Challenges in Nano and Micro Scale Science and Technology
Volume:11 Issue: 1, Winter-Spring 2023

Polymer-modified metal organic frameworks (MOFs) can enhance water purification processes by offering increased adsorption capacity, making them highly effective in separating pollutants from contaminated water. In this research, a facile and low-cost route was used to prepare Polydopamine@Zeolitic Imidazolate Framework-67 (PDA@ZIF-67). The structure, morphology, surface functional groups and particle size distribution of PDA@ZIF-67 was studied using Fourier-transform infrared spectroscopy (FTIR), Field emission scanning electron microscopy (FESEM), Energy dispersive spectroscopy (EDS), and Brunauer-Emmet-Teller (BET) analyses. The specific surface area and pore diameter of PDA@ZIF-67 nanoparticles were found to be 203.78 m2/g and 4.179 nm, respectively. The PDA@ZIF-67 was used as an adsorbent for removal of methylene blue dye from aqueous solution. The results showed that the maximum adsorption capacity of PDA@ZIF-67 for removal of MB was achieved at pH 2, 65 °C, 10 mg of adsorbent, and methylene blue concentration of 7.5 ppm. Moreover, the adsorption process isotherms, thermodynamics, and kinetics were studied to ascertain the adsorption mechanism. The methylene blue molecules located in the fine pores of the PDA@ZIF-67 adsorbent determine the adsorption rate. Moreover, it was found that the adsorption process of methylene blue at elevated temperature is a spontaneous and endothermic reaction. The adsorption capacity of PDA@ZIF-67, after six recovery cycles decreased to 62.21%, which can be considered as an excellent advantage for this adsorbent.

Identification of heavy metal ions has become a global problem due to continuous industrial and human pollution. In the present study, first, the synthesis of aluminate nanostructures was done by sol-gel method. Then, in order to study and measure copper(II) ions, a modified electrochemical sensor was designed based on synthetic nanostructures. Characterization of the nanostructures and designed nanostructure sensors was performed by various techniques including Fourier transform infrared spectroscopy (FT-IR), field emission scanning electron microscopy (FE-SEM), electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). The conducted studies showed the catalytic effect of the nanostructures for copper(II) ions. Then, the measurement of the analyte was investigated on the surface of a carbon paste electrode modified with the nanostructures. For this purpose, electrochemical techniques were studied. Then, the detection limit and dynamic linear range of copper(II) ions were determined by DPV technique. Therefore, the dynamic range of the analyte was from 0.02 µM to 350.00 µM and the detection limit was found to be 1.20 nM. Finally, for investigation of the ability of the proposed method, the analyte was detected in real samples and so that acceptable recovery percentages were obtained.

β-Amino alcohols play a crucial role in natural product synthesis, especially in the pharmaceutical industry, serving as important intermediates in advancing of medicinal chemistry and drug discovery. This study presents a new method for synthesizing β-amino alcohols using an eco-friendly nanocomposite Fe3O4@SiO2@Chitosan@POCl2-x with an average size of 157nm, achieving rapid efficiency through the aminolysis of an azide compound in the presence of CH3CN-H2O (9:1) and heat (80°C), resulting in yields of 77-90%. The presence of chitosan enhances the catalyst's environmental friendliness and promotes the smooth conversion of azides into amino alcohols. This conversion process involves mechanisms such as water incorporation and proton transfer, facilitated by the surface hydroxyl and amine groups on the nanocomposite. The catalyst demonstrates exceptional performance under mild conditions, promising high yields and highlighting its significance in organic synthesis. Furthermore, the catalyst's magnetic recoverability, diverse composition, and ability to facilitate efficient and clean reactions underscore its indispensable role in amino alcohol synthesis.

