Graphene-based textile strain sensors were reviewed in terms of their preparation methods, performance, and applications with particular attention on its forming method, the key properties (sensitivity, stability, sensing range and response time), and comparisons. Staple fiber strain sensors, staple and filament strain sensors, nonwoven fabric strain sensors, woven fabric strain sensors and knitted fabric strain sensors were summarized, respectively. (i) In general, graphene-based textile strain sensors can be obtained in two ways. One method is to prepare conductive textiles through spinning and weaving techniques, and the graphene worked as conductive filler. The other method is to deposit graphene-based materials on the surface of textiles, the graphene served as conductive coatings and colorants. (ii) The gauge factor (GF) value of sensor refers to its mechanical and electromechanical properties, which are the key evaluation indicators. We found the absolute value of GF of graphene-based textile strain sensor could be roughly divided into two trends according to its structural changes. Firstly, in the recoverable deformation stage, GF usually decreased with the increase of strain. Secondly, in the unrecoverable deformation stage, GF usually increased with the increase of strain. (iii) The main challenge of graphene-based textile strain sensors was that their application capacity received limited studies. Most of current studies only discussed washability, seldomly involving the impact of other environmental factors, including friction, PH, etc. Based on these developments, this work was done to provide some merit to references and guidelines for the progress of future research on flexible and wearable electronics.

The use of synthetic fibers in our daily life is growing continuously; however, the excessive dependence of these chemical fibers on petroleum-based chemicals will lead to large consumption of non-renewable resources. The scarcity of oil resources, economic and environmental problems, reliance on a few oil-rich countries, and predicted depletion of these resources. Therefore, research and development of biobased materials to reduce the use of fossil fuels have become increasingly important. Biobased synthetic fiber has a low carbon footprint in the synthesis process because its raw materials are derived from biomass. In addition, most biobased synthetic fibers have excellent biodegradability, which can be composted and degraded in natural environments or by microorganisms with or without specific conditions. However, all biobased fibers cannot be proven to be biodegradable, so the development of biodegradability is an important driving force for the progress of research on biobased fibers. In the past, biobased fiber was obtained, extracted, or synthesized from food crops, which was soon replaced by non-food crops. With environmental protection, sustainability, and resource conservation, it has become necessary to make non-food crops and food residues biobased raw materials to obtain biobased textile fibers and even to develop ideal biobased raw materials that are carbon negatives, such as moss and CO₂. Besides, there is huge potential for these biobased textile fibers to be used for sustainable clothing and medical textiles due to their non-toxicity, skin friendliness, and antibacterial properties. This review paper introduces biobased synthetic textile fibers, summarizes the recent development, and clarifies key concepts in this domain.

The curing kinetics can influence the final macroscopic properties, particularly the three-point bending of the fiber-reinforced composite materials. In this research, the curing kinetics of commercially available glass fiber/epoxy resin prepregs were studied by non-isothermal differential scanning calorimetry (DSC). The curing kinetic parameters were obtained by fitting and the apparent activation energy E_a of the prepreg, the pre-exponent factor, and the reaction order value obtained. A phenomenological nth-order curing reaction kinetic model was established according to Kissinger equation and Crane equation. Furthermore, the optimal curing temperature of the prepregs was obtained by the T-β extrapolation method. A vacuum hot pressing technique was applied to prepare composite laminates. The pre-curing, curing, and post-curing temperatures were 116, 130, and 153 °C respectively. In addition, three-point bending was used to test the specimens’ fracture behavior, and the surface morphology was analyzed. The results show that the differences in the mechanical properties of the samples are relatively small, indicating that the process settings are reasonable.

Synthetic fibers account for about half of all fiber usage, with applications in every textile field. The phenomenon of fiber composition not matching the label harms consumer interests and impedes market development. Hyperspectral imaging technology as a potential technique can be utilized to discriminate mass synthetic fibers rapidly and nondestructively and achieves the functions that traditional Fourier transform infrared instruments do not have. On the basis of investigating the impact of dope-dyeing and wrapping processes on spectra, the spectral features (from 900 to 2500 nm) of six categories of synthetic fibers were extracted with a hyperspectral imaging system. A principal component analysis-linear discriminant analysis model was developed to discriminate the chemical content of fibers in different colors and structures, which showed 100% discrimination accuracy. Results demonstrated the feasibility of a hyperspectral imaging system in synthetic fiber discrimination. Therefore, this method offers a more convenient alternative for textile industry on-site discrimination.

In recent years, basalt-fiber-reinforced polymers (BFRPs) have been widely used in the field of corrosive aging resistance. In this paper, BFRPs are made into composite laminates, and the flexural properties of BFRPs modified with different types of silane coupling agents, KH550 (aminopropyl-triethoxysilane), KH560 (glycidyletheroxypropyl-trimethoxysilane), and A171 (vinyl-trimethoxysilane), immersed at 20 °C, 40 °C, and 60 °C in a 3.5% NaCl concentration artificial seawater, a 10% NaCl high-concentration artificial seawater, 10% H₂SO₄, or 10% NaOH are investigated. The results show that the flexural strength decreased with increasing exposure time in corrosive aging environments at different temperatures. The temperature greatly influences flexural strength, and the flexural strength decreases rapidly in high-temperature acidic and alkaline environments. In addition, we found that the flexural retention in the seawater environment did not change much compared to that in the water environment, indicating that BFRPs have relatively good resistance to seawater corrosion. The silane coupling agent modification enhances flexural strength and flexural strength retention by enhancing the interfacial bonding property of the BFRPs. Considering the experimental results, the three silane coupling agents modified the corrosive aging performance of the composites in the order of KH550 > KH560 > A171. This will provide theoretical support for the application of silane-coupling-agent-modified BFRPs in corrosive aging environments.

Color prediction and color management for digital printed fabrics remain a challenging task. Accurate prediction of color appearances of digital printed fabrics would enable designers and manufacturers to better fulfill their design requirements and creative visions. We propose a color prediction method for silk digital printing utilizing a Pix2Pix Generative Adversarial Network (GAN) framework. This method aims to generate predicted images that possess the same stylistic and color characteristics as their actual fabrics after production. To develop and validate the method, color data and images are collected and processed from 5252 sets of paired original Pantone TPX color card and actual print sample fabrics. The results of this study demonstrate that the method can predict the colors of silk digital print samples while effectively reproducing the effects of inkjet printing in silk fabrics including silk crepe satin and silk twill. The method exhibits high prediction accuracy to an average CIEDE2000 value of 2.372 for silk crepe satin and 1.846 for silk twill. The findings of this research not only enhance the efficiency and accuracy of color management in fabric digital printing technology but also contribute to the exploration and development of high-fidelity color prediction techniques within the textile industry.