEN Kai, GAO Jun, WU Dingsheng, WANG Dengbing, ZHANG Chun, FENG Quan

Journal of Textile Research. 2026, 47(1):  54-62.  doi:10.13475/j.fzxb.20250603301

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Objective In view of the serious threat cauced by hexavalent chromium (Cr(Ⅵ)) pollution to the ecological environment and human health, current mainstream adsorbent materials still have significant limitations in terms of removal efficiency and recycling. Therefore, the development of highly efficient, stable, and highly selective adsorptive detoxification materials for the treatment of Cr(Ⅵ)-containing wastewater has important practical significance.

Method Polyacrylonitrile (PAN)/1,4-phenylenediamine (Pa) blended fibers were prepared by electrospinning, which were immersed in a solution of 1,3,5-benzenetricarboxaldehyde (Tp) to in situ grow covalent organic framework (COF), thus obtaining a composite membrane. The membrane was characterized by scanning electron microscopy (SEM), X-ray diffractometer (XRD), Fourier transform infrared spectrometer (FT-IR) and X-ray photoelectron spectrometer (XPS). Its adsorption performance was evaluated under conditions of different pH values, temperatures and Cr(Ⅵ) concentrations, and its adsorption isotherm, adsorption thermodynamics and adsorption kinetics were analyzed.

Results The surface of the raw electrospun PAN/Pa fibers was smooth and uniform. Following the in situ growth of the COF, a nanoscale dendritic COF morphology was successfully constructed on the fiber surface, forming a hierarchical porous network and significantly increasing roughness. XRD patterns showed the characteristic crystal planes of the COF, while FT-IR spectra displayed the distinct C≡N vibration of PAN and the key functional group vibrations (C=O, C—N, N—H) of the COF. The attenuation of amino-related FT-IR peaks after Cr(VI) adsorption indicated the direct participation of these groups in the adsorption process. The composite PAN/COF nanofiber membrane exhibited enhanced rigidity, with a tensile stress of 8.3 MPa, a strain of 8.8%, and an elastic modulus of 94.3 MPa, compared to the more ductile pure PAN/Pa membrane. Its hydrophilicity was also improved, evidenced by a decrease in the water contact angle from 65.18° to 49.65°. Adsorption performance for Cr(VI) was highly dependent on solution conditions; and capacity increased with temperature and decreased with rising pH. This pH dependence is attributed to the protonation of amino groups under acidic conditions, enhancing electrostatic attraction, while deprotonation occurs under alkaline conditions. A maximum adsorption capacity of 99.4 mg/g was achieved under the conditions of 318 K and pH=1. Analysis of the adsorption process revealed that the isotherm conformed to the Freundlich model, suggesting multilayer adsorption on a heterogeneous surface. Thermodynamic parameters confirmed the process was spontaneous and endothermic. Adsorption kinetics followed the pseudo-second-order model, and XPS analysis indicated that approximately 46.7% of the adsorbed Cr(VI) was reduced to less toxic Cr(III), jointly pointing to a dominant chemisorption mechanism. Furthermore, the membrane demonstrated promising reusability, maintaining over 80% of its initial adsorption efficiency after seven adsorption-desorption cycles.

Conclusion The PAN/COF composite nanofiber membrane was prepared by electrospinning and in situ growth method for the adsorption and reduction treatment of Cr(Ⅵ)-containing wastewater. Characterizations by SEM, XRD and FT-IR prove that the PAN/COF composite nanofiber membrane with good mechanical properties and hydrophilicity has been successfully prepared. The influences factors such as temperature, pH value, initial Cr(Ⅵ) concentration and contact time on the adsorption performance were studied. The results showed that at 318 K and pH=1, the maximum adsorption capacity of the membrane for 100 mg/L Cr(Ⅵ) could reach 99.4 mg/g. The study of the adsorption mechanism indicated that the adsorption process of Cr(Ⅵ) on the PAN/COF composite membrane conformed to the characteristics of multi-molecular layer adsorption, was a spontaneous endothermic reaction, and was dominated by chemical adsorption. In addition, the composite membrane has good reduction performance and reusability.

Preparation and properties of flame retardant poly(L-lactic acid)/pentaerythritol phosphate fibers and fabrics

DONG Zhenfeng, ZHANG Anying, WEI Jianfei, ZHU Zhiguo, WANG Rui

Journal of Textile Research. 2026, 47(1):  63-71.  doi:10.13475/j.fzxb.20250503201

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Objective Poly (L-lactic acid) (PLLA) is a biodegradable polymer synthesized from renewable biomass resources. Its main raw materials are starch-rich crops. In natural environments, PLLA can be gradually decomposed by microorganisms into carbon dioxide and water, which are non-toxic and harmless to the human body. This aligns with the green development logic of ″coming from nature and returning to nature″. PLLA exhibits good mechanical strength, stiffness, and processing fluidity. It can be processed into products of various forms meeting application needs in different fields. PLA has become a research hotspot and application focus in the field of materials science in recent years.

However, PLLA and its fabrics also has obvious performance shortcomings, among which flammability is the most critical one. Its limiting oxygen index (LOI value) is only 21%, meaning that PLLA is easily flammable when in contact with an open flame. This defect severely restricts its application in fields with explicit fire resistance requirements. In order to improve the flame retardancy of PLA fabrics, a large number of studies have been conducted in the industry. Pentaerythritol phosphate, as a cage-like phosphate flame retardant, not only has extremely low biological toxicity (avoiding the harm of conventional flame retardants to the environment and human body) but also can interact with the terminal hydroxyl groups in the PLA molecular chain to form a stable flame-retardant system. While increasing the LOI value of PLLA and suppressing molten dripping during combustion, it retains the original physical and mechanical properties as well as biodegradability of PLLA to the greatest extent, opening up a new path for the application of PLLA in fields with high fire protection requirements.

Method The PLLA/PEPA masterbatch with 40% PEPA was prepared using a twin-screw extruder, and then the masterbatch was mixed with PLLA followed by injection molding to fabricate a series of PLLA/PEPA composites with different mass percentages of PEPA. The structure and properties of the composites were characterized and tested using differential scanning calorimetry, thermogravimetric analysis, extreme oxygen index analyzer, cone calorimeter, vertical combustion tester, and universal tensile machine. The composite with the best comprehensive performance was optimized for the preparation of fibers and fabrics, and the flame retardant performance of the fabrics was evaluated.

Results When the mass fraction of PEPA added was not greater than 6%, PLLA/PEPA showed good processing performance, and the glass transition temperature, crystallization temperature, melting temperature, and thermal decomposition temperature of the composite were not significantly affected. When the mass percentage of PEPA added was 5%, the LOI value of the composite was 32%, the vertical combustion test reached V-0 level, and the ignition time was prolonged by about 25 s. The composite with 5% PEPA added demonstrated good spinnability. The mechanical testing results showed that the fiber fineness was 185.7 dtex(72 f), and the fiber breaking strength and elongation at break were 2.38 cN/dtex and 21.5%, respectively. Flame retardant performance testing conformed that the LOI value of the fabric was about 33%, the vertical combustion test of the fabric was V-0 level, and the flame retardant performance of the fabric remained unchanged after 30 cycles of water washing. Study on flame retardant mechanism suggested that the improvement of flame retardant performance of PLLA by PEPA was attributed to the combined effect of free radical scavenging mechanism, condensed phase flame retardant mechanism and cross-linking induced melt viscosity increase.

Conclusion After adding PEPA with a mass fraction of 5% to PLLA, the flame retardant performance of the material is significantly improved in effectively increasing the difficulty of ignition, delaying the combustion process, and reducing the flame spread rate. The fabric obtained from the modified PLLA fibers by weaving maintains excellent mechanical properties as well as flame retardant performance, balancing practicality and safety. More importantly, the flame retardant effect of the fabric does not decrease after 30 washing cycles, showing outstanding performance stability. This scheme provides a new method with great application value for preparing safe, harmless, environmentally friendly and biodegradable flame retardant fabrics.

Polyester fiber ultrastructure segmentation algorithm based on improved U-Mamba network

ZHOU Yu, WEI Bing, HAO Kuangrong, GAO Lei, WANG Huaping

Journal of Textile Research. 2026, 47(1):  72-79.  doi:10.13475/j.fzxb.20250500101

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Objective In order to address the product performance degradation caused by agglomeration in the ultrastructure of polyester fibers, this research aims to propose an improved U-Mamba segmentation algorithm integrated with a high-order visual state space module and a multi-scale fusion module. The algorithm achieves accurate identification and segmentation of agglomerates, providing technical support for industrial machine vision-based ultrastructure analysis and meeting the application requirements of machine vision technology in the ultrastructural analysis of high-performance fibers in industrial production.

Method In order to address the issue of agglomeration effects in the ultrastructure of polyester fibers during industrial production, which negatively impacts product properties such as color uniformity, mechanical consistency, and gloss, a polyester fiber ultramicrostructure segmentation algorithm based on improved U-Mamba network was proposed. First, high-resolution images of agglomerated particle distributions in the ultramicrostructure of polyester fibers were acquired using the GeminiSEM 560 scanning electron microscope, and a corresponding dataset was constructed to evaluate the model's performance. A pre-trained neural network integrated with edge detection algorithms was employed to denoise, filter, and automatically colorize the fiber images. Then, an improved deep network model based on U-Mamba was adopted to accurately identify and segment agglomerates in the ultramicrostructure.

Results A polyester fiber ultrastructure dataset was established, and the proposed model was compared with five mainstream segmentation models, which are DeepLabV3, UNet, AttUNet, TransUNet, and SwinUNet. The proposed models demonstrated superior segmentation performance across 5 evaluation metrics, i.e.intersection over onion(IoU), dice similarity coefficient (DSC), accuracy (Acc), specificity (Spe), and sensitivity (Sen). Specifically, the model achieved an IoU of 78.9%, DSC of 88.2%, Acc of 96.1%, Spe of 97.4%, and Sen of 89.1%, indicating excellent capability in segmenting aggregates within the ultramicrostructure. Furthermore, ablation studies were conducted to assess the contributions of the improved high-order visual state space module and the multi-scale information fusion module to the overall segmentation performance. The results demonstrated that removing the module resulted in a 3.4% decrease in IoU, while omitting the module caused a 2.2% reduction. When both modules were removed, the IoU decreased by 4.5%, highlighting the crucial role of these modules in enhancing segmentation performance. Finally, in order to visually compare the segmentation results of different algorithms on the proposed dataset, visualization experiments were performed. The findings indicated that, relative to other models, the proposed method more accurately identifies and segments abnormal aggregates, contributing a novel approach to the application of neural networks in the segmentation of polyester fiber ultramicrostructures.

Conclusion This paper proposed an improved U-Mamba based segmentation algorithm for polyester fiber ultrastructure. Specifically tailored to the research requirements of ultrastructural analysis, a dedicated dataset of polyester fiber ultrastructure was constructed. During the image preprocessing stage, a pretrained neural network integrated with edge detection algorithms was employed to perform denoising, filtering, and auto-coloring on fiber ultrastructure images to facilitate subsequent segmentation. The key innovation lies in the design of a high-order visual state space module, which introduces higher-order operations into semantic segmentation. This module maintains the global receptive field advantages of SS2D while minimizing redundant information. Furthermore, convolutional blocks are embedded within the visual state space module, effectively combining the feature extraction capabilities of both convolutional operations and SS2D to enrich multi-level feature representations. Additionally, a multi-level multi-scale feature fusion module was designed incorporating channel attention and spatial attention mechanisms to enhance feature diversity during decoder fusion. Experimental results demonstrate that the proposed model achieves superior segmentation performance on the polyester fiber ultrastructure dataset compared to existing methods, while maintaining high segmentation accuracy. The integration of computer vision techniques for polyester fiber ultrastructure analysis represents a future trend in intelligent industrial production. This approach not only improves working conditions by replacing manual inspection of microscopic fiber defects but also enhances detection efficiency in practical manufacturing. Our algorithm successfully identifies and segments agglomerates within the ultrastructure, showing potential for applications in fiber material defect detection. The proposed method also provides insights for embedded device deployment, which will be the focus of future research.