Nanoparticles unique chemical and physical properties signifiicantly differentiate them from their bulk and macroscopic counterparts. Their small size and specific surface area contribute to these differences. Nickel-magnesium-cobalt ferrite is a well-known spinel ferrite magnetic material recognized for excellent performance at high frequencies. It is a novel composite material formed by incorporating a specific amount of magnesium into the nickel-cobalt ferrite matrix, paving the way for further research into new combinationsNickel-iron-cobalt alloys are among the most significant magnetic metals, exhibiting significant ferromagnetism at temperatures above room temperature (RT). In this study, nickel-cobalt-magnesium-iron nanoparticles were synthesized using the coprecipitation method. The initial raw materials including metal nitrates, sodium hydroxide, and citric acid, were used for the occurrence of the reaction. Magnesium was substituted for nickel in the nickel-cobalt ferrite powders for a short duration. The resulting materials were analyzed using X-ray Diffraction (XRD), Scanning Electron Microscope (SEM), Fourier Transform Infrared (FTIR), and Vibrating Sample Magnetometry (VSM). The magnesium substitution influenced the particle size and magnetic properties of MgxNi1-xCo0.5Fe2O4 nanoparticles. The average size of the crystallites increased with the addition of magnesium while the lattice constant remained stable ranging from 8.375 to 8.397 nm. All samples exhibited a simple cubic crystal (SC) structure with a cauliflower-like morphology. According to the VSM results, the hysteresis loops indicate that as magnesium is added and nickel is reduced in the samples, both the Saturation Magnetization (Mₛ) and Remanent Magnetization (Mᵣ) decreased. Adding magnesium and reducing nickel in the samples decreased the saturation magnetization (MS) from 3.19 emu/g to 0.12 emu/g while the residual magnetization (Mr) decreased from 0.54 to 4.32 × 10-3 emu/g. Samples M01, M02, and M03 displayed ferromagnetic properties, whereas sample M04 exhibited characteristics similar to paramagnetism.

This research focuses on the removal of the dye methyl orange from aqueous solutions, a common environmental pollutant found in textile and dyeing industries. For this purpose, the efficiency of the photocatalytic degradation process using Fe2O3@ZnO core-shell nanocomposite was investigated in order to remove dye pollutant methyl orange from aqueous solutions using the response surface method (RSM) in the central composite design (CCD). Scanning electron microscopic (SEM), X-ray diffraction (XRD) and X-ray energy diffraction (EDX) analyzes were used to investigate the structural characteristics of the synthesized nanocomposite. Then, in order to investigate the efficiency of the synthesized nano photocatalyst in the process of photocatalytic degradation, the effect of important functional variables such as photocatalyst dose, pH, temperature and time were investigated to optimize the maximum dye removal. Based on the statistical results, the optimal efficiency of dye removal was 49.80% with a catalyst dose of 3 g/L, pH = 4, temperature 44.12 °C and irradiation time 148.34 min. Among the investigated parameters, temperature and pH had a significant effect on the photocatalytic degradation process. Also, the recovery of Fe2O3@ZnO photocatalyst during five consecutive cycles showed that the synthesized nanocomposite has good recovery capability for removing methyl orange from aqueous solutions. The experimental results indicate that the use of Fe2O3@ZnO nanoparticles has a high efficiency in removing dye pollutants and can also be an ideal method for treating dye-polluted wastewater, due to their excellent selective absorption and good recovery.

 Adaptive Kalman filtering method is based on generating a separable Variational model for estimating joint posterior distribution of states in dynamical system and noise parameters on each time step separately. In this article we present a Gaussian approximation based framework for optimal smoothing of non-linear stochastic state space models, and also time-varying noisy measurements of the system are obtained at discrete instances of time. It is also shown how the method can be applied to a class of models. The result is a recursive algorithm, where on each step the state is estimated with Kalman filter and the sufficient statistics of the noise variances are estimated. We also numerically compare accuracies and error performance of the algorithm with different simulated data. We also numerically compare accuracies and error performance of the algorithm with different simulated data. We also numerically compare accuracies and error performance of the algorithm with different simulated data.