Electrically conductive cotton fabrics were successfully prepared by a first free radical polymerization, assisted by a conventional pad-dry-cure textile technology and subsequent electroless deposition (ELD) of copper thin layers on cotton fiber surfaces. Pad-dry-cure takes a major role in the even uptake and penetration of the monomer [2-(methacryloyloxy)ethyl]trimethyl-ammonium chloride (METAC) solution throughout the cotton fabrics, making subsequent ELD plating of copper metal uniform. The surface electrical resistivity of as-prepared electro-conductive cotton fabrics varies from 10^3 to 10^{-1}Ω/sq, depending on the ELD conditions. The whole process is low cost, low chemical wastage and compatible with current wet processing in the textile industry, which provides a great potential in commercialization in a bulk scale.

Multi-functional fibrous materials are a new family of fiber materials whose physical and chemical properties are sensitive to the change in the environment such as temperature, pressure, electric field, magnetic field, optical wavelength, adsorbed gas molecules and the pH value. This paper introduces a new approach to translate functions from nanoparticles to advanced fibrous structures by co-electrospinning of composite nanofibers. To illustrate this composite nanofiber concept an example of nanofibers that have tailorable, electrical and magnetic functions is shown.

An analytical determination of electric voltage generated by the pressure-driven flow through complex microchannels was analyzed based on the fractal theory. The pressure-driven flow through a complex microchannel with consideration of electrokinetic phenomena is described by the momentum and Poisson- Boltzmann (P-B) equations. The solution of induced electric field strength and electric voltage across complex microchannels are obtained using the fractal theory and technique, which are the function of dimensionless electroosmotic radius and the porosity. The results obtained show that the analytical results are agreed well with the experimental data.

A heat and moisture transfer model for the clothed human body is developed based on the 3-D scanning data of the human body. The whole passive clothed human body system is subdivided into four sub- systems which are the tissue system, the circulatory system, the respiratory system and the clothing system. The physical process of the heat and moisture transfer in the each sub-system is described by energy and mass balance equations. The Finite Element Method (FEM) is used to solve the governing equations for each sub-system. For a multi-layer clothing sub-system, a simple solution scheme is given by defining the air element. Also, the model is validated by comparing the simulation with experimental results from the literature. Finally, the 3-D simulation results are shown. The conclusion shows that the model has good prediction ability and it can be applied in the clothing design.

The high-speed fabric defect detection algorithm based on fabric image layered model is proposed to achieve the goal of accurate defect detection in the fabric production process. The image layered model assumes that fabric image is a superposition of the periodic texture background image, noise image, and defect image. Thus fabrics can be divided and conquered. Firstly, image preprocessing and mean sampling algorithms were used to suppress the background texture and interference image layer, and variances sampling was used for enhancing defect image layer. Secondly, the Otsu method was applied for determining the segmentation threshold to segment the defect image automatically, then clear and accurate defect image was obtained via image post-treatment algorithm. Finally, defect positions were marked by a labeling algorithm to prepare for subsequent offine processing. Experiments on common defect images from a standard defect library were described, and experimental results show that the proposed algorithm based on image layered model is reliable, accurate, real-time and well used in the industrial field.

The biosorption characteristics of mercury ions from aqueous solution using jute nanofibers, a cellulose- based natural fiber, were explored as a function of pH, nanofiber concentration, contact time and temperature. Jute nanofibers were obtained after wet milling the jute fibers and subsequently their morphology was assessed by various spectroscopy and microscopy techniques. The maximum biosorption capacity of the fiber calculated by the Langmuir model was found to be 85.5 mg g^{1.} The adsorption experiments revealed that ion-exchange and complexation mechanisms were principal role in the biosorption process. The present experimental evidence implies that jute nanofibers could be a potential natural biomaterial for the removal of environmental contaminations from textile and chemical industries.

Butyl stearate and polyurea resin polymerized via toluene-2, 4-diisocyanate (TDI) and diethylene triamine (DETA) were respectively used as the core material and the shell material in preparing MicroPCMs. The phase change properties, chemical structure, surface morphology and particle size and size distribution of microcapsules were investigated by using DSC, FTIR, SEM and Malvern laser particle size analyzer. The results show that the prepared MicroPCMs were smooth and compact. Their melting temperature and enthalpies were 23.3 °C and 79.7 J ⋅ g^{-1} respectively. The average diameter of microencapsulated PCM (MicroPCMs) is in the range of 4.5 ˜ 10.2 μm under the stirring speed of 3000 ˜ 6000 rpm. Furthermore, a two-step method of adding DETA was firstly presented, through this method, the heat stability and ethanol wash stability of the MicroPCMs has been greatly improved, and the shell of MicroPCMs prepared by the two-step method has better stability and compactness than one-step method.

Fabric thickness and texture directly affect heat and moisture transfer characteristics in the human body-fabric-environment system, which determine the heat-moisture comfort level of the human body. Based on the model of steady-state heat and moisture transfer from skin to environment, we put forward an Inverse Problem of Thickness-texture Determination (IPTTD). Adopting the idea of the least-squares method, we formulate IPTTD into a function minimization problem. We employ Particle Swarm Optimization (PSO) method to directly search optimal solution of the objective function. The results of numerical simulation show the effectiveness of the presented algorithms and validity of the proposed IPTTD.

The purpose of this study was to investigate the relationship between wicking coefficients of fabrics and yarns. A range of plain fabrics were woven by varying the weave density, yarn count and fibre content. From experiments on the in-plane capillary water flow within these fabrics and the yarns obtained from the corresponding fabric, the wicking coefficients of fabrics and yarns were determined. The wicking coefficient was higher for lower weave density because of the effective capillary radius. The results for four kinds of yarns showed that the 100% cotton yarn and cotton fabric had the highest wicking rate. Based on scanning electron microscope observation of cross section and longitudinal section of yarns, we discussed the effects of inter-fibre space and yarn twist on the wicking influence factor and found that the wicking rate is higher for larger inter-fibre space and yarns with fewer twists.

With the rapid increase in the number of available 3D garment models, the ability to accurately and effectively search for 3D garment model is crucial for many applications such as industrial design and engineering, and in areas such as manufacturing. In this paper, a sketch-based 3D garment model retrieval algorithm is reported. Users can draw their sketch images according to their interest sensations. Then they can retrieve the 3D garment from the 3D garment database. Each garment in database is used to render an associated image to represent it. Then local features are extracted based on Gabor filtering for each image and the input sketch. The bag of features model is used for vectorizing each image and the sketch. Then the cosine similarities of each garment with the sketch are computed.Evaluations are performed to show the retrieval performance ofthe proposed algorithm.