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 this paper, glass fiber fabric reinforced polyphenylene sulfide composites were prepared by hot pressing. The effects of glass fibre modification and hot pressing temperature on the properties of the composites were investigated using a scanning electron microscope, infrared spectrometer, universal testing machine, and DIGEYE digital imaging colour measurement system. The results show that after the treatment with a silane coupling agent, the silane coupling agent was more uniformly distributed on the surface of the glass fibres, and the bonding effect between the glass fibre fabric and polyphenylene sulphide was significantly improved. The strength of the composites increased and then decreased with the increase of hot pressing temperature, and the surface colour of the composites became darker and darker. When the hot-pressing temperature is 310 °C, the mechanical properties of glass fabric-reinforced polyphenylene sulfide composites are at their best, the tensile strength reaches 51.9 MPa, and the bending strength reaches 78 MPa.
Herein, a MnFe2O4/RGO knitted fabric derived from manganese waste was constructed by a simple in-situ assembled coating method, involving the incorporation of Graphene Oxide (GO) and manganese ferrite nanoparticles on polyester fabric, followed reducing by hydrazine hydrate. The reuse of manganese waste from the preparation of GO can reduce chemical waste emissions and endow the absorption performance. The coated particles possess certain magnetism can be attracted and securely collected in seconds, which is convenient for recycling. This fabric gives well microwave absorption with the maximum reflection loss (RL) of −58.6 dB at 9.1 GHz by a thickness of 1.9 mm. In addition, this fabric presents high stable strain sensing under 1000 stretching and bending cycles. Meanwhile, the resistance-deformation-velocity relationship is provided based on the structure, for the analysis of electromechanical behaviors. Moreover, the fabric has the capability for temperature sensing (TCR=−0.738%/°C), and fire alarm. As such, this fabric can be promising alternatives for a wide application on motion and temperature sensing, microwave blocking.
In this paper, according to the one-dimensional heat transfer mechanism between fabric and human body, it is found that different thermal properties affect different heat transfer stages of fabric. Therefore, we used the maximum heat flux qmax as the index to characterize the transient contact cool feeling of fabrics, and measured the thermal properties, various specifications and surface morphology of 40 kinds of summer fabrics. Firstly, we discussed the influence of the above properties on the transient cool feeling. Secondly, according to multivariate stepwise regression, the significant representative variables are selected, and the prediction model of transient coolness and fabric properties is established. Furthermore, the model was verified to explore the subjective and objective consistency. The results show that, in the transient heat transfer stage, the influencing factors that are significantly related to the cool feeling of fabric include fabric thickness, grammage, volumetric heat capacity, thermal conductivity, warp and weft density, and roughness. The main component representative variables of the cooling sensation regression equation are volumetric heat capacity and thickness, and other variables can be explained by these two variables. Changing them is the key to enhance the cooling sensation. The predicted value of coolness is in good agreement with the subjective evaluation of cooling sensation, which has a certain guiding effect on the actual human cool feeling. The purpose of this study is to find out the main factors that affect the cool feeling, and then apply the established cool feeling model to the development of fabrics in summer, so as to meet the thermal comfort requirements of human body’s fabrics.
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