Kinetics and Adsorption Isotherms of Amine-Functionalized Magnesium Ferrite Produced Using Sol-Gel Method for Treatment of Heavy Metals in Wastewater.

This study is focused on the kinetics and adsorption isotherms of amine-functionalized magnesium ferrite (MgFe2O4) for treating the heavy metals in wastewater. A sol-gel route was adopted to produce MgFe2O4 nanoparticles. The surfaces of the MgFe2O4 nanoparticles were functionalized using primary amine (ethanolamine). The surface morphology, phase formation, and functionality of the MgFe2O4 nano-adsorbents were studied using the SEM, UV-visible, FTIR, and TGA techniques. The characterized nanoparticles were tested on their ability to adsorb the Pb2+, Cu2+, and Zn2+ ions from the wastewater. The kinetic parameters and adsorption isotherms for the adsorption of the metal ions by the amine-functionalized MgFe2O4 were obtained using the pseudo-first-order, pseudo-second-order, Langmuir, and Freundlich models. The pseudo-second order and Langmuir models best described the adsorption kinetics and isotherms, implying strong chemisorption via the formation of coordinative bonds between the amine groups and metal ions. The Langmuir equation revealed the highest adsorption capacity of 0.7 mmol/g for the amine-functionalized MgFe2O4 nano-adsorbents. The adsorption capacity of the nanoadsorbent also changed with the calcination temperature. The MgFe2O4 sample, calcined at 500 °C, removed the most of the Pb2+ (73%), Cu2+ (59%), and Zn2+ (62%) ions from the water.

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection.

Stress has become a dangerous health problem in our life, especially in student education journey. Accordingly, previous methods have been conducted to detect mental stress based on biological and biochemical effects. Moreover, hormones, physiological effects, and skin temperature have been extensively used for stress detection. However, based on the recent literature, biological, biochemical, and physiological-based methods have shown inconsistent findings, which are initiated due to hormones' instability. Therefore, it is crucial to study stress using different mechanisms such as Electroencephalogram (EEG) signals. In this research study, the frontal lobes EEG spectrum analysis is applied to detect mental stress. Initially, we apply a Fast Fourier Transform (FFT) as a feature extraction stage to measure all bands' power density for the frontal lobe. After that, we used two type of classifications such as subject wise and mix (mental stress vs. control) using Support Vector Machine (SVM) and Naive Bayes (NB) machine learning classifiers. Our obtained results of the average subject wise classification showed that the proposed technique has better accuracy (98.21%). Moreover, this technique has low complexity, high accuracy, simple and easy to use, no over fitting, and it could be used as a real-time and continuous monitoring technique for medical applications.

Synthesis and Characterization of Manganese-Modified Black TiO2 Nanoparticles and Their Performance Evaluation for the Photodegradation of Phenolic Compounds from Wastewater.

The release of phenolic-contaminated treated palm oil mill effluent (TPOME) poses a severe threat to human and environmental health. In this work, manganese-modified black TiO2 (Mn-B-TiO2) was produced for the photodegradation of high concentrations of total phenolic compounds from TPOME. A modified glycerol-assisted technique was used to synthesize visible-light-sensitive black TiO2 nanoparticles (NPs), which were then calcined at 300 °C for 60 min for conversion to anatase crystalline phase. The black TiO2 was further modified with manganese by utilizing a wet impregnation technique. Visible light absorption, charge carrier separation, and electron-hole pair recombination suppression were all improved when the band structure of TiO2 was tuned by producing Ti3+ defect states. As a result of the enhanced optical and electrical characteristics of black TiO2 NPs, phenolic compounds were removed from TPOME at a rate of 48.17%, which is 2.6 times higher than P25 (18%). When Mn was added to black TiO2 NPs, the Ti ion in the TiO2 lattice was replaced by Mn, causing a large redshift of the optical absorption edges and enhanced photodegradation of phenolic compounds from TPOME. The photodegradation efficiency of phenolic compounds by Mn-B-TiO2 improved to 60.12% from 48.17% at 0.3 wt% Mn doping concentration. The removal efficiency of phenolic compounds from TPOME diminished when Mn doping exceeded the optimum threshold (0.3 wt%). According to the findings, Mn-modified black TiO2 NPs are the most effective, as they combine the advantages of both black TiO2 and Mn doping.

A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings.

The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method.