Polyaniline nanofibers (PAni-NFs) have been synthesized under various concentrations (0.12, 0.16, and 0.2 g/l) of aniline and different times (2h and 3 h) by hydrothermal method at 90°C. Was conducted with the use of X-ray diffraction (XRD), Fourier Transform Infrared spectra (FTIR), Ultraviolet-Visible (UV-VIS) absorption spectra, Thermogravimetric Analysis (TGA), and Field Emission-Scanning Electron Microscopy (FE-SEM). The X-ray diffraction patterns revealed the amorphous nature of all the produced samples. FE-SEM demonstrated that Polyaniline has a nanofiber-like structure. The observed typical peaks of PAni were (1580, 1300-1240, and 821 cm-1 ), analyzed by the chemical bonding of the formed PAni through FTIR spectroscopy. Also, tests

In this work, Co-Y-oxide Nano Structure is successfully synthesized via hydrothermal method. The XRD analysis, SEM analysis, optical, electrical and photo sensing properties have been investigated for Co3O4 and Co-Y-oxide thin films. The X-ray diffraction (XRD) analysis reveals that all films are polycrystalline in nature, having cubic structure. The SEM images of thin films clearly indicates that Co3O4 possesses nanosphere like structure and flower like for Co-Y-oxide. The optical properties show that the optical energy gap follows allowed direct electronic transition calculated using Tauc equation and it increases for Co-Y-oxide. The photo sensing properties of thin films are investigated as a function of time at different wavelengths to

Ternary polymer blend of chitosan/poly vinyl alcohol/ poly vinyl pyrrolidone was prepared by solution castingmethod, nanocomposite was prepared by sonication method with nano Ag and Zn. All prepared compounds have been characterizedby FT-IR, SEM, DSC, as well as Biological activity. Antimicrobialactivity related to prepared blendsand Nanocomposites againstsix types of bacteria namely, Staphylococcus aureas, E. faecalis, S.typhi, P. aeruginosa, Bacillus subtilis, Escherichia coli andC. albicans fungal were examined and evaluated. The results reveal that the prepared polymer blends and nanocompositeshavegood antimicrobial activity against all kinds of microbials.

In this study, the preparation and characterization of hyacinth plant /chitosan composite, as a heavy metal removal, were done. Water hyacinth plant (Eichhorniacrasspes) was collected from Tigris river in Baghdad. The root and shoot parts of plant were ground to powder. Composite materials were prepared at different ratios of plant part (from 2.9% to 30.3%, wt /wt) which corresponds to (30-500mg) of hyacinth plant (root and shoot) and chitosan. The results showed that all examined ratios of plant parts have an excellent absorption to copper (Cu (II)). Moreover, it was observed that 2.9% corresponds (30mg) of plant root revealed highest removal (82.7%) of Pb (II), while 20.23% of shoot removed 61% of Cd (II) within 24 hr

Polycyclicacetal was prepared by the reaction of PEG with 4-nitrobenzaldehyde. Cobalt was used for producing a polymer metal complex and solution casting was used to produce a polymer blend including nano chitosan. All produced compounds have been characterized by FT-IR, DSC/ TGA, and SEM techniques as well as biological activity. The production of polyacetal is illustrated by the FT-IR analysis. The DSC/TGA results indicate the prepared polymer blends' thermal stability. Staphylococcus aureas, Klebsiella pneumoniae, Bacillus subtilis, and Escherichia coli were the four types of bacteria selected to study and evaluate the antibacterial activity of produced polyacetal, its metal complex, and polymer blend. Results indicates that ther

 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification