Preparation and properties of novel activated carbon doped with aluminum oxide and silver for water treatment

Novel activated carbon (AC) composite materials, namely AC doped with aluminum oxide (Al2O3) and AC doped with Al2O3 and silver (Ag) nanoparticles, have been prepared via a one-step thermal decomposition method. The developed composite materials were used to study the adsorptive removal of molybdenum (Mo) and arsenic (As) from contaminated water. Several techniques, including X-Ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron spectroscopy (SEM), energy dispersive X-Ray spectroscopy (EDS), and thermal gravimetric analysis (TGA), were used to characterize the synthesized materials. TGA results show that the material is very stable and decay starts only above 450 °C. The effects of pH on the adsorptive removal of As and Mo on AC-Al2O3 have also been studied. The prepared AC-Al2O3 material showed 94% removal of total As at pH of 6% and 97% removal of Mo at pH 2. The pollutants removal is due to electrostatic attraction and ligand exchange adsorption mechanisms. It was also found that the novel AC-Al2O3-Ag composite materials exhibit notable antibacterial properties towards both Gram-negative (Escherichia coli) and Gram-positive (Bacillus subtilis) bacteria.Open access funding provided by the Qatar National Library

Morphology-Controlled Aluminum-Doped Zinc Oxide Nanofibers for Highly Sensitive NO2 Sensors with Full Recovery at Room Temperature

Room-temperature (RT) gas sensitivity of morphology-controlled free-standing hollow aluminum-doped zinc oxide (AZO) nanofibers for NO2 gas sensors is presented. The free-standing hollow nanofibers are fabricated using a polyvinylpyrrolidone fiber template electrospun on a copper electrode frame followed by radio-frequency sputtering of an AZO thin overlayer and heat treatment at 400 degrees C to burn off the polymer template. The thickness of the AZO layer is controlled by the deposition time. The gas sensor based on the hollow nanofibers demonstrates fully recoverable n-type RT sensing of low concentrations of NO2 (0.5 ppm). A gas sensor fabricated with Al2O3-filled AZO nanofibers exhibits no gas sensitivity below 75 degrees C. The gas sensitivity of a sensor is determined by the density of molecules above the minimum energy for adsorption, collision frequency of gas molecules with the surface, and available adsorption sites. Based on finite-difference time-domain simulations, the RT sensitivity of hollow nanofiber sensors is ascribed to the ten times higher collision frequency of NO2 molecules confined inside the fiber compared to the outer surface, as well as twice the surface area of hollow nanofibers compared to the filled ones. This approach might lead to the realization of RT sensitive gas sensors with 1D nanostructures

Efficient Charge Transfer Mechanism in Polyfluorene/ZnO Nanocomposite Thin Films

The optical properties and charge transfer mechanism of poly (9,9′-di-n-octylfluorenyl-2.7-diyl) (PFO)/ZnO thin films have been investigated. The ZnO nanorods (NRs) were prepared via a microwave technique. The solution blending method was used to prepare the PFO/ZnO nanocomposites. X-ray diffraction (XRD) and field emission scanning electron microscope (FE-SEM) were used to determine the structural properties, while UV-Vis and photoluminescence (PL) were employed to investigate the optical properties of the films. XRD patterns confirmed that there was no variation in the structure of both PFO and ZnO NRs due to the blending process. FE-SEM micrographs displayed that ZnO NRs were well coated by PFO in all nanocomposite films. The absorption spectra of the nanocomposite thin films exhibited a red-shift indicating the increment in conjugation length of the PFO/ZnO nanocomposite. Significant quenching in the emission intensity of PFO was observed in fluorescence spectra of the nanocomposite films. This quenching was attributed to efficient charge transfer in the PFO/ZnO nanocomposites, which was further supported by the shorter PL lifetime of PFO/ZnO than that of the PFO thin film. The continuous decline in PL intensity of these nanocomposites is attributed to homogenous dynamic quenching between PFO and ZnO NRs

Antibiofouling Performance by Polyethersulfone Membranes Cast with Oxidized Multiwalled Carbon Nanotubes and Arabic Gum

Despite extensive research efforts focusing on tackling membrane biofouling, one of the biggest problems associated with membrane technology, there has been little headway in this area. This study presents novel polyethersulfone (PES) membranes synthesized via a phase inversion method at incremental loadings of functionalized oxidized multiwalled carbon nanotubes (OMWCNT) along with 1 wt. % arabic gum (AG). The synthesized OMWCNT were examined using scanning electron microscopy and transmission electron microscopy for morphological changes compared to the commercially obtained carbon nanotubes. Additionally energy-dispersive X-ray spectroscopy was carried out on the raw and OMWCNT materials, indicating an almost 2-fold increase in oxygen content in the latter sample. The cast PES/OMWCNT membranes were extensively characterized, and underwent a series of performance testing using bovine serum albumin solution for fouling tests and model Gram-positive (Bacillus subtilis) and Gram-negative (Escherichia coli) bacterial species for anti-biofouling experiments. Results indicated that the composite PES membranes, which incorporated the OMWCNT and AG, possessed significantly stronger hydrophilicity and negative surface charge as evidenced by water contact angle and zeta potential data, respectively, when compared to plain PES membranes. Furthermore atomic force microscopy analysis showed that the PES/OMWCNT membranes exhibited significantly lower surface roughness values. Together, these membrane surface features were held responsible for the anti-adhesive nature of the hybrid membranes seen during biofouling tests. Importantly, the prepared membranes were able to inhibit bacterial colonization upon incubation with both Gram-positive and Gram-negative bacterial suspensions. The PES/OMWCNT membranes also presented more resilient normalized flux values when compared to neat PES and commercial membrane samples during filtration of both bacterial suspensions and real treated sewage effluents. Taken together, the results of this study allude to OMWCNT and AG as promising additives, for incorporation into polymeric membranes to enhance biofouling resistance

