Experimental modelling studies on the removal of dyes and heavy metal ions using ZnFe2O4 nanoparticles.

The presence of dyes and heavy metals in water sources as pollutants is harmful to human and animal health. Therefore, this study aimed to evaluate the efficacy of zinc ferrite (ZnFe2O4) nanoparticles (ZF-NPs) due to their outstanding properties including cost-effectiveness, availability, and applicability for removal of auramine O (AO), methylene blue (MB), and Cd (II). The effect of the main operating parameters such as AO concentration, MB concentration, Cd (II) concentration, adsorbent amount, solution pH, and sonication time was optimized by the response surface methodology (RSM). Optimal conditions were obtained at adsorbent amount of 0.25 g, pH = 6, sonication time of 15 min, and concentration of 15 mg L-1, and more than 91.56% were removed from all three analytes. The adsorption of AO, MB, and Cd (II) onto ZF-NPs followed pseudo-second-order kinetics and the equilibrium data fitted well with Langmuir isotherm. The maximum adsorption capacities of ZF-NPs for AO, MB and Cd (II) were as high as 201.29 mg g-1, 256.76 mg g-1 and 152.48 mg g-1, respectively. Also, the reuse of the adsorbent was investigated, and it was found that the adsorbent can be used for up to five cycles. Based on the results of interference studies, it was found that different ions do not have a significant effect on the removal of AO, MB, and Cd (II) in optimal conditions. The ZF-NPs was investigated successfully to remove AO, MB, and Cd (II) from environmental water samples. The results of this study showed that ZF-NPs can be used as a suitable adsorbent to remove AO, MB, and Cd (II) from aqueous solution.

Deep learning approach for chemistry and processing history prediction from materials microstructure.

Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials' microstructure, they are not efficient techniques for predicting processing and chemistry if a specific morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition simulation of Fe-Cr-Co alloy created by the phase-field method. The mixed dataset, which includes both images, i.e., the morphology of Fe distribution, and continuous data, i.e., the Fe minimum and maximum concentration in the microstructures, are used as input data, and the spinodal temperature and initial chemical composition are utilized as the output data to train the proposed deep neural network. The proposed convolutional layers were compared with pretrained EfficientNet convolutional layers as transfer learning in microstructure feature extraction. The results show that the trained shallow network is effective for chemistry prediction. However, accurate prediction of processing temperature requires more complex feature extraction from the morphology of the microstructure. We benchmarked the model predictive accuracy for real alloy systems with a Fe-Cr-Co transmission electron microscopy micrograph. The predicted chemistry and heat treatment temperature were in good agreement with the ground truth.

Application of Taguchi method and response surface methodology into the removal of malachite green and auramine-O by NaX nanozeolites.

In the present study, the simultaneous removal of malachite green (MG) and auramine-O (AO) dyes from the aqueous solution by NaX nanozeolites in a batch system is investigated. Taguchi method and response surface methodology (RSM) were used to optimize and model dye removal conditions. In order to do so, the effect of various factors (dyes concentration, sonication time, ionic strength, adsorbent dosage, temperature, and pH of the solution) on the amount of dye removal was evaluated by the Taguchi method. Then, the most important factors were chosen and modeled by the RSM method so as to reach the highest percentage of dye removal. The proposed quadratic models to remove both dyes were in good accordance with the actual experimental data. The maximum removal efficiencies of MG and AO dyes in optimal operating conditions were 99.07% and 99.61%, respectively. Also, the coefficients of determination (R2) for test data were 0.9983 and 0.9988 for MG and AO dyes, respectively. The reusability of NaX nanozeolites was evaluated during the adsorption process of MG and AO. The results showed that the adsorption efficiency decreases very little up to five cycles. Moreover, NaX nanozeolites were also applied as adsorbents to remove MG and AO from environmental water samples, and more than 98.1% of both dyes were removed from the solution in optimal conditions.