F.H. She

Seeded growth of ZIF-8 on the surface of carbon nanotubes towards self-supporting gas separation membranes

We report the synthesis of the first continuous, inter-grown ZIF-8 membrane via the secondary growth method. ZIF-8 crystals were seeded and strongly anchored onto porous carbon nanotube bucky-paper supports and hydrothermally grown into a dense and continuous ZIF-8 network. The self-supporting hybrid metal organic framework membranes were characterized by scanning electron microscopy and shown to exhibit both a very homogeneous structure and a very smooth nanotube-metal organic framework interface. Gas adsorption (H2, CH4, CO2, N2) and permeation (He, CO2, N2 and Xe) tests were performed to evaluate the permeance of each gas and to predict the selectivity. The membranes were highly robust and sustained pressures as high as 500 kPa. Furthermore, the high selectivity of N2 over CO2 and Xe (33 and 163 respectively), shown by gas adsorption single gas permeation and mixed gas permeation clearly demonstrates the near defect-free nature of the membranes while the high hydrothermal stability of ZIF-8 makes these novel composites highly promising for water vapour saturated gas treatment.

Intelligent Animal Fiber Classification with Artificial Neural Networks

Artificial neural networks (ANN) are increasingly used to solve many problems related to pattern recognition and object classification. In this paper, we report on a study using artificial neural networks to classify two kinds of animal fibers: merino and mohair. We have developed two different models, one extracting nine scale parameters with image processing, and the other using an unsupervised artificial neural network to extract features automatically, which are determined in accordance with the complexity of the scale structure and the accuracy of the model. Although the first model can achieve higher accuracy, it requires more effort for image processing and more prior knowledge, since the accuracy of the ANN largely depends on the parameters selected. The second model is more robust than the first, since only raw images are used. Because only ordinary optical images taken with a microscope are employed, we can use the approach for many textile applications without expensive equipment such as scanning electron microscopy.

A morphing technique for facial image representation

Presents a method for the representation of facial images. The proposed method consists of two modules: face-image matching and face-image morphing. In the first module, the correspondence between two images are calculated for all pixel locations. A novel area-based matching method is proposed that makes use of the concept of the fractal dimension, and develops a non-parametric local transform as a basis for establishing the correspondence between two face images. In the second module, a mapping is performed for deformation of the source face image on to the target face image. This is done to map the pixels in the source face image to the location of their corresponding pixels in the target image.

Theoretical Investigation of Heat and Moisture Transfer through Porous Textile Materials

The phenomenon of heat and moisture transport through porous textile media is a natural problem encountered in real life and has been studied over years by many researchers. However, since the flow rate of moisture diffusing through a clothing fabric is too small to be measured directly, the measurement is usually indirect and the interaction between thermal and moisture transport is not considered. A mathematical model was introduced in this study to describe the moisture migration and thermal transport through porous textile materials to evaluate the thermal clothing comfort and the interaction between heat and moisture transportation. Heat and mass transportation parameters and the distribution of moisture and temperature within porous textiles are mathematically derived based on the energy and moisture conservation equations during the transportation. In addition, an experimental principle is established to simultaneously measure four moisture and thermal coefficients introduced in this study.

Selective removal of anionic dyes using poly (N, N -dimethyl amino ethylmethacrylate) functionalized graphene oxide

An in situ polymerization strategy was used to functionalize graphene oxide (GO) with poly(N,N-dimethyl amino ethylmethacrylate) (PDMAEMA) for the selective removal of anionic dyes. Various characterization methods demonstrate that PDMAEMA-grafted GO (GO-PDMAEMA) was successfully synthesized, and the high PDMAEMA content of 68.5% in GO-PDMAEMA changed the zeta potential significantly from −36.5 (GO) to 41.5 (GO-PDMAEMA). This change in the charge of GO-PDMAEMA greatly increased the adsorption capacities for anionic dye orange G (OG) compared to the pristine GO. The maximum adsorption capacity for anionic OG dye based on the Langmuir model is 609.8 mg g−1. The adsorption mechanism is believed to be a consecutive process of intra-particle diffusion and surface adsorption, with electrostatic interactions as the key driving force. The GO-PDMAEMA nanocomposite also showed excellent regeneration capacity and selectivity towards the separation of various anionic dyes (i.e. OG, Eosin yellow and Congo red) from an aqueous dye mixture. In conclusion, our method offers a promising strategy for developing new anionic dye adsorbents.

Fuzzy pattern recognition and classification of animal fibers

Several techniques, including chemical and physical approaches, have been previously developed to differentiate between animal fibers. Since all animal fibers are comprised of essentially the same keratin, they cannot be effectively distinguished by existing physical or chemical technique. A fuzzy neural pattern recognition system is developed to classify two typical animal fibers: mohair and merino. Two multilayer networks are used, with the unsupervised network being used for automatic feature extraction and the supervised network serving as the classifier based on the information extracted from unsupervised network. It is found that this hybrid network can accurately classify the two fibers and the accuracy improves with the increase in the features being extracted from the unsupervised network.

Development of Ligand Incorporated Chitosan Nanoparticles for the Selective Delivery of 5-Fluorouracil to Colon

Drug delivery systems with active targeting ligand provide improved therapeutic efficiency due to the selectivity towards tumor cells. In this paper we prepared drug loaded nanoparticles (NPs) using folate (FA) incorporated chitosan (FA-CS) based on ionic gelation technology. FA-CS NPs were spherical in shape with an average particle size of 100 nm, while 5-fluorouracil (5-FU) loaded NPs became less circular with average particle size of 100-500 nm. NPs made from FA-CS conjugates exhibited improved capability to encapsulate hydrophilic 5-FU. It was found 5-FU distributed in FA-CS NPs in solid solution state. In vitro release results demonstrated the release of 5-FU from FA-CS NPs was more controllable as compared to that of CS NPs.

Thermal control with image processing and fuzzy controllers

Image processing is used to identify areas of different temperatures in die thermal images for thermal control of high pressure die casting. Areas of higher and lower temperature ranges than the optimum empirical/experimental range can be identified. Using the heat index developed, the heat stored in different areas of the die can be quantitatively calculated and controlled using fuzzy neural networks. This fuzzy neural networks control system makes the decision about whether to take more (H/sub -/) or less heat (H/sub +/) away from a specific area.

Particle Dynamics and Heat Transfer at Workpiece Surface in Heat Treatment Fluidised Beds

The particle behaviour in a heat treatment fluidised bed was studied by the analysis of particle images taken with a high speed CCD digital video camera. The comparison of particle dynamics was performed for the fluidised beds without part, with single part and with multi-parts. The results show that there are significant differences in particle behaviours both in different beds and at different locations of part surfaces. The total and radiative heat transfer coefficients at different surfaces of a metallic part in a fluidised bed were measured by a heat transfer probe developed in the present work. The structure of the probe was optimized with numerical simulation of energy conservation for measuring the heat transfer coefficient of 150-600 W/m2K. The relationship between the particle dynamics and the heat transfer was analysed to form the basis for future more rational designs of fluidised beds as well as for improved quality control.

Image processing and pattern recognition in textiles

Image processing and pattern recognition have been successfully applied in many textile related areas. For example, they have been used in defect detection of cotton fibers and various fabrics. In this work, the application of image processing into animal fiber classification is discussed. Integrated into/with artificial neural networks, the image processing technique has provided a useful tool to solve complex problems in textile technology. Three different approaches are used in this work for fiber classification and pattern recognition: feature extraction with image process, pattern recognition and classification with artificial neural networks, and feature recognition and classification with artificial neural network. All of them yields satisfactory results by giving a high level of accuracy in classification.