Ultrasonic-assisted oxidative desulfurization (UAOD) is utilized to lessen environmental problems due to sulfur emissions. The process uses immiscible polar solvents and ultrasonic waves to enhance desulfurization efficiency. Prior research focused on comparing the effectiveness of UAOD for gasoline using response surface methodology. This study evaluates the desulfurization efficiency and operating costs, including ultrasonic power, irradiation time, and oxidant amount to determine optimal conditions. The study used a multi-objective fuzzy optimization (MOFO) approach to evaluate the economic viability of UAOD for gasoline. It identified upper and lower boundaries and then optimized the desulfurization efficiency and operating costs while considering uncertainty errors. The fuzzy model employed max-min aggregation to optimize the degree of satisfaction on a scale from 0 (unsatisfied) to 1 (satisfied). Optimal conditions for gasoline UAOD were found at 445.43 W ultrasonic power, 4.74 min irradiation time, and 6.73 mL oxidant, resulting in a 66.79 % satisfaction level. This yielded a 78.64 % desulfurization efficiency (YA) at an operating cost of 13.49 USD/L. Compared to existing literature, gasoline desulfurization was less efficient and less costly. The solutions provided by MOFO demonstrate not only economic viability through decreased overall operating costs and simplified process conditions, but also offer valuable insights for optimizing prospective future industrial-scale UAOD processes.

Nanoparticles, owing to their unique physicochemical properties, have garnered significant attention in various scientific disciplines, including materials science, chemistry, biology, and environmental engineering. In recent years, the synthesis of metal oxide nanoparticles, such as NiO, Fe2O3, ZnO, SnO2, and CuO via green routes, has gained attraction due to their diverse applications in fields ranging from catalysis and electronics to medicine and environmental remediation. This study focuses on the green synthesis of copper oxide (CuO) and zinc oxide (ZnO) nanoparticles using Calotropis gigantea (Apple of Sodom) leaf extract as a reducing agent and stabilizer, with zinc nitrate (ZnNO3.6H2O) and copper nitrate (CuNO3.3H2O) as precursors. The hexagonal phase of ZnO and monoclinic plan structure of CuO with high crystallinity was confirmed by XRD and elemental composition by EDX analysis. With the help of an SEM image, particle size measured for CuO and ZnO using ImageJ software was found to be 56.08 nm and 46.49 nm, respectively. This study investigates the efficacy of nanoparticles in wastewater treatment, particularly focusing on methylene blue dye decolorization using the statistical processing of response surface methodology (RSM) using the Box-Behnken method. Additionally, it explores the impact of synthesized nanoparticles on seed growth enhancement, using Vigna radiata (green gram) seeds immersed in various doses of nanoparticles (0, 0.5, 1, 1.5, 2 mg/30 mL). Furthermore, the antibacterial activity of the nanoparticles against both gram-positive and gram-negative bacteria is evaluated. The results confirm the effectiveness of the materials for methylene blue dye removal, achieving 80.53% with CuO and 78.25% with ZnO. Significant seed growth was observed with a low nanoparticle dosage of 1.5 mg/30 mL, resulting in the highest seedling vigour index and germination percentage. This reduces the need for fertilizers and lessens environmental impact.

The dyeing process of textile materials is inherently intricate, influenced by a myriad of factors, including dye concentration, dyeing time, pH level, temperature, type of dye, fiber composition, mechanical agitation, salt concentration, mordants, fixatives, water quality, dyeing method, and pre-treatment processes. The intricacy of achieving optimal settings during dyeing poses a significant challenge. In response, this study introduces a novel algorithmic approach that integrates response surface methodology (RSM), artificial neural network (ANN), and genetic algorithm (GA) techniques for the precise fine-tuning of concentration, time, pH, and temperature. The primary focus is on quantifying color strength, represented as K/S, as the response variable in the dyeing process of polyamide 6 and woolen fabric, utilizing plum-tree leaves as a sustainable dye source. Results indicate that ANN (R2 ~ 1) performs much better than RSM (R2 > 0.92). The optimization results, employing ANN-GA integration, indicate that a concentration of 100 wt.%, time of 86.06 min, pH level of 8.28, and a temperature of 100 °C yield a K/S value of 10.21 for polyamide 6 fabric. Similarly, a concentration of 55.85 wt.%, time of 120 min, pH level of 5, and temperature of 100 °C yield a K/S value of 7.65 for woolen fabric. This proposed methodology not only paves the way for sustainable textile dyeing but also facilitates the optimization of diverse dyeing processes for textile materials.

