Thermodynamic and structural investigation of oily wastewater treatment using peach kernel and walnut shell based activated carbon.

Despite the many articles about activated carbon with different precursors in adsorption process, no in-depth research has been carried out to understand the causes of the difference in surface adsorption characteristics of activated carbon with different precursors and different activation processes. In this work, the ability of two active carbon adsorbents made of walnut shell and peach kernel by two chemical and physical methods (totally 4 different types of activated carbon) in treatment of oily wastewater including diesel, gasoline, used oil or engine lubricant has been compared. The results show that the chemical activated peach carbon active with 97% hardness has provided the highest hardness and physical activated walnut carbon active has obtained the lowest hardness value (87%).

Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system's performance by artificial neural network.

The alarming pace of environmental degradation necessitates the treatment of wastewater from the oil industry in order to ensure the long-term sustainability of human civilization. Electrocoagulation has emerged as a promising method for optimizing the removal of chemical oxygen demand (COD) from wastewater obtained from oil refineries. Therefore, in this study, electrocoagulation was experimentally investigated, and a single-factorial approach was employed to identify the optimal conditions, taking into account various parameters such as current density, pH, COD concentration, electrode surface area, and NaCl concentration.

UV and solar-based photocatalytic degradation of organic pollutants from ceramics industrial wastewater by Fe-doped ZnS nanoparticles.

UV and solar-based photocatalytic degradation of 2,4-dichlorophenol (2,4-DCP) as an organic contaminant in ceramics industry wastewater by ZnS and Fe-doped ZnS NPs was the focus of this research. Nanoparticles were prepared using a chemical precipitation process. The cubic, closed-packed structure of undoped ZnS and Fe-doped ZnS NPs was formed in spherical clusters, according to XRD and SEM investigations.

Solar light driven enhanced photocatalytic treatment of azo dye contaminated water based on Co-doped ZnO/ g-CN nanocomposite.

The current research concentrated on the Co-precipitation synthesis of g-CN (CN), ZnO, ZnO/CN, and Co-doped ZnO/CN nanocomposite, as well as the solar light enhanced photocatalytic treatment of Reactive Red 120 (RR120) from genuine wool textile effluent. The 3D flower-like structure of Co-doped ZnO distributed on the surface of CN thin sheets, according to structural studies employing XRD and SEM examinations Electrochemical experiments exhibited that the Co-doped ZnO/CN nanocomposite has a large electroactive surface area. The optical band-gap values of CN, ZnO, ZnO/CN, and Co-doped ZnO/CN nanocomposites were 2.

Mechanisms of Action of Noninvasive Brain Stimulation with Weak Non-Constant Current Stimulation Approaches.

Non-constant current stimulation (NCCS) is a neuromodulatory method in which weak alternating, pulsed or random currents are delivered to the human head via scalp or earlobe electrodes. This approach is widely used in basic and translational studies. However, the underlying mechanisms of NCCS, which lead to biological and behavioral effects in the brain, remain largely unknown.

Thermodynamics Investigation and Artificial Neural Network Prediction of Energy, Exergy, and Hydrogen Production from a Solar Thermochemical Plant Using a Polymer Membrane Electrolyzer.

Hydrogen production using polymer membrane electrolyzers is an effective and valuable way of generating an environmentally friendly energy source. Hydrogen and oxygen generated by electrolyzers can power drone fuel cells. The thermodynamic analysis of polymer membrane electrolyzers to identify key losses and optimize their performance is fundamental and necessary.

Designing Effective Multi-Target Drugs and Identifying Biomarkers in Recurrent Pregnancy Loss (RPL) Using In Vivo, In Vitro, and In Silico Approaches.

Recurrent pregnancy loss (RPL) occurs in approximately 5% of women. Despite an abundance of evidence, the molecular mechanism of RPL's pathology remains unclear. Here, we report the protective role of polo-like kinase 1 (PLK1) during RPL.

Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing.

Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study.