Immobilization of Magnetic Nanoparticles on Cellulosic Wooden Sawdust for Competitive Nudrin Elimination from Environmental Waters as a Green Strategy: Box-Behnken Design Optimization.

The role of engineering in our society is not to just to continue creating chemicals, but sharing the responsibility for environmentally sound appropriate design of substances for a circular economy. Concerning this contemporary strategy, waste wooden sawdust (WSD) as a biobased by-product is augmented with magnetite (Mag) nanoparticles to meet the concept of cyclic application of resources in environmentally relevant photocatalytic reactions. The physical properties of the prepared WSD:Mag material were characterized to emphasis their structure and morphology by using X-ray diffraction (XRD) and transmission electron microscopy (TEM), then the prepared catalyst was applied in augmentation with hydrogen peroxide as a type of photocatalyst in the form of Fenton's reaction system to oxidize Nudrin pesticide in queues media. Twinned WSD:Mag has been verified to exhibit higher performance than pristine single-phase catalysts. System parameters, i.e., pH, hydrogen peroxide, catalyst dozing, and temperature, were studied to check their effect on the reaction activity. In the present study, further promotion of photocatalytic activity of twinned WSD:Mag was obtained by optimizing the process parameters at the optimal reaction time of 30 min. The optimal results investigated via Box-Behnken design regression model based on response surface mythology (RSM) showed that the photocatalytic activity of the twinned catalyst could reach 94% at pH 2.5 and 386 and 38 m/L of H2O2 and WSD:Mag, respectively. The regression coefficient and probability obtained from analysis of variance (ANOVA) were used to check the adequacy of the applied model, and were 92% and 0.02, respectively. Additional confirmatory tests were carried out under optimum conditions for verification and agreed with the predicted values. Experimental data analysis revealed that the reaction is well fitted with the second-order reaction model. Thermodynamic parameters highlighted the oxidation reaction is non-spontaneous at high temperature and exothermic in nature and proceeds at a low activation energy barrier (31.46 kJ/mol). Catalyst recyclability was also checked, which confirmed catalyst sustainability and high removal rates (78%) after six cycles of use. This work introduces a new concept to design a promising environmentally benign photocatalyst with high potential for applicability to environmental remediation of agricultural effluents with a view to a circular economy.

Adsorptive Pattern Using Drinking Water Treatment Residual for Organic Effluent Abatement from Aqueous Solutions.

Zeolite (ZSM-12) is a unique material obtained from the drinking water treatment plants' residual "alum sludge", as a result of using aluminum sulphate as a primary coagulant in the plants. Herein, alum sludge (AS) is initially dewatered and subjected for various calcination temperatures 400 °C, 600 °C and 800 °C and the corresponding materials are named as AS400, AS600 and AS800, respectively. Such calcination is provided to attain ZSM-12, which is considered a highly adsorptive material. The material characterization and morphology were investigated using scanning X-ray diffraction (XRD) and electron microscope (SEM) that confirm the presence of ZSM-12 and porosity of such prepared materials. Thereafter, such materials are introduced for phenol remediation from aqueous solution. The experimental data reveal that AS400 had the largest adsorption capacity (275 mg-phenol/g), in comparison to the commercial adsorbent materials during 2 h of isotherm time. Such a result confirms the suitability of alum sludge residue to be a good candidate for environmental remediation. Furthermore, adsorption isotherm models were applied, and the data are well-fitted to the Langmuir isotherm model. In addition, thermodynamic parameters are investigated which verify the physisorption adsorption process and exothermic nature with a spontaneous reaction system.

A Predictive and Preventive Model for Onset of Alzheimer's Disease.

Alzheimer's Disease (AD) is a neurodegenerative irreversible brain disorder that gradually wipes out the memory, thinking skills and eventually the ability to carry out day-to-day tasks. The amount of AD patients is rapidly increasing due to several lifestyle changes that affect biological functions. Detection of AD at its early stages helps in the treatment of patients. In this paper, a predictive and preventive model that uses biomarkers such as the amyloid-beta protein is proposed to detect, predict, and prevent AD onset. A Convolution Neural Network (CNN) based model is developed to predict AD at its early stages. The results obtained proved that the proposed model outperforms the traditional Machine Learning (ML) algorithms such as Logistic Regression, Support Vector Machine, Decision Tree Classifier, and K Nearest Neighbor algorithms.