In this study, RM (red mud) was acidified with sulfuric acid, and the acidified ARM (acidified red mud) was utilized as an innovative adsorption material for treating antibiotic-containing wastewater. The adsorption conditions, kinetics, isotherms, thermodynamics, and mechanism of ARM for CIP (ciprofloxacin) were investigated. The characterization of the ARM involved techniques such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), Brunauer-Emmett-Teller (BET), X-ray diffraction (XRD), X-ray fluorescence (XRF), thermogravimetric analysis (TGA), and NH3-TPD analysis. Adsorption studies employed a response surface methodology (RSM) for the experimental design. The results showed that ARM can absorb CIP effectively. The RSM optimal experiment indicated that the most significant model terms influencing adsorption capacity were solution pH, CIP initial concentration, and ARM dosage, under which the predicted maximum adsorption capacity achieved 7.30 mg/g. The adsorption kinetics adhered to a pseudo-second-order model, while equilibrium data fitted the Langmuir-Freundlich isotherm, yielding maximum capacity values of 7.35 mg/g. The adsorption process occurred spontaneously and absorbed heat, evidenced by ΔGθ values between -83.05 and -91.50 kJ/mol, ΔSθ at 281.6 J/mol/K, and ΔHθ at 0.86 kJ/mol. Analysis using attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) indicated a complex reaction between the Al-O in the ARM and the ester group -COO in CIP. The C=O bond in CIP was likely to undergo a slight electrostatic interaction or be bound to the internal spherical surface of the ARM. The findings indicate that ARM is a promising and efficient adsorbent for CIP removal from wastewater.

ZnO nanorod nonwoven fabrics (ZNRN) were developed through hydrothermal synthesis to facilitate the prevention of the transmission of respiratory pathogens. The superhydrophobicity and antibacterial properties of ZNRN were improved through the response surface methodology. The synthesized material exhibited significant water repellency, indicated by a water contact angle of 163.9°, and thus demonstrated antibacterial rates of 91.8% for Escherichia coli (E. coli) and 79.75% for Staphylococcus aureus (S. aureus). This indicated that E. coli with thinner peptidoglycan may be more easily killed than S. aureus. This study identified significant effects of synthesis conditions on the antibacterial effectiveness, with comprehensive multivariate analyses elucidating the underlying correlations. In addition, the ZnO nanorod structure of ZNRN was characterized through SEM and XRD analyses. It endows the properties of superhydrophobicity (thus preventing bacteria from adhering to the ZNRN surface) and antibacterial capacity (thus damaging cells through the puncturing of these nanorods). Consequently, the alignment of two such features is desired to help support the development of personal protective equipment, which assists in avoiding the spread of respiratory infections.

Extensive research on medicinal herbs for bioactive compounds proposes that they could replace synthetic drugs, reducing side effects and economic burdens. Especially, interest in the synergistic benefits of natural products is increasing, implying that their combined use may enhance therapeutic effectiveness. This study aimed to explore the synergetic effects of turmeric (Curcuma longa L.) and black pepper (Piper nigrum L.) extract on lung normal (MRC-5) and cancer (A549 and NCI-H292) cell lines. The turmeric extract (TM) only affected the lung cancer cell lines, but it had no impact on the MRC-5 cell line. On the other hand, the black pepper extract (BP) did not cause any damage to either the lung normal or cancer cell lines, even at concentrations of up to 400 µg/mL. Response surface methodology was used to predict the ideal synergistic concentrations (EC50) of TM and BP, which were found to be 48.5 and 241.7 µg/mL, respectively. Notably, the selected condition resulted in higher cytotoxicity compared to the exposure to TM alone, indicating a potent synergetic effect. The rate of curcumin degradation under this combined treatment was significantly decreased to 49.72 ± 5.00 nmol/h/µg for A549 cells and 47.53 ± 4.78 nmol/h/µg for NCI-H292 cells, respectively, as compared to curcumin alone. Taken together, this study confirmed the potent synergistic effect of TM and BP on lung cancer cell lines. Further research is required to identify their specific synergetic mechanisms. Our findings provide crucial foundational data on the synergistic effects of TM and BP.

In this study, ethylene diamine-coated reduced graphene oxide-supported silver composite (Ag/rGO-ED) was synthesized and used as an efficient catalyst for the decolorization of methylene blue (MB) in the presence of NaBH4. The morphology of the obtained material was elucidated using field emission scanning electron microscopy (FE-SEM), Fourier-transform infrared spectroscopy (FTIR), energy-dispersive X-ray analysis (EDX), transmission electron microscopy (TEM), and X-ray diffraction (XRD) techniques. The influences of four parameters (MB concentration (mg/L), NaBH4 amount (mM), catalyst amount (g/L), and contact time (s)) on the decolorization process were appraised and optimized via response surface methodology (RSM). For the decolorization of MB, the optimum solutions were obtained as Co of 32.49 mg/L, NaBH4 amount of 152.89 mM, catalyst amount of 0.83 g/L, and 101.39 s contact time with MB decolorization efficiency of 97.73%. MB, a pollutant in wastewater, was decolorized rapidly by Ag/rGO-ED with an efficiency of approximately 97%. The exploration of kinetics and thermodynamics was another major emphasis of the work. The activation energy (Ea) and rate constant (k) for the decolorization of MB were obtained as 37.9 kJ/mol and 0.0135 s-1, respectively. The obtained results show that the catalyst, a new composite material in the literature, is promising for decolorization of wastewater.