Wool and cashmere fiber recognition algorithm based on frequency-domain field depth fusion and improved SOLOv2 model

YE Zenan, LI Ziyin, HE Jianjun, WANG Xiaodong, YE Fei, LIU Weihong

Journal of Textile Research. 2026, 47(1):  80-88.  doi:10.13475/j.fzxb.20250501801

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Objective In order to address the persistent challenges in wool and cashmere fiber recognition in small training datasets, strong dependence on high-resolution microscopy, and poor performance with intertwined fibers, a novel recognition framework is proposed and validated. It integrates a frequency-domain multi-focus image fusion technique with an improved instance segmentation model SOLOv2. The framework aims to enhance source imagery quality and subsequently improve the segmentation accuracy and robustness of the model, providing a reliable technological solution for automated fiber analysis in industrial settings.

Method A series of multi-focus images of wool and cashmere fibers were captured and preprocessed using spatial filtering and morphological operations. These images were then fused in the frequency domain via a Fourier transform coupled with a Gaussian kernel filter to generate all-in-focus, high-quality representations. Building upon this, a comprehensive dataset comprising 11 799 precisely annotated images was constructed. The recognition model, built upon the SOLOv2 architecture, incorporates a Swin Transformer as its backbone for superior hierarchical feature extraction and replaces the standard Feature Pyramid Network (FPN) with a Path Aggregation Feature Pyramid Network (PAFPN) to enhance multi-scale feature fusion. In order to improve model generalization, a composite data augmentation strategy involving random cropping, flipping, and high pass was systematically employed during training.

Results In order to quantitatively evaluate the effectiveness of the proposed depth-of-field fusion algorithm, a comparative experiment was carefully designed and conducted on a set of multi-focus fiber images captured under identical microscopic conditions. This ensured fairness of comparison and eliminated potential biases caused by variations in illumination, magnification, or sample preparation. The proposed frequency-domain algorithm demonstrated clear superiority over conventional fusion methods, including wavelet transform and Laplacian pyramid fusion. By effectively combining high-frequency and low-frequency information, the algorithm produced fused images with both sharper edge detail and stronger structural integrity. Quantitative analysis further confirmed that the fused images achieved an average information entropy of 0.80, a spatial frequency of 153.57, and an average gradient of 126.01. Such metrics indicate that the fused images contain richer texture detail, clearer contours, and enhanced information content, all of which are critical for resolving ambiguous cases of overlapping and entangled fibers that frequently occur in textile inspection. The validated fusion method also enabled the creation of a large, high-quality dataset containing 11 799 samples, which served as a solid foundation for subsequent model training and evaluation. Building upon this dataset, the performance of the improved SOLOv2 model was rigorously assessed through comparative experiments with several established instance segmentation frameworks. The results showed that the proposed model significantly outperformed existing benchmarks, achieving a mean average precision (mAP) of 96.85% on the test set. This value was notably higher than those of Mask R-CNN, Yolact, and the original SOLOv2 with a ResNet-50 backbone. In order to disentangle the contributions of individual improvements, systematic ablation studies were conducted. Experimental results demonstrate that replacing the backbone with Swin Transformer significantly increased the mAP from 94.12% to 95.90%, fully verifying its superior capability in feature representation. Meanwhile, substituting the FPN structure with PAFPN improved the detection accuracy to 94.81%, confirming the positive contribution of enhanced multi-scale feature fusion to model performance. Under the synergistic effect of these two improvement strategies, the model achieved the final mAP of 96.85%. Qualitative evaluations complemented these quantitative results, revealing that the segmentation masks generated by the improved model exhibited smoother contours, higher fidelity to fiber boundaries, and a notable reduction of artifacts, particularly in challenging cases involving densely intertwined fibers where other models often failed.

Conclusion The empirical results conclusively demonstrate the efficacy of the proposed two-stage framework. The frequency-domain filtering-based depth-of-field fusion algorithm effectively overcomes the reliance on pristine and high-resolution imaging inherent in conventional methods, yielding superior image quality that facilitates subsequent analysis. Meanwhile, the improved SOLOv2 model, enhanced by Swin Transformer and PAFPN, excels at accurately identifying and segmenting interlaced fibers, producing high-quality masks with smooth, artifact-free edges. Achieving an average precision of 96.85% on the challenging wool and cashmere fiber recognition task validates the synergy between advanced image preprocessing and state-of-the-art network architecture. The developed solution not only presents a high-performance approach for a specific textile analysis problem but also provides valuable insights for other microscopic image segmentation tasks facing similar challenges.

Textile Engineering

Simulation and experimental study on airflow field and fiber motion in air-jet vortex spinning

FU Jiaqi, JI Chenxiang, YANG Ruihua

Journal of Textile Research. 2026, 47(1):  89-97.  doi:10.13475/j.fzxb.20250503901

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Objective The quality of yarn produced by airjet spinning is highly dependent on the airflow field characteristics inside the nozzle. However, the nozzle's complex structure and small internal space make direct experimental measurement of the airflow field challenging. In oder to clarify the airflow variation and fiber motion rules in the twisting chamber during spinning, finite element simulation was employed to investigate the airflow and fiber motion during both initial and staple spinning stages.

Method First, a three-dimensional geometric model of the air-jet vortex spinning nozzle was established, followed by mesh division and boundary condition configuration. Numerical simulation of the flow field in the standard nozzle model was then performed, with in-depth analysis of the flow patterns inside the twisting chamber. Second, a discretized flexible fiber model was developed to characterize the fiber's physical and mechanical properties. Finally, coupled simulations were conducted using Rocky DEM 2022R1 and ANSYS Fluent 2022R1 to simulate the fiber's dynamic behavior during the yarn piecing and twisting processes of air-jet vortex spinning, and systematic spinning experiments were carried out accordingly.

Results The results indicate that the rotational suction effect of the high-speed swirling airflow induces negative pressure at the nozzle inlet to draw in fibers. Meanwhile, it drives the airflow in the twisting chamber to move upward and forms a backflow region in the fiber feeding channel. The opposite directions of radial and axial airflow at different radial positions are crucial for ensuring the smooth convergence of fibers into the twisting chamber for entanglement and twisting, thereby forming a core-sheath yarn structure (outer fibers wrapping core fibers). The distribution pattern of the airflow field in the nozzle twisting chamber during the initial spinning stage is similar to that in the steady spinning state. However, affected by the airflow in the hollow tube, the negative pressure and airflow velocity increase as the position approaches the hollow spindle orifice, while the gradient becomes less significant near the guide needle. A distinct pressure and velocity gradient distribution is observed in the guide channel.

Fiber motion within the nozzle in the spinning stage can be divided into four stages, corresponding to the following periods: fiber passage through the inlet and outlet of the fiber feeding channel, the inlet and outlet of the twisting chamber, fiber residence on the hollow spindle surface, and passage through the inlet and outlet of the guide tube. Fiber velocity increases gradually in the fiber feeding channel. Upon entering the twisting chamber, the acceleration increases further, leading to a higher speed. As the fiber moves forward and contacts the hollow spindle surface, its speed decreases gradually before entering the guide tube. After entering the guide tube, the speed increases again and peaks at the guide tube outlet. The fiber velocity variation in the yarn piecing stage follows the same trend.

Conclusion This study conducts an in-depth investigation into the airflow characteristics within the twisting chamber, analyzes the motion characteristics of fibers during both the yarn piecing stage and the spinning stage, and reveals the coupled motion mechanism between the airflow and fibers inside the nozzle. The findings are of great significance for optimizing the spinning process and designing key structural components. Currently, fiber simulations are limited to motion pattern variations. Future research could extend to simulating fiber aggregation, twisting, and yarn piecing processes, which would facilitate a more comprehensive understanding of the laws governing fiber motion and morphological evolution. Owing to computational resource limitations, the current simulations are constrained by the number of fibers and time steps. Future work should focus on expanding the simulation scale (i.e., increasing the number of fibers and prolonging the simulation duration) and simulating continuous fiber feeding to better replicate actual production scenarios.

Technical principle and experiment of real-time core yarn reverse twisting method for snarl-free covered yarns

XU Haowen, AO Limin

Journal of Textile Research. 2026, 47(1):  98-105.  doi:10.13475/j.fzxb.20250303501

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Objective In order to address the snarl problem of non-elastic covered yarns caused by the internal stress generated by the outer yarn wrapping, a real-time core yarn reverse twisting method is proposed, aiming at offsetting the reverse twisting moment generated by the outer yarn wrapping through the reverse twisting moment generated by the reverse twisting of the core yarn, so as to achieve a stable structure of snarl-free covered yarns. The core tasks include the verification of the feasibility of two core yarn twisting technologies, i.e. hollow spindle mechanism and doubling twisting mechanism, the clarification of the principle of twist direction matching, and the exploration of the influence of the reverse twisting twist of the core yarn on the snarl index of the covered yarn with a given wrapping twist.

Method Two technologies for realizing the twisting of the core yarn on the hollow spindle covering machine were adopted. The first is twisting by the hollow spindle mechanism, where based on the technical feature of the hollow spindle mechanism of wrapping when in the presence of a core, and twisting when in the absence of a no core, the original hollow spindle components of the equipment were utilized, and the reverse twisting of the core yarn was achieved through the series configuration of the upper-row and lower-row. The second is twisting by the doubling twisting mechanism. The doubling twisting mechanism was utilized to replace the lower-row of hollow spindle mechanisms to achieve the real-time twisting of the core yarn. The advantages and disadvantages of the two twisting methods were analyzed. Polyester filament covered yarns with different levels of core yarn twisting were spun using the hollow spindle twisting mechanism (core yarn 167 dtex, outer wrapping yarn 83 dtex, wrapping twist 500 twists/m); wool/polyester covered yarns with different core yarn twists were spun using the doubling twisting mechanism (28 tex wool yarn as the core yarn, 111 dtex polyester filament as the outer wrapping yarn, and the wrapping twist of 800 twists/m). The closed-loop method (GB/T 7960.6—2013) was adopted to measure the snarl index of the covered yarns, and the influence of twist matching on torque offset was analyzed. The tensile properties were tested by CRE method (GB/T 3916—2013) to investigate the influences of the core yarn twist on covered yarns.

Results For polyester filament covered yarns, when the reverse twist of the core yarn increased from 0 to 400 twists/m, the average value of the snarl index decreased from 140.0 twists/m to 11.6 twists/m, representing a decrease of over 90%. The increase in the twist significantly reduced the self-twisting snarl, and the yarn tended to be stable. For wool/polyester mixed-color covered yarns, as the reverse twist of the core yarn increased, the snarl index of the covered yarn decreased. When the core yarn twist reached 200 twists/m, the snarl index is close to zero (-0.2 twists/m), indicating that the reverse twisting moment was completely offset. For polyester filament covered yarns, the breaking strength increased first and then decreased with the increase of twist of the core yarn, and the elongation at break of the covered yarn was higher than that of the core yarn without twisting, but the change with the twist of the core yarn was not significant. For wool/polyester mixed-color covered yarns, the breaking strength decreased with the increase of twist of the core yarn, and the elongation at break of the covered yarn was lower than that of the core yarn without twisting, but no significant change occurred with the increasing twist of the core yarn.

