Fibers and Polymers最新文献

Sanitary pads are vital for feminine hygiene, and there is growing interest in developing affordable and eco-friendly alternatives. Among various materials explored for this purpose, plantain fibers have received limited attention. This study aims to fill this research gap by investigating the use of plantain fibers extracted from waste pseudo-stems as absorbent cores for sanitary pads. The fibers underwent chemi-thermomechanical treatment, followed by bleaching with 5 wt% hydrogen peroxide. Comprehensive characterization using FT-IR spectroscopy revealed that the treated plantain fibers (PFAC) retained essential functional groups conducive to effective absorption. The absorbent cores were rigorously tested for absorption capacity, fluid retention, liquid strike-through, rewet, and absorption rate. The PFAC demonstrated an absorption capacity of 810% and a strike-through time of 7.8 s seconds, while the commercial sample (Always) showed an absorption capacity of 700% and a strike-through time of 2.8 s. Although the PFAC had slightly lower fluid retention (90.7%) compared to the commercial samples, it exhibited superior absorption characteristics. This study highlights the potential of utilizing waste plantain pseudo-stems as a sustainable and efficient resource for developing absorbent materials, offering a promising alternative for environmentally conscious and cost-effective sanitary pad production.

This study explores the removal of methyl orange (MO) dye from aqueous solutions using electrospun poly(ε-caprolactone)/polyethyleneimine (PCL/PEI) nanofibers, highlighting their potential as a reusable and efficient adsorbent. Process optimization was performed using Box–Behnken design (BBD), chosen for its efficiency in evaluating interactions among key parameters—pH, contact time, initial dye concentration, and adsorbent dosage—with minimal experimental runs. The statistical model identified pH and adsorbent dosage as the most influential factors. At optimal conditions (pH 3.8, 230 min, 35 ppm MO, 0.58 g/L adsorbent), the nanofibers achieved 98% removal efficiency. Adsorption behavior fit the Langmuir isotherm and pseudo-second-order kinetic model, with a maximum adsorption capacity of 357.14 mg/g, surpassing many comparable adsorbents. Thermodynamic analysis confirmed the process as spontaneous and exothermic. Furthermore, the nanofibers retained over 90% efficiency after four regeneration cycles, indicating promising reusability. These findings demonstrate the practical potential of PCL/PEI nanofibers for dye-contaminated wastewater treatment and offer mechanistic insights for further material development.

In this study, polypyrrole (PPy) coatings were fabricated on alginate nonwoven fabrics (ANFs) through electrochemical polymerization in a constant current mode, assisted by an indium tin oxide (ITO) conductive glass electrode. The effects of two anionic surfactants and two cationic surfactants on the polymerization process were compared. Among them, anionic DNS-86 exhibited the highest negative zeta potential, which significantly improved the dispersion of Py monomers and facilitated their uniform polymerization on ANFs. The mechanism of Py polymerization assisted by anionic surfactant was analyzed, revealing its dual roles as a surfactant and dopant. Characterization techniques including FTIR, XPS, and SEM confirmed the successful synthesis of PPy. The PPy coatings significantly increased the hydrophobicity of the ANFs while maintaining their flexibility. The resulting PPy-ANFs exhibited appreciable electrical conductivity and photothermal properties. This research would provide a new insight into the preparation of conductive polymers on green textile substrate for acquiring functionality.

In accordance with environmentally friendly dyeing standards and the trend toward low carbon emissions, sustainable nonaqueous dyeing is a promising technology for reducing the water and energy consumed in dyeing wool fibers. Under optimized conditions—a cylinder density of 240 g/m² and 150% (o.w.f.) water—the process achieved a dye fixation rate of 98.35%, exceeding that of conventional methods, while reducing water consumption by more than 60%, with the D5 medium being recyclable for at least 50 cycles. To further clarify the advantages of this approach, the dyeing mechanism and fiber structure were investigated. The results showed that the extraction capacity of the medium and the swelling action of water increased the number of dye channels, enabling smaller dye particles to penetrate the cortical layer and yield uniformly colored fibers. Compared with conventional dyeing, the nonaqueous method preserved the F layer and cuticle, provided more stable cross-linking, and increased the fiber strength, resulting in reduced damage (weight loss of 1.27% vs. 6.08%). Moreover, the excellent reusability of the medium enabled the dyeing of 300 g of fibers with comparable colorfastness, as the rubbing and soaping fastness reached ≥ Grade 4–5, indicating that this technique is approaching industrial application. Overall, less water use, a recyclable medium, reduced chemical demand, and lower energy consumption demonstrate that the nonaqueous dyeing system offers a promising pathway toward the sustainable and greener coloration of wool fibers.

Herein, a series of nanofibrous membranes, based on polyurethane (PU), was prepared by an electrospinning approach for dye removal application from water. PU was synthesized using polycaprolactone via the pre-polymerization method. Following this, graphene oxide (GO) nanosheets/PU nanocomposites were produced through the solution casting method. Their structural, thermal, and mechanical features were systematically analyzed. Water contact angle study showed that the prepared nanofibers have a hydrophobic nature, due to the hydrophobic PU and morphological effects, which could maintain the dimensional stability of the prepared membranes in aqueous media. UV/vis spectroscopy was utilized to study the adsorption ability of the nanofibers. Accordingly, methylene blue (MB) was selected as a model pollutant. The influence of various factors, including initial MB concentration, contact duration, temperature, adsorbent dosage, and solution pH, on the adsorption capacity of the nanofibers was investigated. According to thermodynamic and kinetic evaluations, the adsorption of MB onto the membranes is best described by the Langmuir isotherm and pseudo-second-order model, indicating that the process occurs via monolayer adsorption. The optimized GO/PU-5 nanofibers achieved a maximum adsorption capacity of 30.54 mg g⁻¹ and a dye removal efficiency of 98.85%, which was about three times higher than pristine PU. Moreover, thermodynamic evaluation confirmed spontaneous and endothermic adsorption (ΔG = −14.59 kJ mol⁻¹, ΔH = +5.90 kJ mol⁻¹) for these nanofibers. This study shows that the GO/PU electrospun nanofibers could be introduced as an efficient platform for dye removal from water.

Graphical Abstract

In this study, we present a coupled, dimensional energy-balance model enhanced with machine-learning validation to predict residual-velocity curves and ballistic limits of fiber-reinforced composites. Projectile deceleration is described as a three-term balance involving strength-like, drag-like, and inertial effects, mapped to the nondimensional groups Π₀, Π₁, and Π₂; closed-form and RK4 solutions yield residual velocity and regime boundaries (Π₀ = Π₁, Π₁ = Π₂). Validation against six literature datasets (CFRP and aramid laminates; V_r–V₀ curves) shows high accuracy: median R² = 0.93–0.96 and typical RMSE = 10–30 m·s⁻¹, with best case R² = 0.976 and RMSE = 6.99 m·s⁻¹ for thin CFRP. Ballistic-limit predictions accurately capture the nonlinear increase with thickness, with errors less than 1 m·s⁻¹ in brittle CFRP and up to 10 m·s⁻¹ in Kevlar laminates. A global master curve of w_r = V_r/V₀ versus ∥Π∥₂ collapses all data and shows a consistent trend. Energy-budget analysis quantifies the contributions of the three terms: the strength term Π₀ dominates in about 90% of operational points, while drag-like effects are minimal and inertial effects only appear at thick or high-velocity limits; the dominance fractions and combined contributions support these shifts. The (V₀, h) regime map, derived by setting Π₀ = Π₁ and Π₁ = Π₂, separates design-relevant domains and aligns with observed transitions in V_r–V₀ modes and slopes. An independent machine-learning check using Random Forests achieves R² = 0.992, RMSE = 17.5 m·s⁻¹, and MAE = 12.4 m·s⁻¹ (fivefold cross-validation: R² = 0.835 ± 0.145), supporting the mechanistic hierarchy through feature importance. The integrated physics-based model and machine-learning analysis provide traceable parameters (α, β, γ), uncertainty bounds, and practical screening maps for composite and geometric options under high-velocity impact.

Thermal-responsive shape-memory polymer fibers represent a promising candidate for controllable local drug delivery systems. In this study, we developed thermo-responsive shape-memory fibrous membranes through electrospinning and UV cross-linking, utilizing a blend of poly(p-dioxanone) (PPDO) and poly(ε-caprolactone) (PCL), which were loaded with doxorubicin (DOX). Scanning electron microscopy (SEM) characterization revealed the formation of three types of electrospun fibrous membranes with fiber diameters measuring 1.8 ± 0.4, 3.0 ± 0.7, and 3.7 ± 1.8 μm, designated as PDC-S, PDC-M, and PDC-L, respectively. The results indicated that the chains of PPDO and PCL are crosslinked by UV irradiation through benzophenone and triallyl isocyanurate. Specifically, PPDO functions as hard segments while PCL serves as switching segments. Further investigation demonstrated that the PDC-M membrane exhibited optimal performance with approximately 90% shape fixity ratio and around 82% recovery ratio alongside favorable mechanical properties. After programming, PDC-M displayed controllable drug release behavior capable of releasing DOX in a two-step manner under thermal stimulation. Consequently, they showed significantly enhanced antitumor efficacy along with improved drug-utilization efficiency. Therefore, the PDC membranes prepared through simple blending and UV crosslinking exhibit the desired shape-memory effect, demonstrating significant potential for local antitumor drug delivery applications.