Enhanced Fouling Resistance and Antibacterial Properties of Novel Graphene Oxide-Arabic Gum Polyethersulfone Membranes

Membrane biofouling has proved to be a major obstacle when it comes to membrane efficiency in membrane-based water treatment. Solutions to this problem remain elusive. This study presents novel polyethersulfone (PES) membranes that are fabricated using the phase inversion method at different loadings of graphene oxide (GO) and 1 wt. % arabic gum (AG) as nanofiller and pore forming agents. Synthesized GO was examined using scanning electron microscopy (SEM) and transmission electron microscopy (TEM) for morphological studies and energy-dispersive X-ray spectroscopy (EDX) for elemental analysis. The prepared GO flakes showed a high content of oxygen-containing groups (~31%). The fabricated membranes were extensively characterized, including water contact angle analysis for hydrophilicity, zeta potential measurements for surface charge, SEM, total porosity and pore size measurements. The prepared membranes underwent fouling tests using bovine serum albumin (BSA) solutions and biofouling tests using model Gram-positive (Bacillus subtilis) and Gram-negative (Escherichia coli) bacterial suspensions as well as real treated sewage effluent (TSE). The results showed that the novel PES/GO membranes possessed strong hydrophilicity and negative surface charge with an increase in porosity, pore size and water flux. The PES/GO membranes exhibited superior antibacterial action against both Gram-positive and Gram-negative bacterial species, implicating PES membranes which incorporate GO and AG as novel membranes that are capable of high antibiofouling properties with high flux

Effects of annealing temperature on the phase formation, optical, photoluminescence and magnetic properties of sol-gel YFeO3 films

YFeO3 (YFO) thin films were deposited onto quartz substrates via sol-gel spin-coating technique and annealed at different temperature ranged between 650 and 900 °C. The impact of annealing temperature on the phase formation, microstructural, optical, photoluminescence (PL) and magnetic properties of the films were systematically investigated. X-ray diffraction analysis revealed an amorphous structure in film annealed at 650 °C and formation of hexagonal-YFO (h-YFO) phase in films annealed at 750–800 °C. The films annealed at 850–900 °C exhibited an orthorhombic-YFO (o-YFO) structure. Atomic force microscopy images of h-YFO films showed homogeneous surface with uniform particles size and shape. The particle size increased and had irregular shape in o-YFO films. The average particle size was 44 and 117 nm, while the root square roughness was 1.38 and 2.55 nm for h- and o-YFO films annealed at 750 and 850 °C, respectively. The optical band gap (Eg) was 2.53 and 2.86 eV for h- and o-YFO films annealed at 750 and 850 °C, respectively. The PL spectra of h-YFO films were red-shifted compared with that of o-YFO films. The PL emission related to near band edge was observed at 459.0 and 441.9 nm for h- and o-YFO films annealed at 750 and 850 °C, respectively. The magnetization was enhanced with the increasing of annealing temperature and has the value of 4.8 and 12.5 emu/cm3 at 5000 Oe for h- and o-YFO films annealed at 750 and 850 °C, respectively

Intelligent System Architecture for Smart City and its Applications Based Edge Computing

There is no doubt that smart city applications dramatically increase and the need for smart cities in our modern life becomes a demand. Smart cities will enable various applications and introduce many market innovations. However, the dramatic increase in wireless devices and network traffic puts many constraints in designing such networks. To this end, we provide to develop a reliable smart city system that enables various heterogeneous applications and provides the communication infrastructure for the expected enormous number of wireless devices. The proposed system is scalable so that the increase in network traffic will be supported with no degradation in network performances. The system deploys edge computing servers and artificial intelligence. In this study, we simulated a model for the structure of a smart city based on heterogeneous edge computing and defined evaluation parameters. Finally, according to the analysis of the results will be developed a prototype for the practical implementation of the selected method using a specific example is based on a neural network algorithm to generate forecasts of Internet of Things traffic activity. © 2020 IEEE

Using a Resnet50 with a Kernel Attention Mechanism for Rice Disease Diagnosis

The domestication of animals and the cultivation of crops have been essential to human development throughout history, with the agricultural sector playing a pivotal role. Insufficient nutrition often leads to plant diseases, such as those affecting rice crops, resulting in yield losses of 20–40% of total production. These losses carry significant global economic consequences. Timely disease diagnosis is critical for implementing effective treatments and mitigating financial losses. However, despite technological advancements, rice disease diagnosis primarily depends on manual methods. In this study, we present a novel self-attention network (SANET) based on the ResNet50 architecture, incorporating a kernel attention mechanism for accurate AI-assisted rice disease classification. We employ attention modules to extract contextual dependencies within images, focusing on essential features for disease identification. Using a publicly available rice disease dataset comprising four classes (three disease types and healthy leaves), we conducted cross-validated classification experiments to evaluate our proposed model. The results reveal that the attention-based mechanism effectively guides the convolutional neural network (CNN) in learning valuable features, resulting in accurate image classification and reduced performance variation compared to state-of-the-art methods. Our SANET model achieved a test set accuracy of 98.71%, surpassing that of current leading models. These findings highlight the potential for widespread AI adoption in agricultural disease diagnosis and management, ultimately enhancing efficiency and effectiveness within the sector