Conclusion The anti-twisting torque generated by the outer wrapping yarn when wrapping the core yarn causes the non-elastic single-covered yarn to exhibit a snarling phenomenon similar to yarn twisting. By using a twisting mechanism to first apply real-time twisting to the core yarn in a direction opposite to the wrapping twist direction, followed by the covering process, the anti-twisting torque from the core yarn twisting can offset the anti-twisting torque produced by the outer wrapping yarn during covering. This achieves torque balance inside the covered yarn and produces non-snarling covered yarn. Core yarn twisting can be achieved using a hollow spindle mechanism, leveraging its characteristic of wrapping when in the presence of core and twisting when in the absence of a core, or it can be realized with a general-purpose double-twisting mechanism. The former has a smaller core yarn package capacity and is more suitable for core yarns with smaller linear density. The latter, however, requires the core yarn to have higher strength to overcome processing tension, but it can achieve high twist at low speed. For filament core yarns, the twisting direction of the core yarn and the wrapping twist direction only need to meet the requirement of being in opposite configurations. For staple fiber core yarns, due to their inherent initial twist, the reverse twisting direction of the core yarn must be the same as its initial twist direction, while the wrapping twist direction must be opposite to the initial twist direction of the core yarn. The snarl index of the covered yarn decreases as the reverse twist level of the core yarn increases, until the non-snarling state is achieved. The required reverse twist level of the core yarn to achieve non-snarling covered yarn varies depending on the configurations of the core yarn and outer wrapping yarn, as well as the wrapping twist level of the outer wrapping yarn. It must be determined through experiments on the variation of the core yarn's reverse twist level. Twisting the core yarn changes the structure of the covered yarn, which in turn leads to changes in its properties. For different combinations of core yarn and outer wrapping yarn, as well as different wrapping twist configurations, the variation trends of the covered yarn's performance indicators will differ with changes in the core yarn's twist level. Specific analysis through experiments is therefore required.

Macro-mesoscopic coupled analysis study and numerical simulation of mechanical behavior for staple yarns

ZHAO Zewen, LÜ Kuan, SU Xuzhong, SUN Fengxin

Journal of Textile Research. 2026, 47(1):  106-114.  doi:10.13475/j.fzxb.20250503301

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Objective The main aim of this study is to quantitatively study the influence of the meso-structure of staple yarns under low stress on its macroscopic mechanical properties, so as to solve the problems of low efficiency, high time consumption in the current simulation research on the tensile properties of staple yarns. A detailed discussion of meso-structural parameters, such as twist, fiber orientation, scale effect between fibers, and the related physical properties was conducted to reveal the meso-mechanical mechanism and macro-mechanical behavior of staple yarns.

Method An equivalent staple yarn stretching model was established. By leveraging finite element analysis, the yarn stretching process with embedded meso-mechanical factors was simulated and analyzed. Briefly, the yarn sample was observed by VHX-5000 ultra-depth digital microscope to obtain geometric parameters, and the yarn finite element modeling was performed with the help of SoliWorks software. Combined with actual measurement, the effectiveness of the finite element simulation stretching yarn model was demonstrated. Finite element analysis software ABAQUS was utilized to simulate and analyze the three influencing factors i.e. the staple yarn twist, overlap fiber length and the fiber curve angle. The influences of friction coefficient and elastic modulus on the tensile properties of yarn were discussed, and the mechanisms affecting the macroscopic mechanical properties of yarn were investigated.

Results According to the simulation results, the stress changes of the yarn were observed. It was found that yarn twist was the main factor affecting the mechanical properties of spun yarns, and the twist of 4 twists/(10 cm) was determined as the critical value for converting the fiber slip failure to the fiber broken failure in the yarn. The overlap fiber length was also identified as the main factor affecting the mechanical properties of spun yarns, and the maximum yarn strength and the overlap fiber length showed a typical nonlinear relationship and the critical conversion value of the overlap length was 200 mm. The simulation also revealed that the larger was the fiber orientation angle in the yarn, the smaller were the tensile modulus and strength of the yarn. When the fiber orientation angle was smaller than 5°, the yarn strength became very low, leading to early failure, indicating that the fiber orientation angle was governed by the yarn twist and was a key factor affecting the tensile properties of the spun yarn. The inter-fiber friction coefficient and elastic modulus of the fiber demonstrated a great influence on the tensile behavior of the yarn. With the increase of the friction coefficient and elastic modulus of the yarn, the tensile force shows an increasing trend. For every 0.1 increase in the friction coefficient, the maximum tensile force increases by 3.63 N; the elastic modulus increases by 50 MPa, and the maximum tensile force increases by 6.98 N. Comparing the simulation results with the test results, it is found that the morphology and deformation of the yarn are highly similar during the stretching process, and the relative errors of each mechanical parameter are within 4%. The correlation coefficient between the test displacement and the simulated tensile force is 0.962, and the correlation coefficient between the simulated displacement and the test tensile force is 0.967. Both are significantly correlated at the 0.01 level.

Conclusion In order to quantitatively study the influence of the microstructure of spun yarn under low stress conditions, specifically, the mechanical behavior prior to yarn failure dominated by mechanisms such as fiber slippage and structural reorganization rather than breaking of the fibers within the yarn, on its macroscopic mechanical properties and better evaluate the mechanical properties of spun yarn, this paper simulates and verifies the tensile process of spun yarn using the finite element method. With the help of professional modeling software SolidWorks, an equivalent yarn geometric model is established using an array of interlaced continuous yarns, namely a novel and simplified approach that effectively captures key meso-structural features of staple yarn while maintaining computational efficiency in simulation. The finite element analysis software ABAQUS is utilized to simulate and verify the micro-mechanism of yarn tensile mechanical behavior from three perspectives, including fiber overlap length (scale effect), fiber orientation (deflection angle) and yarn twist. The finite element simulation results were compared with the experimental results. The simulation results show high consistency with experimental data, with a correlation coefficient of 0.962 (p<0.01), confirming the validity of the finite element model for analyzing the tensile properties of spun yarn. In subsequent studies, yarns with different raw materials and different structural parameters can be selected for simulation tests to further study the tensile properties of spun yarn.

Preparation of elastic conductive yarns with internal spiral structure and regulation of their strain-insensitive performance

LIU Yiming, LI Lin, DU Xianjing, LIU Pan, YIN Xia, TIAN Mingwei

Journal of Textile Research. 2026, 47(1):  115-122.  doi:10.13475/j.fzxb.20250302901

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Objective Elastic electronic yarns play a significant role in the field of smart wearable electronic textiles. However, the conventional elastic conductive yarns have a strain-sensitive problem, which not only hinders the lossless transmission of signals but also limits their applications in stretchable devices. The aim of this study is to enhance the mechanical properties of these yarns by endowing them with both good elasticity and sensing performance. This research is crucial as it is essential for the development of high performance wearable electronics in meeting the growing market demand for reliable and multifunctional smart textiles.

Method The study utilized the principle of combining materials with complementary characteristics. Silver-plated nylon yarn with excellent conductivity and polyurethane resin with high elasticity were selected. The coaxial wet spinning process was employed, where the polyurethane resin solution was extruded through a concentric spinneret to encapsulate the silver-plated nylon yarn, forming a core-sheath structure. Differential stretching was then carried out by adjusting the drafting ratio between the extrusion speed and the winding speed. The prepared coaxial yarns were characterized using surface electron microscope to observe the micro-structure, tensile tester to measure the mechanical properties, and parameter analyzer to test the electrical properties.

Results SEM images revealed a distinct internal spiral structure in the prepared conductive yarns. This structure was highly correlated with the drafting ratio during the manufacturing process. As the drafting ratio increased, the number and density of the spiral structures within the yarn were notably augmented. When the drafting ratio was 1∶1, the resistance change rate of the conductive yarn reached 90%, indicating excellent conductive sensing performance. When applied for smart sports wristband, it was able to detect the slightest change in muscle tension during exercise. Slight muscle twitching, equivalent to slight stretching, triggers significant resistance changes, thus facilitating accurate monitoring of sports-related actions. When the drawing speed ratio was 1∶5, remarkable mechanical and electrical properties emerged. The resistance change rate of the conductive yarn under 300% strain is as low as 5.6%, showing outstanding stain-insensitive performance and its high elasticity. At 30% tensile strain, the change rate of resistance maintained remarkable stability in 2 000 cycles, which suggests suitability for flexible sensors in smart clothing. These sensors need to maintain a stable electrical connection during daily wear, while stretching and bending. The low resistance changes ensured reliable signal transmission, and high elongation enabled the sensor to withstand repeated mechanical stress without abrupt change, thus improving the long-term function and durability of the smart clothing.

Conclusion This study successfully developed elastic conductive yarns with tunable properties. The higher drawing ratio of 1∶5 led to a densely coiled internal structure, resulting in low resistance change under strain, high elongation at break, and excellent stability during cyclic stretching. Conversely, a 1∶1 drafting ratio provided remarkable conductive sensing performance. The strain-insensitive yarns find applications in stretchable electronics requiring stable conductivity, such as flexible sensor in smart clothing. The highly sensitive ones can be used in precise motion-sensing wearable devices. This research also shows that further optimization of spinning and drawing parameters could produce yarns with better properties. Future work might explore the integration of other functional materials into the yarn, enduring the yarn with additional characteristics such as self-repair or antibacterial capabilities. Generally speaking, this study paves the way for developing the next generation of the smart wearable electronic textiles.

Construction and sensing performance of all knitted multi-modal flexible capacitive sensor

SHAO Jianbo, YUE Xinyan, CHEN Yu, HAN Xiao, HONG Jianhan

Journal of Textile Research. 2026, 47(1):  123-131.  doi:10.13475/j.fzxb.20250602201

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Objective Flexible sensors, as the core components of intelligent textiles, have become a new research hotspot. Multi-modal sensors have a broader market prospect by virtue of their ability to monitor various external signals. At present, research on multi-modal flexible capacitive sensors mainly focuses on three-dimensional and one-dimensional structures. Three-dimensional flexible sensors are difficult to miniaturize due to their relatively large thickness and area. Long time wearing of one-dimensional flexible capacitive sensors has been reported to cause a sense of compression or friction discomfort to the skin. Therefore, it is of great significance to develop a two-dimensional knitted flexible sensor that is comfortable to wear and well integrated with clothing.

Method Silver-coated polyamide fiber (SCP) was used as the core yarn, on which double-layer polyester (PET) was wrapped, with the inner layer being S-twist and the outer layer being Z-twist, and the twist factor was 1 000 twists/m. PET/SCP yarn was thus prepared. A layer of waterborne polyurethane (PU) was coated on the outer surface of the PET/SCP yarn to obtain PU-PET/SCP composite conductive yarn, which was used as raw material to prepare an all knitted multi-modal flexible capacitive sensor. The influence of coating process on the performance of core yarn was studied. The tensile, pressure and non-contact sensing properties of the sensor were analyzed, and the sensor was applied to human activity monitoring.

Results After PET wrapping and PU coating, the mechanical properties of the composite yarns were significantly improved, with the breaking strength and elongation at break increased by 216.9% and 9.33%, respectively. The composite conductive yarns were knitted into a flexible capacitive sensor with the plain knitted structure on a computerized flat knitting machine, and the sensor was designed to have multi-modal external force detection capabilities. In the tensile sensing tests in both the transverse and longitudinal directions, the capacitance of the sensor gradually increased with the increase of strain. The maximum sensitivity coefficient reached 0.324 3. Under the condition of stretching at a speed of 8.8 mm/s for 250 s at different elongation rates for a total of 1 000 s, the capacitance change of the sensor was relatively stable, demonstrating good repeatability. The sensor had good linearity, with R² values of the fitting equations being higher than 0.97 at different elongation rates, indicating that within a certain strain range, the ΔC/C₀ and elongation rate of the sensor showed a good linear correlation. In the pressure sensing test, by placing weights on the sensor, it was found that the sensor had good recognition ability for weights of different masses, and the capacitance change was stable for weights of the same mass, demonstrating good pressure sensing characteristics. In the non-contact sensing performance test, the sensor demonstrated multi-directional sensitivity. When an object approaches in either the vertical or horizontal direction, the sensor can display a relatively stable capacitance change signal. When an object approached the sensor without contact, the capacitance of the sensor gradually decreased with both the diminishing distance to the object and the increasing area of the object. This sensor demonstrates excellent non-contact sensing capabilities. It can identify the size (from a finger to a palm) and speed of approaching objects, and the corresponding relative capacitance changes are respectively significant. The speed detection was validated at specific vertical and horizontal palm movement frequencies.For human body monitoring, it precisely measures arm and knee bending (by 0°-90°) and detects respiratory rate by capturing the periodic capacitance variations caused by abdominal movements during breathing.

Conclusion A core-spun yarn structure was fabricated using SCP as the core yarn and PET as the sheath, subsequently over-coated with a PU layer to produce PU-PET/SCP composite conductive yarn. The composite conductive yarn exhibited excellent mechanical properties. The knitted capacitive sensors based on the PU-PET/SCP composite conductive yarn demonstrated the capability to monitor three types of external loading, i.e. stretching, pressure, and non-contact interactions. The two-dimensional knitted structure of the sensor features compact dimensions in both area and thickness, ensuring enhanced wearing comfort. Owing to these characteristics, this sensor shows significant application potential in fields such as human motion monitoring, healthcare, and robotics.

Dyeing and Finishing Engineering

Preparation of biomass polyphenol-ferrous ion multicolor dyes and its application on cotton fabrics

REN Xiao, PAN Linjie, JIANG Haixia, GE Fengyan, GAO Hongguo

Journal of Textile Research. 2026, 47(1):  132-141.  doi:10.13475/j.fzxb.20250703101

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Objective The development of bio-based dyes is of great importance for sustainable fabrics. However, natural dyes often suffer from low yield, poor stability, and a limited color range. Microbial pigments offer functionality but are costly and difficult to purify. Existing polyphenol-based dyes mostly produce dull yellow-brown tones, limiting their application in multicolor fabrics. Additionally, conventional dyeing methods are energy-intensive and environmentally unfriendly. Therefore, it is essential to develop a sustainable, multicolor, and functional dyeing system using natural resources, suitable for cotton fabrics under mild and eco-friendly conditions.

Method Three natural polyphenols with different structures, such as caffeic acid, protocatechuic acid, and chlorogenic acid, were complexed with ferrous ions to prepare polyphenol-Fe²⁺dyes. The influences of reaction conditions on dye color were investigated. Polyethyleneimine (PEI) grafting modification was applied to enhance dye affinity for cotton fibers. Optimal dyeing parameters were determined, and the dyed fabrics were evaluated for pH-responsive color change and UV protection. Characterization was performed using UV-visible spectroscopy, Fourier fransform infrared spectroscopy(FT-IR), scanning electron microscopy(SEM), and spectrophotometric color measurement.

Results The study demonstrated that three natural polyphenols, i.e. caffeic acid, protocatechuic acid, and chlorogenic acid, formed effective complexes with ferrous ions to produce polyphenol-Fe²⁺ dyes with distinct colors. Optimal molar ratios were 1∶1 for caffeic and protocatechuic acids, and 2∶1 for chlorogenic acid. The complexation was pH-dependent, with pH7 yielding the highest dye concentration and brightest colors by virtue of enhanced coordination from phenolic hydroxyl deprotonation. The dyes showed significant pH-responsive color changes, suitable for pH monitoring. Cotton fabrics modified with polyethyleneimine (PEI) overcame electrostatic repulsion and improved dye uptake. PEI with a molecular weight of 3 000 at 5%(o.w.f) dosage provided the best modification effect. Optimal dyeing conditions were 10 mmol/L dye concentration and 60 ℃ temperature, maximizing color strength (K/S value) without degrading the dye complex. Characterization via elemental mapping and FT-IR confirmed uniform dye adsorption and coordination bonds between polyphenols and ferrous ions. Furthermore, the level dyeing propertyies (S_r values) of the three dyed fabrics were all smaller than 0.05, indicating that under the optimal process, all three dyes exhibited good uniformity in dyeing the fabrics. The dyed fabrics exhibited excellent color fastness, exceeding standard requirements for rubbing and washing. The fabrics also displayed clear color changes in alkaline solutions, confirming their application potential in alkaline environment sensing. Additionally, the polyphenol-Fe²⁺ dyes significantly enhanced UV protection, reducing UV transmittance to about 0.05% through extended conjugation in the complexes.

Conclusion Caffeic acid (CA), protocatechuic acid (PCA), chlorogenic acid (CGA) were complexed with ferrous ions to prepare natural dyes, which were applied to cationized cotton fabrics, producing black, purple, and brown colors. The dyed fabrics exhibited excellent rubbing fastness, uniform coloration, and clear color changes in alkaline environments (pH>9). Additionally, all dyed fabrics showed high ultraviolet resistance (UPF>50), confirming their effective UV-shielding performance. This work offers a sustainable method to produce multifunctional cotton fabrics with pH-responsive and UV-shielding properties via natural polyphenol-metal complexation and fiber surface modification.

Antibacterial finishing of wool and silk fabrics with Ginkgo Biloba flavonoids

GU Jiayu, ZHANG Weidong, DONG Yongchun, SUN Xuan, XU Liangjun

Journal of Textile Research. 2026, 47(1):  142-150.  doi:10.13475/j.fzxb.20250205601

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Objective In order to further explore and enhance the added value of colorless or light-colored plant extracts in the field of textile functionalization, this study focuses on utilizing flavonoids derived from Ginkgo Biloba leaf extracts (GBL-e) to impart antibacterial properties to two fabrics (wool and silk fabrics). While much existing research emphasizes the dyeing potential of pigmented plant extracts, this work intentionally targets underutilized, low-color botanical resources that are rich in bioactive compounds, aiming to provide an eco-friendly and sustainable approach to fabric finishing, aligning with growing demands for green manufacturing processes. The research systematically evaluates the extraction, identification, adsorption behavior, and antibacterial efficacy of GBL-e flavonoids on wool/silk substrates, thereby offering a comprehensive strategy for valorizing plant waste and expanding the functional applications of natural extracts in fabrics.

Method Initially, the flavonoid-rich Ginkgo Biloba leaf extract (GBL-e) was prepared using an ethanol/water extraction technique. Following extraction, high performance liquid chromatography (HPLC) was employed to qualitatively and quantitatively identify the specific types of flavonoids present in GBL-e, offering critical insights into its composition. Subsequently, the two fabrics were subjected to functional finishing treatment using the as-prepared GBL-e solution. Throughout this finishing process, the adsorption behavior and binding mechanism of flavonoids onto the wool/silk substrates were rigorously examined using established adsorption reaction models, enabling a deeper understanding of the interaction kinetics and equilibrium characteristics. Finally, the antibacterial efficacy of the treated fabrics was evaluated to determine their potential for practical applications in bioactive fabrics.

Results The retention time of the HPLC spectrum of GBL-e was the same as that of that of the mixed standards (quercetin, kaempferol and isorhamnetin), which were 10.657 min, 23.288 min and 41.247 min, respectively, suggesting that these three flavonoids were present in GBL-e; After the determination of flavonoids of Ginkgo Biloba leaves collected in different seasons, it was found that although the flavonoid content of the leaves was the highest in June, but the flavonoid content of Ginkgo Biloba leaves in November did not decrease a lot, which indicated an additional value of Ginkgo Biloba fallen leaves. In the process of finishing protein fabrics with GBL-e, the flavonoid adsorption amount (Q_fla value) and dye-uptakes (E_t) and K/S curves of the two protein fabrics showed a gradual increase in the trend with the prolongation of the finishing time; moreover, by increasing the concentration of flavonoids in the finishing solution, the Q_fla,e values of the two fabrics also increased. In terms of kinetic modeling, the correlation coefficients of quasi-primary kinetics of flavonoid adsorption of GBL-e on the two fibers were poor, while the fitting correlation coefficient (r²) of quasi-secondary kinetics was above 0.99, so the quasi-secondary kinetics was more in line with the process of the adsorption of flavonoids on the protein fabrics. In terms of thermodynamic modeling, the Langmuir and Freundlich adsorption isothermal equations of flavonoids from GBL-e on both wool/silk fabrics reached r² values above 0.98, but the Langmuir isothermal adsorption equation had a higher r² value than that of the Freundlich isothermal adsorption equation. In the presence of the control fabrics only, the medium contained more of colonies in the medium decreased significantly in the presence of GBL-e finished protein fabrics, which implies that both GBL-e finished protein fabrics have significant antibacterial activity.

Conclusion The main chemical components in GBL-e quercetin, kaempferol and isorhamnetin; prolonging the finishing time and increasing the concentration of flavonoids contribute to the adsorption of flavonoids on the protein fabrics. The adsorption process of GBL-e on the protein fibers conforms to Lagergren's quasi second-order kinetic model, and the isothermal model of Langmuir and Freundlich adsorption. The GBL-e-finished wool/silk fabrics had good antibacterial activity.

Aging behavior of high-strength polyimide fabrics under various environmental factors

LAN Hanyu, CHEN Xin, LIANG Dongxu, ZHAO Xin, ZHANG Qinghua

Journal of Textile Research. 2026, 47(1):  151-158.  doi:10.13475/j.fzxb.20250502501

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Objective As a representative of advanced polymer materials, polyimide fibers show irreplaceability in extreme service environments such as military and aerospace with their unique molecular structure and performance synergy. However, the outstanding properties of polyimide will inevitably be affected by prolonged exposure to extreme conditions such as high-energy irradiation and atomic oxygen. This research is an attempt to explore in-depth the aging mechanism of polyimide fibers and fabrics under multiple environmental factors, so as to further improve the stability and reliability of the materials for the intended applications.

Method The aging test chamber was used to evaluate the radiation resistance, corrosion resistance and other properties of materials by simulating and accelerating environmental factors, from which the performance degradation of products in extreme environments was predicted. According to the GJB 150.10A-2009 standard, polyimide fabrics were placed in an aging test chamber with different experimental conditions to separately simulate mold, xenon lamp and salt spray aging environment before they were cleaned to remove surface contaminants by washing. In addition to the environmental aging tests, the fabrics underwent another abrasion aging test using a Martindale abrasion tester, referring to the GB/T 21196.2—2007 standard. The properties and structure of fabrics were analyzed by mechanical property testing, thermos gravimetric analysis (TGA), Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM).