Composite pressure vessels (CPVs) for hydrogen vehicles require irregular geometries with variable curvatures to maximize space utilization, presenting significant manufacturing challenges. Traditional filament winding excels in directional reinforcement but struggles with complex shapes, while conventional 3D braiding faces limitations in axial yarn tension and directional reinforcement at 0° and 90°. This study presents the first process simulator that integrates hybrid radial braiding and filament winding—a manufacturing approach that combines radial braiding's capability for three-dimensional complex preforms and filament winding's superior directional reinforcement. Unlike computationally intensive finite-element analysis-based simulations or single-process platforms, the developed kinematic simulator enables rapid prediction of critical manufacturing parameters, including yarn consumption, processing time, preform geometry, and cover factor through an intuitive graphical user interface. The simulator features real-time three-dimensional animation that visualizes the braiding process, facilitating understanding for users without specialized knowledge. Experimental validation across seven operating conditions demonstrated strong correlation between simulated and measured braid angles (r = 0.94, R² = 0.89) with a mean absolute error of 6.40°, confirming the simulator's reliability for design-stage manufacturing predictions.

The surface functionalization technologies include the incorporation of functional groups on textile substrates. These groups can serve as initiators in the grafting process or enhance the finishing and dyeing of textile materials. Our study focused on the crosslinking of sericin, a natural biopolymer, onto polyethylene terephthalate (PET) fabric to enhance its properties such as hydrophilicity through an environmentally friendly surface functionalization process. In fact, our previous research demonstrated that the hydrophilicity of PET fabric could be improved by grafting sericin using glutaraldehyde as a crosslinking agent; however, due to the toxic nature of glutaraldehyde, alternative eco-friendly approaches are needed. For this purpose, we proceeded to the grafting of a biopolymer on the surface of the pre-activated PET by an air atmospheric plasma. Citric acid (CA) was employed as an ecological crosslinking agent during the grafting process. A statistical study was conducted to determine the optimal parameters for PET surface functionalization. The parameters under investigation include surface state (Untreated and Plasma-treated), Time (min), and Temperature (°C) of crosslinking process. Therefore, we opted to implement a full factorial design with three factors and multiple levels. The evaluation of the substrates under consideration involved measuring wettability through water contact angle (WCA°) and capillarity (Cp%). The results show a decrease in the water contact angle to 46° and an increase in capillarity to 75% for the plasma-treated PET samples grafted with sericin using CA at 120 °C for 15 min, compared to 81° and 3% for the untreated samples. The homogeneity test was conducted using an aqueous solution containing methylene blue. Additionally, X-ray Photoelectron Spectroscopy (XPS) and scanning electron microscopy (SEM) analyses of the textile surface demonstrate the grafting of the biopolymer crosslinked by citric acid through the appearance of new peaks related to nitrogen atoms and carbon involved in a double bond with oxygen. The abundance of functional groups in the biopolymer, crosslinking agent, and especially the PET plasma pre-activated surface offers various bonding possibilities among the different components.

This study proposed a lightweight deep-learning approach to localize loop-formation defects in air-jet textured yarn (ATY) directly from grayscale surface-loop images. Inspection of ATY is challenging due to its elongated geometry, semi-transparent filaments, and defect morphologies that differ from conventionally spun yarns. To preserve longitudinal context, we designed a structure-preserving convolutional neural network (CNN) that processes full-length images without cropping. Ground-truth defects—defined as loop-formation failure and insufficient core-effect yarn entanglement—were manually annotated using computer vision annotation tool (CVAT). Pre-processing with binarization and aspect-ratio preservation reduces noise and computational cost, while loop-density weighting increases sensitivity to defect-prone regions. Instead of dense masks, the network performs one-dimensional boundary regression, outputting two horizontal coordinates (x₁, x₂) that delimit the defective span along the yarn axis, improving stability, and reducing complexity. Trained on 53 annotated images with data augmentation, the model was evaluated using mean absolute error (MAE), intersection over union (IoU), and expert visual inspection; MAE stabilized at approximately 130 epochs, and the mean IoU reached 0.41. Despite the limited dataset, targeted data refinement and the boundary-regression formulation produced accurate, interpretable localization at low computational cost. The method is suitable for ATY quality control and is potentially extensible to other filament-based yarns exhibiting similar defect morphologies.