EMI Shielding Characteristics of Electrically Conductive Polymer Blends of PS/PANI in Microwave and IR Region

Conductive polymeric blends (CPBs) of polystyrene and polyaniline (PS/PANI) were prepared by solution casting method in various compositions. Film thickness of CPBs was achieved?～?250 micron. PS/PANI blend films were analyzed for electromagnetic interference (EMI) shielding characteristics in microwave and near-infrared (NIR) regions. PS/PANI blends showed remarkable features. Most mobile telecommunications use GHz frequency range and shielding effectiveness was observed in 9 GHz to 18 GHz. In 9 GHz to 18 GHz frequency range, 45 dB shielding effectiveness was measured. CPBs were also analyzed in the NIR region and showed transmittance of <1%. Microwaves and NIR radiation are the most abundant in the environment and cause damage to human health. Both types of radiation causes serious damage to electronic devices as well.

     

H~+,N~+和Ar~+离子束辐射诱导碳纳米结构的变化(英文)

在室温和高温下,以不同辐射剂量的H+、N+和Ar+离子辐照多壁碳纳米管和无定形碳纳米线。利用透射电镜和拉曼光谱研究多壁碳纳米管和无定形碳纳米线的结构变化及损伤。以70keV N+离子束辐射多壁碳纳米管在室温下可形成无定形碳纳米线。1000K下70keV的H+离子束照射导致无定形碳纳米线向金刚石结构转变。离子辐照多壁碳纳米管的有序程度足够高,70keV的N+和Ar+离子能够引起碳从多壁碳纳米管的剥落。离子辐射能够为缺陷的转化提供必需的动力学驱动力。     

Electrochemical Pretreatment of Wastewater from Bulk Drug Manufacturing Industry

The present study involves the treatment of high strength Bulk Drug Industry Wastewater by electrochemical method. The treatability studies were carried out with four different electrodes made of mild steel, aluminum, carbon, and stainless steel. The treatment efficiencies for chemical oxygen demand (COD)/biochemical oxygen demand (BOD), color, and heavy metal removals were assessed at different electrolysis time. A comparative study for heavy metal removal between chemical precipitation using aluminum and electrocoagulation with aluminum electrode has shown electrocoagulation to be more effective. Carbon electrode has shown COD removal of 34.0% and high BOD5f/CODf ratio of 0.581 at 120-min exposure time. Among all electrodes, aluminum was found as the most efficient in removal of color, suspended solids, and heavy metals with the least energy consumption of 95.83?Wh?kg?1?CODr and anode efficiency of 5.76?kg?COD?A?1?m?2?h?1. After electrochemical treatment, certain increase in BOD/COD ratio attributed to increase in biodegradability of wastewater. The study reveals that the wastewater could be effectively pretreated by electrochemical method.     

Machine Learning Empowered Security Management and Quality of Service Provision in SDN-NFV Environment

With the rising demand for data access, network service providers face the challenge of growing their capital and operating costs while at the same time enhancing network capacity and meeting the increased demand for access. To increase efficacy of Software Defined Network (SDN) and Network Function Virtualization (NFV) framework, we need to eradicate network security configuration errors that may create vulnerabilities to affect overall efficiency, reduce network performance, and increase maintenance cost. The existing frameworks lack in security, and computer systems face few abnormalities, which prompts the need for different recognition and mitigation methods to keep the system in the operational state proactively. The fundamental concept behind SDN-NFV is the encroachment from specific resource execution to the programming-based structure. This research is around the combination of SDN and NFV for rational decision making to control and monitor traffic in the virtualized environment. The combination is often seen as an extra burden in terms of resources usage in a heterogeneous network environment, but as well as it provides the solution for critical problems specially regarding massive network traffic issues. The attacks have been expanding step by step; therefore, it is hard to recognize and protect by conventional methods. To overcome these issues, there must be an autonomous system to recognize and characterize the network traffic’s abnormal conduct if there is any. Only four types of assaults, including HTTP Flood, UDP Flood, Smurf Flood, and SiDDoS Flood, are considered in the identified dataset, to optimize the stability of the SDN-NFV environment and security management, through several machine learning based characterization techniques like Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR) and Isolation Forest (IF). Python is used for simulation purposes, including several valuable utilities like the mine package, the open-source Python ML libraries Scikit-learn, NumPy, SciPy, Matplotlib. Few Flood assaults and Structured Query Language (SQL) injections anomalies are validated and effectively-identified through the anticipated procedure. The classification results are promising and show that overall accuracy lies between 87% to 95% for SVM, LR, KNN, and IF classifiers in the scrutiny of traffic, whether the network traffic is normal or anomalous in the SDN-NFV environment.     

Development of optimized triaxially electrospun titania nanofiber-in-nanotube core-shell structure

One dimensional (1D) nanostructures and its derivatives can be manipulated to serve special functions like hollow structure, and higher surface area. 1D TiO₂ nanotube-in-nanofibers (NF@NT) are developed through triaxial electrospinning followed by a calcination process. A blended solution of polyvinyl pyrrolidone and tetra-butyl titanate is used in outer and inner layers of nanofibers, respectively, while paraffin oil is used in the middle layer. The optimized triaxial nanofibers of 669.4 ± 52.43 nm are developed at 7.5 w/w% concentration, 28 kV applied voltage, and 24 cm spinning distance. TiO₂ NF@NT structure is obtained through calcination of optimized triaxial nanofibers at 550°C. Subsequently, the morphology of TiO₂ NF@NT and its uniform diameter distribution is confirmed through scanning electron microscopy. Fourier-transform infrared spectroscopy results indicates the formation of TiO₂ NF@NT. X-Rays diffraction pattern peaks also reveals the presence of both anatase and rutile crystalline phases. The presence of only titanium (Ti) and oxygen (O) elements in the TiO₂ NF@NT is confirmed through energy dispersive X-ray spectroscopy. Brunauer–Emmett–Teller analysis indicates that TiO₂ NF@NT has a higher specific surface area of ~141.68 m²/g compared with the solid TiO₂ nanofiber (~75.31 m²/g). This study can be adopted to develop TiO₂ NF@NT for wide range of application.