Results The initial polyimide fabric showed a warp tensile strength of 705.66 N/cm and an elongation at break of 9%. After undergoing mold, xenon lamp, abrasion, and salt-spray aging respectively, the tensile strength and elongation at break of the fabric decreased to various degrees. The retention rates of fabrics warp tensile strength were 91.44%, 89.35%, 87.48%, and 85.29%, respectively, still remaining at a relatively high level. The elongation at break of the different aged samples all decreased to around 7%, indicating that the flexibility of the fabric has declined. The thermal decomposition temperatures of the polyimide fabrics decreased after aging. The correlation between mechanical properties and heat resistance of fabric demonstrated that the experimental aging schemes indeed caused damage to the actual performance of the polyimide fabric to some extent. Subsequently, the chemical structures of the fabric before and after aging were investigated. The positions of different characteristic absorption peak in the FT-IR spectra of these samples did not change. In the characteristic XPS spectra of C 1s for the initial fabric, the proportions of C—O and C—N were 4.19% and 5.05%, respectively, and after different aging treatments, the proportions of both decreased. In contrast, an increase in the peak area of the C=N and the emergence of C^*—C=O peak were observed, confirming that all three aging conditions affected the chemical structure of the polyimide. Mould, xenon lamp, and salt-spray aging treatments have no significant effect on the overall macroscopic morphology of the fabrics. The original surface morphology of the fiber was smooth, but the surface roughness increased after the above three aging treatments, displaying obvious etching grooves and partial peeling. After abrasion and stretching, the regularity of fabrics in warp direction deteriorated significantly. For the sample after abrasion aging, fibers showed obvious bending, the smooth surface structure was completely destroyed, and a structure similar to microfibrils emerged. For the fabric after warp tensile test, the longitudinal fibers exhibited obvious layered fracture morphology and “V-shaped” fracture notches. Finally, the possible aging mechanisms of polyimide fabrics under different environmental conditions were explored.

Conclusion The research results show that mold, xenon lamp, salt spray and abrasion aging negatively affected the mechanical properties and heat resistance of the high-strength polyimide fabrics to various degrees, and salt spray aging appears to be the most significantly damaging factor which reduced the tensile strength and elongation at break of the fabrics by 14.71% and 28.44%, respectively, and caused decrease in thermal weight loss temperature. After aging under different environmental conditions, the surface smoothness of fibers decreased, and microscopic defects such as grooves and peeling appeared. The chemical structure of fiber before and after experiment was analyzed, and the aging mechanism may be related to the destruction of chemical bonds such as C—N and C—O, as well as long molecular chains or conjugated structures in the polyimide macromolecular chain.

Preparation and application of low-temperature high-efficiency scouring agent

FENG Pinqi, ZHANG Lining, WANG Nana, LÜ Zhong, ZHOU Cun

Journal of Textile Research. 2026, 47(1):  159-167.  doi:10.13475/j.fzxb.20250704601

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Objective Conventional cotton fabric pre-treatment processes are plagued by high energy consumption, extensive water usage, and significant fiber damage due to their reliance on high temperatures and strong alkali. In order to address these critical limitations, this study aimed to develop a novel, low-temperature high-efficiency scouring agent. The primary objective was to design a synergistic surfactant composite that enables a short process under mild conditions, thereby reducing environmental impact while preserving fabric integrity. Establishing an optimized low-temperature bleaching process was also a key task to validate the agent's industrial applicability.

Method A low-temperature high-efficiency scouring agent was designed and prepared by compounding anionic surfactant sodium alpha-olefin sulfonate(AOS), nonionic surfactant isotridecanol ethoxylate, and amphoteric surfactant tetradecyl dimethylamine oxide (OA-14) at a weight ratio of 3∶7∶1. The short process utilizing low temperature and low alkali was systematically studied and optimized via single-factor experiments. The mechanism of action was investigated through surface tension measurements, wetting performance characterization, and comprehensive stability analysis. This approach revealed that the ternary system forms mixed micelles, leading to a synergistic effect that lowers the critical micelle concentration (CMC) and enhances surface activity. The optimal one-bath low-temperature scouring and bleaching process parameters were determined, where the scouring agent 1.4 g/L (based on active content), NaOH 3.5 g/L, Na₂SiO₃ 2 g/L, 30% H₂O₂ 6-14 g/L, process temperature of 85 ℃, and treatment time of 55 min.

Results The prepared low-temperature high efficiency scouring agent demonstrated excellent performance. It exhibited superior surface activity, with a remarkably low CMC of 0.01% and a surface tension at CMC (γ_cmc) of 25.33 mN/m, indicating high efficiency at low usage levels. The wetting time was significantly short, measured at just 4.43 s, which is crucial for rapid and uniform treatment. Furthermore, the agent showed outstanding stability, including good alkali resistance, oxidation resistane, and high-temperature stability, ensuring its robustness under the intended application conditions.

When cotton fabrics were treated using the optimized one-bath low-temperature scouring and bleaching process established in this study, the results met key textile performance benchmarks. The capillary effect, which indicates wettability and absorbency, reached an excellent 14.0 cm, demonstrating effective removal of hydrophobic impurities like waxes. The whiteness of the fabric was measured at 81.23%, confirming successful bleaching and removal of natural pigments. Importantly, the fabric's mechanical strength was well-preserved despite the chemical treatment; and the breaking strength retention rate was 82.12%. This high retention value is a direct benefit of the milder low-temperature process compared to conventional harsh treatments, highlighting the agent's effectiveness in minimizing fiber damage. The synergistic effect within the ternary surfactant system was key to these results. The formation of mixed micelles enhanced the overall surface activity, allowing for effective contaminant removal and wetting at lower concentrations and temperatures than typically required.

Conclusion In conclusion, this study successfully developed a novel ternary composite scouring agent that is both low-temperature efficient and highly effective. The synergistic interaction between the anionic, nonionic, and amphoteric surfactants was identified as the core mechanism, leading to excellent surface activity, wetting power, and stability. The concurrently established optimized one-bath low-temperature scouring and bleaching process, operating at 85 ℃, demonstrates a viable and superior alternative to conventional energy-intensive and fiber-damaging methods. This new process significantly reduces alkali dosage, energy consumption, and water footprint while maintaining high fabric quality, as evidenced by the excellent capillary effect, good whiteness, and, most notably, high strength retention of over 82%. The findings strongly suggest that this approach has significant potential for industrial application, promoting a more sustainable and eco-friendlier pathway for cotton fabric pre-treatment without compromising on performance. Future work could focus on the long-term durability testing of treated fabrics and scaling up the process for industrial evaluation.

Preparation of CO₂-based polyurethane acrylate emulsion and its film properties

LIU Xuying, YIN Qianlin, WANG Xiancheng, FAN Gaoqing, QI Dongming, CHEN Zhijie

Journal of Textile Research. 2026, 47(1):  168-175.  doi:10.13475/j.fzxb.20250601501

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Objective In view of the fact that the current digital inkjet ink adhesives are mainly composed of petrochemical raw materials, with poor renewability and difficulty in achieving both durability and flexibility, the aim of this research is to use CO₂-based polycarbonate propylene carbonate diol (PPCD) as the soft segment, isophorone diisocyanate (IPDI) as the hard segment, hydroxypropyl acrylat (HPA) as the end-capping agent, and butyl acrylat (BA) and methyl methacrylate (MMA) as the copolymer monomers to prepare a water-based polyurethane acrylate (PUA) emulsion with environmentally friendly properties and high durability and flexibility, which is then applied in the preparation of digital inkjet ink.

Method CO₂-based waterborne polyurethane prepolymer (VWPU) was prepared by copolymerization of CO₂-based PPCD, IPDI, HPA and 2,2-dimethylolbutyric acid (DMBA). The VWPU was characterized by Fourier transform infrared spectroscopy (FT-IR) and H nudear magnetic resonance spectroscopy (¹HNMR). Consequently, BA and MMA were introduced into VWPU for emulsion polymerization to obtain CO₂-based PUA.

Results The particle size distributions of emulsions prepared by different polyols are unimodal, and the average particle size of PPCD-PUA was the smallest (50.79 nm), lower than PTMEG-PUA (68.69 nm) and significantly lower than PEG-PUA (146.10 nm). The conversion test results of PUA emulsion showed that the reaction rate of PPCD-PUA was the fastest, which was attributed to the increase of free radical capture efficiency and chain growth driving force of small particle size droplets. The mechanical tests of PUA film showed that the elastic modulus of PPCD-PUA film (42.0 MPa), fracture strength (10.8 MPa) and elongation at break (825%) were between that of PEG-PUA and that of PTMEG-PUA, indicating a soft and tough film. AFM analysis of PUA film revealed that PPCD-PUA had the lowest surface roughness. According to the PUA printing fabric wearing performance test results and SEM images, dry and wet rubbing fastness of PPCD-PUA ink increased to level 4-5. The relative stiffness of PPCD-PUA printed fabrics was found relatively low, enabling a comfortable feel, and the comprehensive performance was confirmed to meet the needs of high-end pigment printing.

Conclusion The VWPU prepolymer with good reactivity was prepared by using PPCD as soft segment, and PUA emulsion with particle size of about 60 nm was prepared by emulsion polymerization. Compared with PEG and PTMEG, PPCD has both ether bonds and ester bonds, resulting in a higher cohesion energy and lower crystallinity. This enables the PUA film produced there from to have a higher elongation at break (825%) and a higher fracture strength (10.8 MPa), meeting the performance requirements of high-end printing adhesives. The printed fabric made of PPCD-PUA has higher dry and wet rubbing fastness, higher softness and better air permeability, which has greater comprehensive advantages than the printed fabric made of PEG-PUA and PTMEG-PUA.

Cross-attention excited color-semantic consistent generation for textile digital printing patterns

ZHANG Hui, ZHOU Xuan, WANG Junwei, DENG Yongmei, ZHANG Kaibing

Journal of Textile Research. 2026, 47(1):  176-185.  doi:10.13475/j.fzxb.20250600501

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Objective Textile digital printing technology is popular in the textile dyeing and printing industry because of its high printing precision, fast production speed, and environmentally friendly processes. However, current digital printing pattern design still relies heavily on manual creation by designers, leading to long cycles, low efficiency, and poor market responsiveness. Leveraging Artificial Intelligence Generated Content (AIGC) technology to achieve rapid text-to-digital printing pattern generation can significantly improve the development efficiency of digital textile products and support user personalized customization, opening up new methods for digital pattern design. Nevertheless, mainstream text-to-image generation models (e.g., Stable Diffusion (SD) trained on generic datasets) have two key limitations, i.e. monotonous generated patterns lacking artistic diversity, and mismatch between the generated colors and the text-described color semantics due to inaccurate color attribute binding. Therefore, it is crucial to develop a specialized generative model for textile digital printing patterns and propose new methods to improve color generation accuracy.

Method A dataset containing 3 090 pairs of digital textile patterns and detailed textual descriptions was first constructed. The SD-V1.5 model was fine-tuned using the Low-Rank Adaptation (LoRA) technology. Subsequently, for the fine-tuned SD model, this study introduced a cross-attention-based color excitation module into the first 70% of the denoising process, without requiring any additional training. This module comprises two loss components, namely the target object color excitation loss and the background color excitation loss. By minimizing these two losses through gradient descent, the model learns to increase the attention weights of color-related keywords in the text prompt, thereby focusing more on the target and background colors of the pattern during generation. Finally, the color semantic consistency was evaluated using the CLIP Score, Text-Text Similarity (T2T-Sim), and a newly proposed Color-Text Accuracy (C2T-Acc) metric.

