Biocompatible gold nanoparticles were successfully synthesized by hibiscus plant leaf extract as a bioreactor. The prepared nanoparticles were evaluated using UV/Vis spectroscopy, Atomic Force Microscopy (AFM), Fourier Transform Infrared Spectroscopy (FT-IR), X-ray Diffraction and Scanning Electron Microscopy (SEM) . The study also investigated the effect of the different gamma irradiation doses on the size and dispersion of the produced nanoparticles. In UV- Vis spectra, the peak of Au NPs' surface plasmon resonance (SPR) was detected as a single peak at 543 nm. The gamma irradiation induced a blue shift in the SPR peak, which indicates that it might be applied as affective factor for size control.  Physical as well as chemi

     A simple method was used to create a graphene oxide/chitosan (GO/CS) nanocomposite, which was then used in batch experiments to remove copper ions from industrial wastewater under various conditions of initial concentration, adsorbent weight, pH, and contact time. Maximum removal percentage equal to 99.4 % for initial copper ion concentration of 5x10^-2 mol/L at pH 6, time 75 min, temperature 25 °C, and adsorbing dose 0.1 g. The pseudo-second order kinetic model and the Freundlich isotherm adequately fit the experimental results. The process was spontaneous and endothermic, according to thermodynamic studies.

Background. “Polyetheretherketone (PEEK)” is a biocompatible, high-strength polymer that is well-suited for use in dental applications due to its unique properties. However, achieving good adhesion between PEEK and hydrophilic materials such as dental adhesives or cement can be challenging. Also, this hydrophobicity may affect the use of PEEK as an implant material. Surface treatment or conditioning is often necessary to improve surface properties. The piranha solution is the treatment of choice to be explored for this purpose. Methods. PEEK disks of 10 mm diameter and 2 mm thickness were used in this study. Those samples were divided into five groups (each group has five samples). The first is the control group, in which no

     In the present work, the possibility of treating many types of radioactive sources was examined practically. Six types of sealed radioactive sources were selected: ¹³⁷Cs, ¹³³Ba, ⁹⁰Sr, ¹⁵²Eu, ²²⁶Ra, and ²⁴¹Am. The sources were exposed to a neutron flux emitted from ²⁴¹Am/Be source for 33 days. The results showed a measurable reduction of activity for ²²⁶Ra, ²⁴¹Am, and ¹⁵²Eu, while the other radionuclides, ¹³⁷Cs, ¹³³Ba, and ⁹⁰Sr, showed less response to neutron incineration.

This paper presents designing an adaptive state feedback controller (ASFC) for a magnetic levitation system (MLS), which is an unstable system and has high nonlinearity and represents a challenging control problem. First, a nonadaptive state feedback controller (SFC) is designed by linearization about a selected equilibrium point and designing a SFC by pole-placement method to achieve maximum overshoot of 1.5% and settling time of 1s (5% criterion). When the operating point changes, the designed controller can no longer achieve the design specifications, since it is designed based on a linearization about a different operating point. This gives rise to utilizing the adaptive control scheme to parameterize the state feedback controll

In this paper, we have extracted Silica from rice husk ash (RHA) by sodium hydroxide to produce sodium silicate. 3-(chloropropyl)triethoxysilane (CPTES) functionalized with sodium silicate via a sol-gel method in one pot synthesis to prepare RHACCl. Chloro group in compound RHACCl replacement in iodo group to prepere RHACI. The FT-IR clearly showed absorption band of C-I at 580 cm^-1. Functionalized silica RHACI has high surface area (410 m²/g) and average pore diameter (3.8 nm) within mesoporous range. X-ray diffraction pattern showed that functionalized silica RHACI has amorphous phase .Thermogravemitric analysis (TGA) showed two decomposition stages and SEM morphology of RHACI showed that the particles have irregu

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D