Enhanced biosynthesis of coated silver nanoparticles using isolated bacteria from heavy metal soils and their photothermal-based antibacterial activity: integrating Response Surface Methodology (RSM) Hybrid Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies.

This study explores the biosynthesis of silver nanoparticles (AgNPs) using the Streptomyces tuirus S16 strain, presenting an eco-friendly alternative to mitigate the environmental and health risks of chemical synthesis methods. It focuses on optimizing medium culture conditions, understanding their physicochemical properties, and investigating their potential photothermal-based antibacterial application. The S16 strain was selected from soils contaminated with heavy metals to exploit its ability to produce diverse bioactive compounds. By employing the combination of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies, we optimized AgNPs synthesis, achieving an improvement of nearly 2.45 times the initial yield under specific conditions (Bennet's medium supplemented with glycerol [5 g/L] and casamino-acid [3 g/L] at 30 °C for 72 h). A detailed physicochemical characterization was conducted. Notably, the AgNPs were well dispersed, and a carbonaceous coating layer on their surface was confirmed using energy-dispersive X-ray spectroscopy. Furthermore, functional groups were identified using Fourier-transform infrared spectroscopy, which helped enhance the AgNPs' stability and biocompatibility. AgNPs also demonstrated efficient photothermal conversion under light irradiation (0.2 W/cm2), with temperatures increasing to 41.7 °C, after 30 min. In addition, treatment with light irradiation of E. coli K-12 model effectively reduced the concentration of AgNPs from 105 to 52.5 µg/mL, thereby enhancing the efficacy of silver nanoparticles in contact with the E. coli K-12.

Biosynthesis, characterization, and evaluation of antibacterial and photocatalytic methylene blue dye degradation activities of silver nanoparticles from Streptomyces tuirus strain.

Silver nanoparticles (AgNPs) are a promising technology for the design of antimicrobial agents against drug-resistant pathogens. It could also be used for the photocatalytic degradation of dyes used in industries such as methylene blue (MB). In this study, 17 different actinomycetal strains isolated from hydrocarbon-contaminated soils collected from an oil distribution company in Algeria were evaluated for their ability to produce NPs. After a selection process, S16 was the main strain capable of synthesizing AgNPs extracellularly. The strain S16 was determined using molecular identification based on the sequencing of the 16S rDNA gene. Among various techniques used for the synthesis of AgNPS, a technique using a temperature of 30 °C, pH of 7, a metal salt concentration of 1 mM, and a period of 72 h in the dark were found to be more effective in the biosynthesis of the AgNPs. The biosynthesized AgNPs that were analyzed by UV-visible spectroscopy resulted in a specific peak at a wavelength of (λ = 400 nm). The DRX analyses showed characteristic peaks of the AgNPs at (1 1 1), (2 0 0), (2 2 2), and (3 1 1), which validated the presence and crystalline nature of the biosynthesized NPs. Zetasizer analysis showed an average size and zeta potential of 64 nm (-32.3 mV), while the SEM-EDS analysis confirmed the spherical shape of AgNPs and the presence of Ag atoms in the elemental composition. The biosynthesized AgNPs indicated adequate antibacterial activity against 5 out of the 6 strains tested in this study, using minimum inhibitory concentration (MIC) that ranged from 217.18 µg/mL to 1137.5 µg/mL. The AgNPs were combined with commercial antibiotics and the synergistic effect of the combination was also assessed against MRSA which resulted in increased antibacterial activity of AgNPs in the presence of the strain S16. Furthermore, the photocatalytic degradation of the methylene blue (MB) was evaluated under sunlight and UV irradiations using biosynthesized AgNPs. The AgNPs showed photocatalytic decolorization potential of 71.3% for MB dye (20 ppm) under sunlight irradiation within 6 h of incubation, while only 11.25% of the MB dye degraded using UV irradiation.

Valorization and optimization of agro-industrial orange waste for the production of enzyme by halophilic Streptomyces sp.

This study underlines the biotechnical valorization of the accumulated and unusable remains of agro-industrial orange fruit peel waste to produce α-amylase under submerged conditions by Streptomyces sp. KP314280 (20r). The response surface methodology based on central composite design (RSM-CCD) and artificial neural network coupled with a genetic algorithm (ANN-GA) were used to model and optimize the conditions for the α-amylase production. Four independent variables were evaluated for α-amylase activity including substrate concentration, inoculum size, sodium chloride powder (NaCl), and pH. A ten-fold cross-validation indicated that the ANN has a greater ability than the RSM to predict the α-amylase activity (R2ANN = 0.884 and R2RSM = 0.725). The analysis of variance indicated that the aforementioned four factors significantly affected the α-amylase activity. Additionally, the α-amylase production experiments were conducted according to the optimal conditions generated by the GA. The results indicated that the amylase yield increased by 4-fold. Moreover, the α-amylase production (12.19 U/mL) in the optimized medium was compatible with the predicted conditions outlined by the ANN-GA model (12.62 U/mL). As such, the ANN and GA combination is optimizable for α-amylase production and exhibits an accurate prediction which provides an alternative to other biological applications.

Photocatalytic-persulfate- oxidation for diclofenac removal from aqueous solutions: Modeling, optimization and biotoxicity test assessment.

In this paper, the influence of several aquatic factors (the nature of catalyst, the initial pH and the initial concentration of the pollutant) on the photocatalytic degradation of diclofenac (DFC), one of the most widely prescribed anti-inflammatory non-steroidal drug, was studied. Also, in order to examine the intensification process, the variation of the photocatalytic DFC degradation in the presence of sodium persulfate (PPS) was analyzed. It was found that, compared to titanium dioxide (TiO2), the zinc oxide (ZnO) photocatalyst performed exceptionally well, with a 96.13% DFC degradation efficiency after 150 min. The photodegradation of DFC by ZnO catalyst fitted well the Langmuir-Hinshelwood kinetic model. The maximum efficiency is 97.27% for simulated solar-UVA/ZnO/PPS and 77% for simulated solar-UVA/ZnO. In order to determine the optimal conditions leading to the maximization of DFC removal, an artificial neural network (ANN) modeling approach combined with genetic algorithm (GA) was applied. The best ANN determined had a correlation of 0.999 and it was further used in the process optimization where a 99.7% degradation efficiency was identified as the optimum under the following conditions: DFC initial concentration 37,9 mg L-1, pH 5,88 and PPS initial concentration 500 mg L-1. The effectiveness of the process and the toxicity of the pharmaceutical pollutants and their by-products were also evaluated and confirmed by the biological tests using liver and kidney of Mus musculus mice.