Electrospinning Technology in Non-Woven Fabric Manufacturing

In the past two decades, research on electrospinning has boomed due to its simple process, small fiber diameter, and special physical and chemical properties. The electrospun fiber is spontaneously collected in a non-woven status in most cases. Therefore, the electrospinning method is becoming an ideal candidate for non-woven fabric manufacturing on a nano scale. More than 50,000 research papers have been published linked to the concept of "electrospinning", and the number is still increasing rapidly. At the early stage of electrospinning research, most of the published papers mainly focused on the research of spinning theories, material systems, and spinning processing. Since then research has turned to functional electrospun fiber preparation and characterization. In recent years, more and more researchers have started to develop a scaling-up method related to the applied products of electrospinning. Interestingly, most electrospinning products are in a non-woven state; that is why we dedicate one chapter to exhibit ongoing, on-woven fabric manufacturing and the basic research progress made using the electrospinning method

Corrosion behaviors of chromia-forming commericial alloys in CO2 gas at high temperatures

As a newly developed technology, oxyfuel combustion makes CO2 capture and sequestration feasible but raises a corrosion problem because of high concentrations of CO2 in flue gases at high temperatures. Conventional ferritic/martensitic heat resisting steels are sufficient to resist corrosion in oxygen or air but cannot survive in CO2-rich gases. To increase the energy production efficiency, higher temperatures will be used for energy production. As a result, austenitic steels (e.g. stainless steels) and/or nickel-base alloys are required because of their excellent corrosion resistance and mechanical properties at high temperatures. This thesis investigates the corrosion behavior of six commercial alloys, including three austenitic steels (304SS, 800H and AC66) and three nickel-based alloys (230, 617 and 601) at 750℃ and 850℃ in a carbon dioxide environment up to 500 h. For three austenitic steels, AC66 behaved protectively by forming a thin chromia layer with the lowest weight gain kinetics, while 304SS showed the worst oxidation resistance with apparent scale spallation. 800H formed partial protection with the mixture of oxide nodules and a thin protective layer. Cross-section analysis of the reacted steels revealed the formation of external Fe-rich oxides and an internal spinel, together with chromia bands for both 304SS and 800H. All three nickel base alloys showed excellent oxidation resistance, forming a protective chromia layer. In addition to this chromia layer, a small amount of Ni-rich oxide was found on the top of chromia layer, together with some alumina or silica precipitated at the interface between the oxide and the matrix. The presence of alloying elements of Al, Mn, Si, Ti was found to diffuse and integrate with oxide, forming additional oxides or combining with Cr to form spinel which enhances the corrosion resistance. Moreover, as the temperature increased, the oxide thickening kinetics increased for all alloys. Carburization due to the reaction was identified by the observation of an increased carbide density in both Fe-based alloys and Ni-based alloys after a 500h reaction at 850℃. Since carbon diffusion was much slower in the nickel-base alloys, less carburization was observed in nickel-based alloys than in iron-based alloys. In addition, increasing the temperature from 750℃ to 850℃ enhanced carbide formation for both types of alloys

Towards Making Deep Transfer Learning Never Hurt

Transfer learning have been frequently used to improve deep neural network training through incorporating weights of pre-trained networks as the starting-point of optimization for regularization. While deep transfer learning can usually boost the performance with better accuracy and faster convergence, transferring weights from inappropriate networks hurts training procedure and may lead to even lower accuracy. In this paper, we consider deep transfer learning as minimizing a linear combination of empirical loss and regularizer based on pre-trained weights, where the regularizer would restrict the training procedure from lowering the empirical loss, with conflicted descent directions (e.g., derivatives). Following the view, we propose a novel strategy making regularization-based Deep Transfer learning Never Hurt (DTNH) that, for each iteration of training procedure, computes the derivatives of the two terms separately, then re-estimates a new descent direction that does not hurt the empirical loss minimization while preserving the regularization affects from the pre-trained weights. Extensive experiments have been done using common transfer learning regularizers, such as L2-SP and knowledge distillation, on top of a wide range of deep transfer learning benchmarks including Caltech, MIT indoor 67, CIFAR-10 and ImageNet. The empirical results show that the proposed descent direction estimation strategy DTNH can always improve the performance of deep transfer learning tasks based on all above regularizers, even when transferring pre-trained weights from inappropriate networks. All in all, DTNH strategy can improve state-of-the-art regularizers in all cases with 0.1%--7% higher accuracy in all experiments.Comment: 10 page