This study investigates and compares arsenic, As(V), removal from aqueous media using the water-stable zinc metal−organic frameworks (Zn-MOF-74) prepared via room-temperature precipitation (RT-Zn-MOF-74) and a solvothermal procedure (HT-Zn-MOF-74). The Zn-MOF-74 crystals possess average particle sizes of 66 nm and 144 μm for RT-Zn-MOF-74 and HT-Zn-MOF-74, respectively. Moreover, nanosized RT-Zn-MOF-74 exhibited a superior performance to HT-Zn-MOF-74. While the Brunauer− Emmett−Teller surface area of RT-Zn-MOF-74 was smaller than that of HT-Zn-MOF-74, higher adsorption uptake took place on the room-temperature-synthesized ones because of their small particle size and better dispersion. Adsorption isotherm studies showed that the Langmuir isotherm was effective for the adsorption of As(V) onto RT-Zn-MOF-74 and HT-Zn-MOF-74 with maximum adsorption uptake (q max ) values of 99.0 and 48.7 mg g −1 , respectively. These values exceed most reported maximum adsorption capacities at neutral pH. The thermodynamics of adsorption revealed a spontaneous endothermic process that is due to the substitution of adsorbed water molecules by arsenate in the pores of the MOF crystal. This was further investigated using plane-wave density functional theory calculations. This study constitutes direct evidence for the importance of tuning the size of the MOF crystals to enhance their properties.

No abstract

No abstract

Wireless networks, in the fifth-generation and beyond, must support diverse network applications which will support the numerous and demanding connections of today's and tomorrow's devices. Requirements such as high data rates, low latencies, and reliability are crucial considerations and artificial intelligence is incorporated to achieve these requirements for a large number of connected devices. Specifically, intelligent methods and frameworks for advanced analysis are employed by the 5G Core Network Data Analytics Function (NWDAF) to detect patterns and ascribe detailed action information to accommodate end users and improve network performance. To this end, the work presented in this paper incorporates a functional NWDAF into a 5G network developed using open source software. Furthermore, an analysis of the network data collected by the NWDAF and the valuable insights which can be drawn from it have been presented with detailed Network Function interactions. An example application of such insights used for intelligent network management is outlined. Finally, the expected limitations of 5G networks are discussed as motivation for the development of 6G networks.

No abstract