Burning fossil fuels causes toxic gas emissions to increase, therefore, scientists are trying to find alternative green fuels. One of the important alternative fuels is biodiesel. However, using eco-friendly primary materials is a main factor. Sustainable catalysts should have high performance, good activity, easy separation from reaction cells, and regenerability. In this study, to solve the mentioned problem NaOH@Graphene oxide-Fe3O4 as a magnetic catalyst was used for the first time to generate biodiesel from waste cooking oil. The crystal structure, functional groups, surface area and morphology of catalyst were studied by XRD, FTIR, BET, and FESEM techniques. The response surface methodology based central composite design (RSM-CCD) was used for biodiesel production via ultrasonic technique. The maximum biodiesel yield was 95.88% in the following operation: 10.52:1 molar ratio of methanol to oil, a catalyst weight of 3.76 wt%, a voltage of 49.58 kHz, and a time of 33.29 min. The physiochemical characterization of biodiesel was based to ASTM standard. The magnetic catalyst was high standstill to free fatty acid due to the five cycle\'s regeneration. The kinetic study results possess good agreement with first-order kinetics as well as the activation energy and Arrhenius constant are 49.2 kJ/min and 16.47 * 1010 min-1, respectively.

Lead is used in many industries such as refining, mining, battery manufacturing, smelting. Releases of lead from these industries is one of the major public health concerns due to widespread persistence in the environment and its resulting poisoning character. In this work, the castor seed shell (CSS) waste was exploited for preparing a beneficial bio-adsorbent for removal of Pb(II) ions from water. The raw CSS was modified with H3PO4 at different acid concentrations, impregnation ratios, activation times, and temperatures. An optimum adsorption capacity was observed for CSS modified with 2 M acid, 5 mL g-1 solid to liquid ratio, treated at 95 °C for 160 min. Exploiting acid modification, the SEM, XRD, and FTIR analyses show some alterations in functional groups and the surface morphology of the biomass. The impacts of physiochemical variables (initial lead ions concentration, pH, adsorbent dose and adsorption time) on the lead removal percentage were investigated, using response surface methodology (RSM). Maximum removal of 72.26% for raw CSS and 97.62% for modified CSS were obtained at an initial lead concentration (50 mg L-1), pH (5.7), adsorption time (123 min) and adsorbent dosage (1.1 g/100 mL). Isothermal and kinetics models were fitted to adsorption equilibrium data and kinetics data for the modified CSS and the adsorption system was evaluated thermodynamically and from the energy point of view. Isothermal scrutinization indicated the mono-layer nature of adsorption, and the kinetics experimental outcomes best fitted with the pseudo-second-order, implying that the interaction of lead ions and hot acid-treated CSS was the rate-controlling phenomenon of process. Overall, results illustrated that the hot acid-treated biomass-based adsorbent can be considered as an alternative bio-adsorbent for removing lead from water media.

The agricultural sector uses 70% of the world\'s freshwater. As clean water is extracted, groundwater quality decreases, making it difficult to grow crops. Brackish water desalination is a promising solution for agricultural areas, but the cost is a barrier to adoption. This study investigated the performance of the fertilizer drawn forward osmosis (FDFO) process for brackish water desalination using response surface methodology (RSM) and artificial neural network (ANN) approaches. The RSM model was used to identify the optimal operating conditions, and the ANN model was used to predict the water flux (Jw) and reverse solute flux (Js). Both models achieved high accuracy, with RSM excelling in predicting Js (R2 = 0.9614) and ANN performing better for Jw (R2 = 0.9801). Draw solution (DS) concentration emerged as the most critical factor for both models, having a relative importance of 100% for two outputs. The optimal operating conditions identified by RSM were a DS concentration of 22 mol L-1, and identical feed solution (FS) and DS velocities of 8.1 cm s-1. This configuration yielded a high Jw of 4.386 LMH and a low Js of 0.392 gMH. Furthermore, the study evaluated the applicability of FDFO for real brackish groundwater. The results confirm FDFO\'s potential as a viable technology for water recovery in agriculture. The standalone FO system proves to be less energy-intensive than other desalination technologies. However, FO exhibits a low recovery rate, which may necessitate further dilution for fertigation purposes.