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

PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization

A multitude of toxic online behaviors, ranging from network attacks to anonymous traffic and spam, have severely disrupted the smooth operation of networks. Due to the inherent sender-receiver nature of network behaviors, graph-based frameworks are commonly used for detecting anomalous behaviors. However, in real-world scenarios, the boundary between normal and anomalous behaviors tends to be ambiguous. The local heterophily of graphs interferes with the detection, and existing methods based on nodes or edges introduce unwanted noise into representation results, thereby impacting the effectiveness of detection. To address these issues, we propose PhoGAD, a graph-based anomaly detection framework. PhoGAD leverages persistent homology optimization to clarify behavioral boundaries. Building upon this, the weights of adjacent edges are designed to mitigate the effects of local heterophily. Subsequently, to tackle the noise problem, we conduct a formal analysis and propose a disentangled representation-based explicit embedding method, ultimately achieving anomaly behavior detection. Experiments on intrusion, traffic, and spam datasets verify that PhoGAD has surpassed the performance of state-of-the-art (SOTA) frameworks in detection efficacy. Notably, PhoGAD demonstrates robust detection even with diminished anomaly proportions, highlighting its applicability to real-world scenarios. The analysis of persistent homology demonstrates its effectiveness in capturing the topological structure formed by normal edge features. Additionally, ablation experiments validate the effectiveness of the innovative mechanisms integrated within PhoGAD.Comment: Accepted by WSDM 202