Results The effectiveness of the proposed method was evaluated through both qualitative and quantitative experiments. Qualitatively, the generated patterns under various text prompts and random seeds showed consistent style, pattern color, and background color with the textual descriptions. Benefiting from the diverse pattern types and richly annotated descriptions in the constructed dataset, the generated results also exhibited strong artistic creativity. Quantitatively, although the introduction of attention excitation caused slight perturbations to noise prediction, resulting in a minor decrease in the CLIP Score metric, the T2T-Sim and C2T-Acc indicators were improved by 2.96% and 8.94%, respectively, compared to the more advanced Structured Diffusion and Attend-and-Excite models. These results indicate that the proposed method not only significantly enhances the color semantic consistency of the patterns but also demonstrates substantial improvements in overall pattern quality and visual fidelity. Furthermore, ablation studies show that both the target color excitation and background color excitation contribute positively to the overall improvement, leading to better color generation, detailed pattern formation, and accurate background rendering.

Conclusion This study investigates cross-modal generation of textile digital printing patterns from textual descriptions. The powerful generative model SD-V1.5 is adopted as the baseline and fine-tuned on a custom-built dataset of textile printing patterns. In order to enhance the consistency between generated pattern colors and color semantics in the text, a cross-attention-based color excitation method is introduced, which increases the attention weights of color-related keywords during generation. Extensive qualitative and quantitative evaluations confirm the effectiveness of the proposed method. With only a textual input, users can efficiently obtain semantically aligned and high-quality textile digital printing patterns, enabling convenient, personalized customization. By leveraging advanced AIGC technologies, the proposed approach establishes a domain-specific generation model for textile digital printing, contributing to the development of new productive forces and facilitating the digital, intelligent, and sustainable transformation of the next-generation textile printing industry.

Apparel Engineering

Model construction for parametric pattern automatic generation based on Python

SUN Weibin, QIAN Juan, YUAN Chengxiao, DU Jinsong

Journal of Textile Research. 2026, 47(1):  186-195.  doi:10.13475/j.fzxb.20250701701

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Objective In garment production, pattern-making usually relies on the experience of manual pattern makers, and this process is ofern associated to low efficiency, high cost, and difficulty in meeting large-scale personalized customization demands. Although 3D reverse engineering can generate body-fitting surfaces, it faces challenges in curve deformation and ease allowance control, less conducive to style modifications. Existing parametric methods rely on specialized CAD software, with limited accessibility. In order to address the creation and adjustment of well-fitting patterns, this study proposes a Python-based parametric pattern generation model for automated pattern creation, providing technical support to shorten the production cycle at the pattern-making stage.

Method This study integrates parametric modeling with self-developed biarc curve fitting algorithm to achieve rapid generation and adjustment of well-fitted garment patterns. Taking the generation of a women's shirt pattern in AutoCAD as an example, the research first investigates two types of biarc curves for constructing garment outlines using Python, along with continuity calculations at their connecting points. Next, by combining the prototype method and short-measurement method from garment structural design, body-related parameters are established. The correlations between body dimensions are obtained through anthropometric experiments, and pattern generation/adjustment rules are incorporated to build an automatic pattern generation model. Finally, the automatically generated patterns are evaluated for pressure distribution and fit using CLO 3D software, verifying the feasibility of this generation method.

Results Based on the fitting principles of two types of biarc curves, this study developed a biarc curve algorithm using Python for automatically generating complex curves in garment patterns. The computational results demonstrate that the curves generated by the biarc algorithm possess G⁰ and G¹ continuity, ensuring smooth linearity of the generated curves with high algorithmic accuracy. All connection points exhibit similar G² deviations while demonstrating variations in curve length. A greater t_c value results in a shorter generated curve. Furthermore, when calculating both the deviations at connection points and the curve length of biarc curves constructed using the incenter method, all values were found to be identical to those obtained with t_c=0.5. This further confirms that the connection points of curves drawn via the incenter method are encompassed within the connection point function described. While maintaining low G² values, the algorithm allows for curve length adjustment, enabling convenient processing of complex structural curves in pattern-making. In regional anthropometric experiments, body measurement data were collected from 200 females in Xinjiang, including length dimensions (height, shoulder width, chest width, back width, back length, bust point distance, arm length) and circumference dimens-ions (waist circumference, bust circumference, neck base circumference). Regression analysis was employed to establish the mapping relationships between key body dimensions, which simplified the construction of the automatic pattern generation model. After incorporating adjustment rules, the model was able to carry out rapid modifications of different pattern sections to generate personalized patterns. The automatically generated shirt pattern was imported into virtual software for seam testing. Pressure and fit tests conducted in CLO 3D software reveal reasonable garment pressure distribution and uniform body-garment clearance, validating the feasibility of the proposed parametric pattern generation method.

Conclusion The Python-generated biarc curves demonstrate both superior smoothness and adjustable length properties, enabling efficient handling of complex pattern contours. The proposed method facilitates rapid pattern generation and modification, providing robust technical support for large-scale personalized apparel pattern production. The anthropometric regression model developed for personalized pattern generation was intentionally designed without body type classification constraints. However, subsequent integration of body type classification with the required measurement data could significantly enhance the model's generalization capability and improve pattern accuracy. The proposed method proves universally applicable for automatic generation of diverse garment patterns, with promising potential for implementation in parametric pattern library development.

Real-time detection model for clothing keypoints based on deep learning

FENG Cailing, YU Shijia, HAN Shuguang

Journal of Textile Research. 2026, 47(1):  196-206.  doi:10.13475/j.fzxb.20250500501

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Objective The objective of this study is to develop a deep-learning-based real-time detection model for clothing keypoints, aiming to address the challenge of balancing accuracy and real-time performance in complex scenarios. The research focuses on enhancing the robustness and precision of keypoint detection across various clothing types. This is essential for advancing applications in intelligent clothing manufacturing and virtual fitting. Existing models struggle with occlusions, diverse clothing styles, varying keypoint sizes, and real-time performance. This work aims to overcome these limitations while maintaining high computational efficiency.

Method This study proposes the Real-Time Fashion Pose Estimation (RTFPose) model for real-time clothing keypoint detection, based on the RTMPose architecture. RTFPose includes the Median Enhanced Channel and Spatial Attention Module (MECS) to enhance key area features and reduce noise for better occlusion detection. The Cross-Scale Feature Fusion Module (CSFF) integrates multi-scale features to handle varying keypoint sizes. The Self-Attention Feature Enhancement Module (SAFE) focuses on keypoint regions to suppress background interference. Additionally, a finetuning strategy addresses data imbalance.

Results The RTFPose model demonstrated excellent performance on the DeepFashion2 dataset, which achieved a high speed of 140 frames/s with an Area Under the Curve (AUC) value of 65.1%, a significant 6.5% improvement in accuracy compared to the baseline model RTMPose. Additionally, on the DeepFashion dataset, the model achieved the percentage of correct keypoints (P) of 68.0% at a real-time speed of 142.3 frames/s. These results further demonstrate the model's strong performance in keypoint detection accuracy while maintaining high efficiency and validate its good generalization capability across different datasets. The model shows that the introduction of the MECS separately improves the accuracy to 59.5%. Through the channel-spatial attention mechanism, it effectively improves the feature visibility of clothing keypoints in occluded scenes. After stacking the CSFF, the accuracy further increased to 62.0%. This module integrates multi-level features of the backbone network and solves the problem of keypoint size variability by fusing high and low-resolution details and semantic information. After further introducing the SAFE, the performance reached 63.8%. The self-attention mechanism adaptively focused on keypoint areas, reduced background texture interference (such as clothing folds and decorative patterns), and improved feature purity. The final overlay classification fine-tuning strategy achieved a model accuracy of 65.1%. The fine-tuning was achieved by independently training six types of clothing, balancing the accuracy of keypoint detection for each type of clothing. These results highlight the effectiveness of the proposed modules and the fine-tuning strategy in enhancing the robustness and accuracy of the RTFPose model in complex scenarios. The model's ability to maintain high efficiency while improving detection accuracy makes it a valuable solution for real-time clothing key-point detection in various industrial applications.

Conclusion In conclusion, the proposed model effectively balances real-time performance and detection accuracy for clothing keypoint detection. By integrating MECS, CSFF, and SAFE, the model significantly enhances its robustness and accuracy in complex scenarios. Additionally, the fine-tuning strategy effectively addresses data imbalance, improving detection performance across different clothing types. The lightweight design and high efficiency of the proposed model make it particularly valuable for industrial applications such as smart clothing manufacturing and virtual fitting. Future work will focus on three main directions: firstly enhancing the system's adaptability to dynamic scenes to improve robustness and real-time processing capabilities in dynamic environments; secondly, utilizing multimodal data fusion technology to integrate depth information and texture features, thereby improving recognition accuracy; thirdly, adopting a self-supervised learning paradigm to reduce dependence on manual annotation and enhance the model's generalization performance. These advancements will further strengthen the applicability and effectiveness of the proposed model in various industrial settings.

Influences of soft finger compression and speed on long straight-line sewing deviations

WANG Jianping, CHU Linping, SHEN Jinzhu, ZHANG Fan

Journal of Textile Research. 2026, 47(1):  207-213.  doi:10.13475/j.fzxb.20250804101

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Objective In order to explore the influence of fingers compression force and sewing speed on the sewing deviations during long straight-line sewing process, the collaborative control of the robot and the sewing machine during this process was studied. This contributes to improving the theoretical research on long straight-line intelligent sewing, expanding the convenitional quilting sewing theory and production methods, and would provide certain theoretical references for the research on unmanned sewing methods for clothing.

Method This study focused on nine-square grid quilting, using three common pure cotton fabrics. The long straight-line sewing path was divided into three segments with three repeated sewing. Two soft fingers served as the UR5 arm's end-effector, with force sensors monitoring their normal contact force and feeding speed during sewing. L₂₅ (5²) orthogonal experiments were carried out to investigate the influences of force and speed combinations applied by soft fingers on the sewing deviations. A support vector regression model optimized by grid search (GS), Bayesian optimization (BO), and particle swarm optimization (PSO) was established to predict the sewing deviations.

Results When the sewing speed was set to the gear 2, the optimal combination of force and speed for the soft fingers of the fabric 1^# in the three sewing segments is (24 N, 9.5 mm/s), (24 N, 10.5 mm/s), and (24 N, 10.5 mm/s); for fabric 2^#, the optimal combination is (28 N, 8 mm/s), (22 N, 10.5 mm/s), and (26 N, 10 mm/s); and for fabric 3^#, the optimal combination is (24 N, 9.5 mm/s), (22 N, 10 mm/s), and (22 N, 10 mm/s). For the first sewing segment of fabric 1^#, the influence of the soft finger speed on the sewing deviation is the greatest, followed by the interaction effect. For the second and third sewing segments of fabric 1^#, the soft finger force, speed, and their interaction all have an impact on the sewing deviation. Among them, the interaction has the strongest impact on the second segment, while soft finger speed dominates the third segment. Before optimization, the support vector regression (SVR) model only had a small portion of the predicted values close to the actual values on both the training set and the test set. When the actual values were large, the corresponding predicted value distribution was relatively scattered, deviating significantly from the ideal line. After optimization using GS, BO, and PSO, the performance of the SVR model on the training set and the test set was significantly better than that of the unoptimized model. Among them, the model obtained by optimizing with GS had the best generalization ability and optimization efficiency (root-mean-square error of 0.039 7 mm, optimization time of 26.52 s), and most of the predicted values were concentrated near the ideal line, meaning they were close to the actual values.

