Preparation and Spectrochemical characterization of Ni-doped ZnS nanocomposite for effective removal of emerging contaminants and hydrogen Production: Reaction Kinetics, mechanistic insights.

In this study, we report the successful synthesis of Ni-doped ZnS nanocomposite via a green route using ethanolic crude extract of Avena fatua. The as-synthesized nanocomposite was comprehensively characterized using Dynamic light scattering (DLS), Zeta potential, scanning electron microscopy (SEM), Transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), and Atomic force microscopy (AFM). These analyses provided detailed insights into the size, morphology, composition, surface properties, and structural characteristics of the nanocomposite. Subsequently, the synthesized nanocomposite was evaluated for their photocatalytic performance against the organic dye Methyl orange. Remarkably, the nanocomposite exhibited rapid and efficient degradation of Methyl orange, achieving 90 % degradation within only 30 min of irradiation under UV light. Moreover, the photocatalyst demonstrated an exceptional hydrogen production rate, reaching 167.73 µmolg-1h-1, which is approximately 4.5 times higher than that of its pristine counterparts. These findings highlight the significant potential of Ni-doped ZnS nanocomposite as highly efficient photocatalysts for wastewater treatment and hydrogen production applications.

Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete.

This research study aims to understand the application of Artificial Neural Networks (ANNs) to forecast the Self-Compacting Recycled Coarse Aggregate Concrete (SCRCAC) compressive strength. From different literature, 602 available data sets from SCRCAC mix designs are collected, and the data are rearranged, reconstructed, trained and tested for the ANN model development. The models were established using seven input variables: the mass of cementitious content, water, natural coarse aggregate content, natural fine aggregate content, recycled coarse aggregate content, chemical admixture and mineral admixture used in the SCRCAC mix designs. Two normalization techniques are used for data normalization to visualize the data distribution. For each normalization technique, three transfer functions are used for modelling. In total, six different types of models were run in MATLAB and used to estimate the 28th day SCRCAC compressive strength. Normalization technique 2 performs better than 1 and TANSING is the best transfer function. The best k-fold cross-validation fold is k = 7. The coefficient of determination for predicted and actual compressive strength is 0.78 for training and 0.86 for testing. The impact of the number of neurons and layers on the model was performed. Inputs from standards are used to forecast the 28th day compressive strength. Apart from ANN, Machine Learning (ML) techniques like random forest, extra trees, extreme boosting and light gradient boosting techniques are adopted to predict the 28th day compressive strength of SCRCAC. Compared to ML, ANN prediction shows better results in terms of sensitive analysis. The study also extended to determine 28th day compressive strength from experimental work and compared it with 28th day compressive strength from ANN best model. Standard and ANN mix designs have similar fresh and hardened properties. The average compressive strength from ANN model and experimental results are 39.067 and 38.36 MPa, respectively with correlation coefficient is 1. It appears that ANN can validly predict the compressive strength of concrete.

Insight into mechanism of excellent visible-light photocatalytic activity of CuO/MgO/ZnO nanocomposite for advanced solution of environmental remediation.

Environmental remediation has sought several innovative ways for the treatment of wastewater and captivated researchers around the globe towards it. Through this study, we aim to proceed with the efforts to foster sustainable and feasible ways for the treatment of wastewater. In this work, we report the sol-gel synthesis of CuO/MgO/ZnO nanocomposite and carry out their systematic characterization with the help of state-of-the-art analytical techniques, such as FTIR, SEM, TEM, PL, XRD, Raman, and AFM. The SEM along with TEM and AFM provided useful insights into the surface morphology of the synthesized nanocomposite on both 2D and 3D surfaces and concluded the well-dispersed behavior of the nanocomposite. The characteristic functional groups responsible for carrying out the reaction of Cu-O, Mg-O, and Zn-O were identified by FTIR spectroscopy. On the other hand, crystal size, dislocation density, and microstrain of the nanocomposite were calculated by XRD. For optical studies, photoluminescence spectroscopy was performed. Once the characterization of the nanocomposite was done, they were eventually treated against the toxic organic dye, methylene blue. The calculated rate constant values of k for CuO was 2.48 × 10-3 min-1, for CuO/MgO (2.04 × 10-3 min-1), for CuO/ZnO (1.82 × 10-3 min-1) and CuO/MgO/ZnO was found to be 2.00 × 10-3 min-1. It has become increasingly evident that nanotechnology can be used in various facets of modern life, and its implementation in wastewater treatment has recently received much attention.

Removal and recovery of U(VI) from aqueous effluents by flax fiber: Adsorption, desorption and batch adsorber proposal.

Flax fiber (Linen fiber), a valuable and inexpensive material was used as sorbent material in the uptake of uranium ion for the safe disposal of liquid effluent. Flax fibers were characterized using BET, XRD, TGA, DTA and FTIR analyses, and the results confirmed the ability of flax fiber to adsorb uranium. The removal efficiency reached 94.50% at pH 4, 1.2 g adsorbent dose and 100 min in batch technique. Adsorption results were fitted well to the Langmuir isotherm. The recovery of U (VI) to form yellow cake was investigated by precipitation using NH4OH (33%). The results show that flax fibers are an acceptable sorbent for the removal and recovery of U (VI) from liquid effluents of low and high initial concentrations. The design of a full scale batch unit was also proposed and the necessary data was suggested.

Enhanced Adsorption of Selenium Ions from Aqueous Solution Using Iron Oxide Impregnated Carbon Nanotubes.

The aim of this research was to investigate the potential of raw and iron oxide impregnated carbon nanotubes (CNTs) as adsorbents for the removal of selenium (Se) ions from wastewater. The original and modified CNTs with different loadings of Fe2O3 nanoparticles were characterized using high resolution transmission electron microscopy (HRTEM), scanning electron microscopy (SEM), X-ray diffractometer (XRD), Brunauer, Emmett, and Teller (BET) surface area analyzer, thermogravimetric analysis (TGA), zeta potential, and energy dispersive X-ray spectroscopy (EDS). The adsorption parameters of the selenium ions from water using raw CNTs and iron oxide impregnated carbon nanotubes (CNT-Fe2O3) were optimized. Total removal of 1 ppm Se ions from water was achieved when 25 mg of CNTs impregnated with 20 wt.% of iron oxide nanoparticles is used. Freundlich and Langmuir isotherm models were used to study the nature of the adsorption process. Pseudo-first and pseudo-second-order models were employed to study the kinetics of selenium ions adsorption onto the surface of iron oxide impregnated CNTs. Maximum adsorption capacity of the Fe2O3 impregnated CNTs, predicted by Langmuir isotherm model, was found to be 111 mg/g. This new finding might revolutionize the adsorption treatment process and application by introducing a new type of nanoadsorbent that has super adsorption capacity towards Se ions.