Conclusion At the same sewing speed, the optimal combinations of soft finger compression force and sewing speed differ among the three-cycle sewing segments for the same fabric, and also vary across different fabrics for the same sewing segment. For the same fabric, the influence of soft finger force, speed, and their interaction on sewing deviations vary with sewing speed and cyclic sewing segment, with significant differences observed between different fabrics. Therefore, the optimal combinations of robotic arm parameters for different sewing segments of various fabrics under different sewing speeds should be dynamically matched according to the specific fabric, sewing segment, and sewing speed. This approach can effectively reduce sewing deviations and improve sewing accuracy. The optimized support vector regression models outperform the unoptimized ones significantly in both training and test sets. Among them, the model optimized by GS exhibits the best generalization ability and optimization efficiency. Thus, the support vector regression model optimized by GS can relatively accurately and efficiently predict the absolute value of sewing deviation for different sewing segments and robotic arm parameter combinations under varying sewing speeds.

Machinery & Equipment

Mechanism construction and implementation of single spindle automatic splicing in automatic rotor spinning machines

LI Jinjian, XUE Yuan, CHEN Yourong, CHEN Guofang, TIAN Feifei, LI Jinzhong

Journal of Textile Research. 2026, 47(1):  214-222.  doi:10.13475/j.fzxb.20250601901

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Objective With the continuous increase of spindle position of the rotor spinning machine, when there is an occasional end breakage in a certain spindle position during the spinning process or when the full roll is automatically changed, a single spindle single-control manipulator splicing technology gradually replaces the splicing mode of the splicing trolley. This technology achieves automatic single spindle splicing through programmable logic controller (PLC) dual-level control. However, due to the unclear mechanisms underlying each splicing process step, it cannot flexibly adjust automatic splicing parameters based on varying process parameters, spinning component structures, or spinning materials. For this reason, this paper discusses in depth the timing control method of single spindle automatic starting and splicing process after yarn breakage, and introduces the development of a continuous spinning technology that can achieve high quality and high-speed splicing.

Method The function of single spindle automatic splicing were firstly analyzed, and an integrated control system based on PLC multi-level control technology was constructed to realize the function of single spindle automatic splicing. Then, a mathematical model of single spindle automatic starting and splicing was constructed by analyzing the function of single spindle automatic splicing and its mechanism, so as to control the reciprocating motion of splicing robot arm and the morphological structure of the splicing between the seed yarn and the fiber flow in the rotor cup. On this basis, 30 groups of experiments were designed and the results were compared and analyzed by a microscope and image processing (MatLab2023b).

Results The success rate of the splicing was 100% for the 30 sets of experimental splices carried out by manually interrupting the yarns, of which 10 sets were used for MatLab image processing and strip dryer to test the yarn diameter, and the other 20 sets were adopted to test the breaking strength of single yarns. The results were analyzed by comparing the parameters of the 30 tubes of yarns tested. The 10 groups of images processed yarns effectively removed the noise, hair feathers and other interference in the image, and obtained a clear yarn trunk, which laid the foundation for the accurate calculation of yarn diameter, and the average diameter of the splice was 0.578 mm, which was 1.778 times of the normal yarn diameter. In 20 groups of breaking strength tests, the average strength at the splice was 10.76 cN/tex, which was 88.1% of the average strength of 12.21 cN/tex of the designed normal yarn, all of which conformed to the quality index for splice of related industry standards.

Conclusion This paper is based on the automatic rotor spinning platform which can realize the function of single spindle automatic splicing, through the analysis of single spindle automatic splicing and splicing mechanism, constructed the corresponding mathematical model to guide the program to control the timing movement of the relevant mechanical structure, solved the problem of how to splice the yarn quickly after the yarn breakage of individual spindles due to the occasional breakage or full roll change in the process of rotor spinning, and verified the accuracy of the model through the relevant experiments. The digital splice technology with flexible adjustment of splice process parameters is realized. Although the final experimental results meet the relevant industry standards, the CV value of the yarn splice section is not satisfactory enough, which is a direction that needs to be further investigated in the future.

Online microwave detection method for combed sliver quality based on resonant cavity perturbation theory

LIU Rongfang, LI Xinrong, LI Li, YUAN Chengxu

Journal of Textile Research. 2026, 47(1):  223-230.  doi:10.13475/j.fzxb.20250603901

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Objective The evenness of combed sliver is an important indicator of combing quality, and its precise detection is an important foundation for achieving process optimization. Conventional detection methods mainly rely on manual sampling and offline analysis, which have problems such as serious detection lag. With the development of intelligent manufacturing technology, real-time dynamic detection and data analysis can be achieved through online detection, which provide real-time data support for process control. Therefore, conducting research on online detection technology for the uniformity of combed slivers is an important development direction for improving the intelligence level of combing machines.

Method A relationship model between sliver density and resonant cavity frequency was established based on the theory of microwave resonant cavity perturbation, before determining the key parameters of the resonant cavity of the microwave sensor and designing the appearance of the sensor. Subsequently, the sampling frequency was calculated and determined, and the theoretical correctness of the online detection system for combed slivers was preliminarily verified by simulating the changes in the dielectric constant inside the resonant cavity and comparing the theoretical values with the simulation values. An online testing platform for on-site testing was eventually established.

Results The simulation results showed that, at a resonant frequency of 10.4 GHz, the deviation between the simulated density and the theoretical density for different dielectric-constant inputs remained below 3%, confirming the accuracy of the proposed theoretical model. In order to further validate the method under practical production conditions, on-site experiments were conducted on a running combing machine. Data were repeatedly collected at 10.438, 10.440, 10.442 GHz and at combing speeds of 300, 350, 400 nips/min. Each data-collection run lasted at least 2 h, and an additional 24 h long-term stability test was performed to evaluate the robustness of the sensing system. The experimental results showed that the sliver CV values measured by the microwave sensor at different frequencies were within 3% of the corresponding values obtained using the Uster evenness tester. In addition, the density-variation trend derived from the microwave sensor closely matched the actual fluctuation pattern of the sliver during operation. This consistency indicates that the proposed system can accurately capture real-time sliver-density changes and provides reliable performance across different speeds and operating conditions. Overall, both simulation and experimental results demonstrate the feasibility and effectiveness of the microwave sensing system for online detection of sliver unevenness.

Conclusion A comprehensive model linking resonant frequency, output voltage, and sliver density was established based on resonant-cavity perturbation theory, forming a solid theoretical basis for the development of microwave-based sliver detection technologies. Both simulation analysis and on-site experiments demonstrated that the proposed system can achieve accurate real-time monitoring of sliver unevenness without altering the combing process or affecting machine operation, with measurement errors consistently maintained within 3%. The microwave sensor adopts a non-contact design that minimizes vibration-induced interference, making it well-suited for installation in high-speed textile machinery. In addition, the system features simple operation and modest hardware requirements for the data platform, thereby reducing overall implementation cost and facilitating large-scale industrial deployment.

Development of virtual simulation software for three-dimensional braiding machines

FU Ruiyun, WU Yinzhou

Journal of Textile Research. 2026, 47(1):  231-239.  doi:10.13475/j.fzxb.20250504401

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Objective In view of the problems existing in the current three-dimensional braiding virtual simulation technology, such as the asynchrony between reverse modeling and the actual manufacturing process, the difficulty of conventional yarn models adapting to multi-layer and multi-yarn braiding, and the disconnection between algorithms and electromechanical systems, a yarn finite element processing method based on the dual states of winding and unwinding is proposed to achieve real-time synchronization of the braiding process and the simulation model, and then a mechatronic simulation system suitable for hexagonal and rotary braiding machines is developed, so as to enhance the universality and practicability of virtual braiding software in engineering applications.

Method The principles of hexagonal and rotary braiding were analyzed. Based on the commonalities of these two braiding methods, a mathematical model of the braiding machine base was established using the Cartesian coordinate system. The motion matrix of the yarn carrier on the base was derived, thereby obtaining the motion data of the yarn. Then, the braiding process was transformed into the position change information of the yarn carriers, and a pressing matrix was constructed to provide a basis for the interlacing judgment of the yarn on the mandrel. Based on the above analysis, the shape of the yarn was accurately expressed using NURBS curves. A yarn finite element processing method based on winding and unwinding states was proposed for the generation of yarn on the mandrel, enabling the three-dimensional dynamic generation of yarn. Taking a real braiding machine composed of 32 angle wheels and 32 dials as the development object, a virtual three-dimensional braiding software suitable for both braiding processes was developed using the Qt Toolkit. Finally, the three-dimensional theoretical models of 10 sets of braided materials produced by the prototype machine were compared with the actual products using industrial Computed Tomography equipment.

Results The yarn generated using NURBS curves can change the local curvature of the yarn with a small number of control points, meeting the multi-layered development requirements of the fabric. The proposed winding and unwinding yarn finite element generation methods can simply and quickly achieve the three-dimensional dynamic generation of yarn. The comparison of 10 sets of three-dimensional theoretical models of braided materials with the actual products showed a high degree of consistency in yarn pressing, with an average error of only 3%-6% in diameter and length.

Conclusion The accuracy of the software algorithm was verified through practical examples, achieving the application of one machine in hexagonal and rotary braiding. The software can also be integrated with other conventional industrial software for secondary development, enabling it to be endowed with material properties and pre-assessment performance, thereby realizing the integrated electromechanical simulation design of three-dimensional braiding.

Comprehensive Review

Research applications and prospect of carbon fiber nonwovens

WANG Shihao, XU Xiaoyu, ZHENG Ting, WANG Jinxing, YAO Degang, WANG Jun, YE Xiangyu, TIAN Hui, LI Ting, ZHU Feichao

Journal of Textile Research. 2026, 47(1):  240-249.  doi:10.13475/j.fzxb.20250304802

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Significance Carbon fiber nonwoven materials are fabricated from carbon fibers or their precursors (e.g., polyacrylonitrile (PAN) and pitch) by various nonwoven forming technologies. These materials integrate the inherent properties of carbon fibers such as wear resistance, ablation resistance, and electrical conductivity with the advantages of nonwoven manufacturing, including high production efficiency and flexible regulation of product structure and functionality. Consequently, they exhibit broad application prospects across multiple fields. For instance, needle-punched carbon fiber nonwovens are widely used in brake discs, rocket nozzles, and nose cones, benefiting from their excellent wear and ablation resistance. Carbon fiber membranes prepared by centrifugal spinning and electrospinning hold great potential in electrode materials and electromagnetic shielding applications. Meltblown carbon fiber nonwovens possess both filtration and adsorption capabilities, while spunlaced carbon fiber nonwovens are suitable for thin insulation materials in high-speed railways.

Progress This paper comprehensively reviews the research progress of carbon fiber nonwoven materials, starting from their preparation by different technologies. Based on the technical and product characteristics of various carbon fiber nonwovens, a detailed analysis is conducted from the perspectives of raw materials, preparation processes, equipment, and application fields. Needle-punched carbon fiber nonwovens are characterized by high production efficiency and adjustable product shapes, thus ideal for high-demand composites requiring superior mechanical resistance (e.g., ablation-resistant and wear-resistant materials). Electrospinning, which enables the preparation of nanoscale carbon fibers, has attracted extensive attention and research from scholars. In the study of wet-laid carbon fiber nonwovens, researchers have continuously proposed innovative solutions to address the key challenge of uniform dispersion of carbon staple fibers. Meanwhile, equipment for meltblown and centrifugal-spun carbon fiber nonwovens is constantly upgraded to achieve stable and consistent production. Regarding applications, this paper focuses on the utilization of carbon fiber nonwovens in high-resistance materials, electromagnetic shielding materials, adsorption/filtration materials, and energy storage systems.

Conclusion and Prospect Advancements in nonwoven and carbon fiber technologies have laid a solid foundation for the development of carbon fiber nonwoven materials, enabling their high performance and multifunctionality and thus ensuring excellent performance across diverse fields. This paper prospects the future development trends of carbon fiber nonwoven materials as follows: 1) ultra-refinement: ultra-fine carbon fibers offer large specific surface area and high entanglement density, which can enhance the versatility of carbon fiber nonwovens and expand their applications in electrode materials, battery separators, adsorption/filtration materials, and electromagnetic shielding materials. 2) Green engineering: currently, research on Lyocell-based carbon fiber nonwovens is limited, with a focus on adsorption capacity. Future efforts should address the low carbon yield and poor performance of Lyocell-based carbon fibers to realize the green production of carbon fiber nonwovens. 3) Energy saving: the carbonization process of pitch-based and PAN-based felts consumes substantial energy and may suffer from uneven carbonization. Therefore, it is necessary to upgrade pre-oxidation and carbonization methods and equipment tailored to carbon fiber nonwovens to achieve energy efficiency. 4) Recycling: recycled carbon fiber staple fibers, obtained by crushing discarded carbon fiber filament products, can be used for the preparation of needle-punched and wet-laid carbon fiber nonwovens, promoting resource reuse.

Technological innovations and research progress in electroluminescent fibers

ZHANG Ningou, WANG Hailong, HU Xingyou, SUN Bin, YOU Chaoyu

Journal of Textile Research. 2026, 47(1):  250-258.  doi:10.13475/j.fzxb.20250601302

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Significance Electroluminescent (EL) fibers represent a critical technology for advancing next-generation wearable electronics, intelligent textiles, and human-machine interfaces. Their unique ability to combine luminescent functionality with inherent fiber flexibility and integrability addresses the pressing need for ultra-thin, highly flexible, and multifunctional platforms. The successful development and application of EL fibers hold significant potential to revolutionize diverse fields. Emerging applications already demonstrate their value in biomedicine (e.g., enabling flexible phototherapy bandages), energy (e.g., contributing to self-powered luminescent textiles), and defense (e.g., facilitating adaptive camouflage systems). Furthermore, the deep integration of EL fibers with sensing and energy harvesting modules is pivotal for creating novel intelligent interactive paradigms.

Progress Research progress in EL fibers is extensively analyzed, focusing on key aspects of material design, structural engineering, and spinning techniques. Significant efforts were dedicated to developing suitable luminescent materials, electrode configurations, and dielectric layers compatible with fiber geometries and processing. Various fabrication strategies, including coating, co-extrusion, and novel spinning methods, were explored to construct functional EL fiber architectures. This work has established a foundation for understanding the core mechanisms and achievable performance metrics of different EL fiber types. Representative achievements include the demonstration of flexible and fiber-based light-emitting devices through weaving and knitting.

Conclusion and Prospect Despite promising advancements, EL fibers face substantial challenges that hinder their widespread adoption and commercialization. Key limitations include insufficient environmental stability (leading to compromised operational lifetime), limited mechanical durability under repeated stress or strain, a narrowly achievable color gamut, and significant scalability barriers in mass production. Addressing these challenges requires synergistic advancements across materials, fabrication processes, and device architectures. Future research must prioritize enhancing stability and durability, expanding color emission capabilities, and developing cost-effective, high-throughput manufacturing processes. The convergence of EL fibers with complementary technologies like sensors and integrated energy systems remains a crucial direction, promising to unlock their full potential for transformative applications in smart textiles and interactive devices. Overcoming the existing bottlenecks is essential to transition EL fibers robustly from laboratory prototypes to industrial-scale production and real-world implementation.

Research status and development trends of fiber-based colorimetric gas sensors

WANG Jiaxi, YUAN Guoshu, CHEN Xiaoyu, CHEN Shuangting, MIAO Ying, FU Chiyu, TANG Wenyang

Journal of Textile Research. 2026, 47(1):  259-267.  doi:10.13475/j.fzxb.20250306802

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Significance Fiber-based colorimetric gas sensor is a new sensing technology that enables visual detection of gas pollutants based on color changes, which has the advantages of portability, high sensitivity and low cost. As air pollution becomes a growing problem, conventional gas detection methods rely on expensive equipment and specialized personnel for real-time monitoring. This paper systematically review the sensing principles, response materials, and preparation methods (such as impregnation method, electrospinning method, etc.) of fiber-based colorimetric sensors and their applications in the detection of chemical warfare agents, volatile organic compounds (VOCs), hydrogen sulfide (H₂S) and ammonia gas (NH₃), together with the challenges and future development directions of fiber-based colorimetric sensors. This research provides an important theoretical basis and technical reference for the development of high-performance and wearable gas sensors, and is of great significance for promoting the development of environmental safety monitoring, industrial protection and smart wearable devices.

Progress This paper systematically reviews the latest progress and representative achievements of fiber-based colorimetric gas sensors. In terms of materials, a variety of response systems based on dyes (e.g., chlorophenol red, bromocresol violet), noble metals (silver, gold nanoparticles) and fluorescent probes have been developed, which significantly improve the sensitivity and selectivity of the sensor. In terms of preparation process, electrospinning, wet spinning, freeze-drying and novel spinning technologies have been innovatively used for constructing fiber-based sensors with high specific surface area. In terms of applications, detection of toxic gases such as chemical warfare agents, NH₃, H₂S and have been achieved, and the potential of wearable applications was demonstrated through patterned textile integration. Representative achievements include LiCl/cellulose-based fibers for NH₃ sensing, ionic liquid/lead acetate nanofiber yarns for H₂S monitoring, and so on. These advances provide an important foundation for the development of portable and intelligent fiber-based gas sensing devices.

Conclusion and Prospect This study systematically reviews the latest research progress of fiber-based colorimetric gas sensors, focusing on their application potential in the fields of environmental monitoring and wearable devices. The results show that fiber-based colorimetric gas sensors show excellent performance in the detection of harmful gases such as NH₃ and H₂S. However, there are still three major problems in current research: the lack of environmental stability of colorimetric sensing materials, the immaturity of mass production processes, and the susceptibility to cross-interference when multifunctional integration. Based on the existing results, this paper puts forward three important points: fiber-based sensors have the potential to be served as portable gas-monitoring devices, material and method innovation is the key to breaking through performance bottlenecks, and intelligence is the future development direction. For instance, a proposed strategy can be conducted by strategically integrating heterogeneous responsive materials and engineering multi-analyte recognition units. These innovative platforms enable simultaneous monitoring of gaseous species, ionic compounds, and biomolecules within complex environmental and biological matrices. Also, incorporating advanced flexible electronics, microfluidic networks, and wireless communication modules can significantly enhance the functional capabilities of fiber-based colorimetric sensors. In terms of application, fiber-based colorimetric sensors with customizable designs hold significant potential for addressing interindividual variability in physiological monitoring. By controlling sensor composition and response properties, these wearable platforms enable real-time, continuous tracking of critical biomarkers, including disease-related analytes, therapeutic drug levels, and nutritional metabolites, which will provide more accurate diagnostic criteria and treatment effect evaluation methods for personalized medicine and facilitate the development of precision medicine.

Research progress in e-textiles based on machine learning model

HU Weilin, BAI Jie, LIU Dan, BAI Meng, LI Juan, LI Qizheng

Journal of Textile Research. 2026, 47(1):  268-276.  doi:10.13475/j.fzxb.20250605302

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Significance E-textiles, which integrate sensing, communication, and interactive capabilities into fabrics through embedded flexible electronics, are heralding a transformative shift in the textile industry. These smart textiles have the potential to revolutionize various domains, including human-computer interaction, motion analysis, and health monitoring, by offering versatile applications that go beyond conventional garment functionality. However, the widespread adoption of e-textiles is currently hindered by several challenges. Key barriers include the complexity of adapting machine learning (ML) models to meet the demanding computational constraints of embedded devices, the difficulty of generalizing models across a diverse range of users, and the high costs associated with optimizing both materials and production processes. Despite the significant potential of e-textiles, existing research has yet to systematically explore the inherent relationships between ML models and the various e-textile structures and applications. The matching of specific ML models to the unique characteristics of e-textile structures and their intended use cases remains an unresolved challenge. By addressing this gap, the review seeks to offer valuable insights that will inform the development of intelligent, efficient, and deployable textile systems, facilitating their transition from lab prototypes to practical, real-world applications.

Progress The review examines the evolving role of machine learning in e-textiles along three key dimensions: past developments, current trends, and future directions. Historically, the application of ML models in e-textiles has been shaped by the interplay between model characteristics, the structural properties of e-textiles, and the requirements of different application scenarios. The review discusses how specific ML models have been adapted to address various needs, including human-computer interaction, motion analysis, and health monitoring. For each of these applications, the strengths and weaknesses of the algorithms are critically assessed, providing a comprehensive understanding of their effectiveness in real-world contexts. Additionally, the review highlights a series of documented cases and statistically analyzes the correlations between e-textile structures, the best-performing ML models, and application-specific requirements. These relationships are visually represented through Sankey diagrams, illustrating how the structural characteristics of e-textiles influence the selection of appropriate ML models for different application contexts. The review also identifies key trends currently shaping the field, including the integration of ML in signal processing, process optimization, and scenario-based implementations. By analyzing the temporal evolution of relevant literature, the review offers a snapshot of ongoing advancements and the trajectory of these developments.

Conclusion and Prospect The review concludes that the successful integration of ML models in e-textiles is largely dependent on the specific use case, data characteristics, and the inherent structure of the textile system. ML models like CNNs, LSTMs, and SVMs have demonstrated significant progress in enhancing performance across key application areas, including human-computer interaction, motion analysis, and health monitoring. CNNs, for example, excel in handling spatially rich data, such as pressure distribution images, making them ideal for applications like gesture recognition and handwriting analysis. LSTMs are particularly effective at modeling temporal dependencies, which is crucial for applications involving continuous motion signals. SVMs, on the other hand, offer efficiency and robustness, particularly in scenarios with limited data and well-defined features, making them a popular choice for health monitoring applications. The field is experiencing three major shifts: the movement from simple classification tasks to the decoupling of multi-modal signals in signal processing; the transition from experience-based trial-and-error approaches to model-driven optimization in manufacturing; and the evolution from generalized monitoring to personalized, precision services in application contexts. Despite these advancements, challenges remain, such as the need for lightweight models that can operate within the computational constraints of embedded devices, the difficulty of ensuring model generalization across diverse user populations, and the high cost of optimizing performance. Future research should focus on developing more efficient, lightweight models, enhancing model generalization with diverse and large datasets, and incorporating cost considerations into the design and optimization of ML-driven e-textiles. By addressing these challenges, the review lays a foundational framework for matching ML models with e-textile applications, which will be crucial in driving the intelligent development and industrial scalability of this emerging technology.