In this article, pure and manganese (Mn)-substituted molybdenum disulfide (Mo_1-xMn_xS₂) nanostructures were synthesized using the hydrothermal method for photocatalytic activity. A number of characterization techniques were employed to examine the impact of Mn incorporation on the morphological, structural, optical, and photocatalytic characteristics of MoS₂. The samples were analyzed using field emission scanning electron microscopy, which revealed an aggregated nanoflower-like structure in the recorded images. X-ray diffraction technique was performed to examine the crystal structure of pure and Mn-doped MoS₂ samples, and the results confirmed the formation of the 2H-MoS₂ polytype in all the as-prepared nanostructures, with a reduction in the average crystallite size by increasing the dopant concentration. Edge-terminated active sites in the Mn-doped samples were indicated by Raman analysis. Also, further investigation of the structure was carried out using Fourier transform infrared spectroscopy, confirming the presence of the characteristic Mo-S band. The elemental composition of pure and Mn-doped MoS₂ nanoflowers (NFs) was verified by energy-dispersive X-ray spectroscopy. Brunaure-Emmett-Teller surface analysis was performed to find out the impact of Mn doping on the surface area. The optical properties were observed through ultraviolet–visible spectroscopy, demonstrating allowed direct transitions with an optical energy bandgap that gradually decreased with increasing Mn concentration. The photocatalytic performance of undoped and Mn-doped MoS₂ NFs was evaluated through the degradation of methylene blue (MB) dye under visible-light irradiation. This study demonstrates that high photocatalytic efficiency (96% MB degradation in 60 min under visible light) is achieved at a low Mn concentration (1.5%), highlighting that controlled Mn incorporation enhances charge separation without excessive doping.

This study proposes a novel bio-inspired meta-heuristic algorithm, the Python Snake Optimization Algorithm (PySOA), that mimics the hunting behavior of the python snake. These reptiles are not poisonous, but they hunt their prey through ambushes. They can detect their prey using senses such as smell, eyesight, and infrared vision. The hunting mechanism consists of three major phases: searching for prey, scanning for prey, and attacking the prey. The searching-for-prey step contributes to exploration, while attacking prey is dedicated to exploitation, and scanning for attack enhances the balance between the two. The mathematical model of the method improves convergence precision and global search capability by capturing the behavioral dynamics of Pythons. PySOA’s performance was assessed on 23 classical benchmark func￾tions, 29 CEC-2017 benchmark functions, 10 CEC-2019 composite functions, and three real-world engineering problems. The outcomes were confirmed by 14 popular meta-heuristic algorithms (MAs). With an average improvement of 39.2%, the PySOA outperformed the compared algorithms across all 62 test functions, achieving the best mean fitness rank in 43% of test cases. By successfully balancing exploration and exploitation, these findings demonstrate that PySOA is both resilient and competitive in addressing unimodal and multimodal optimization problems. The composite CEC-2019 test fitness functions demonstrated PySOA’s ability to explore and exploit simultaneously. The outcomes of the CEC-2017 benchmark tests show that PySOA has a shortcoming in local search exploration. Based on PySOA’s performance on real-world engineering problems, it is a practical algorithm for achieving optimal results and can be applied to real-world problems. The source code of the PySOA is publicly available at https://www.mathworks.com/matlabcentral/​f​​i​leexchange
/175654-a-novel-bio-inspired-python-snake-optimization-algorithm.

This work introduced three different versions of a complex model called the chaotic hidden
attractor jerk model. These versions were categorized as commensurate, non-commensurate, and
distributed-order models. They can be applied in various practical fields like physics and image
encryption. We explored the characteristics of these models such as fixed points, symmetry, and
dissipation. These models exhibit chaotic behaviors, as evidenced by the Lyapunov exponent (LLE)
and bifurcation diagram. We introduced a novel combination synchronization (CS) between these
models using a tracking control method. We established a theorem for achieving synchronization
among these different models. We presented numerical computations to validate the analytical results.
Our research primarily focused on the encryption and decryption processes of grayscale images by
the proposed synchronization method. We evaluated the efficacy of image encryption and decryption
through various metrics like information entropy and histograms to ensure the accuracy and security
of the process

AbstractThis study aimed to enhance the production of hydrogen (H2) from anaerobic digestion of banana peels (BP) using the synergistic interaction between the ruminant bacterial strains. Seven ruminant bacterial consortia were designed from sixteen hydrogen producing ruminant bacterial strains. MOST2 (Pediococcus acidilactici, Streptococcus lutetiensis and Bacillus cereus) was the most highly producer giving 398.00 ± 21.67 ml/L on 25% banana peels. The used optimization strategy in this study resulted in maximum H2 (HMax) of 1446.67 ± 60.64 ml/L with a significant improvement of 363.48%. The maximum hydrogen production rate (Rmax) and lag time (λ) were 47.52 ml/L/h, 2.91 h by MLM, respectively, and 35.99 ml/L/h and 5.05 h by MGM, respectively. GC–MS analysis of the fermentation effluent indicated the presence of butyric acid (3.58%), acetic acid (1.81%), and agaric acid (1.70%). The study reveals the feasibility of utilizing banana peel waste as a sustainable feedstock for hydrogen production by the ruminant bacterial consortia.

One of the most important concerns now governing international attention is energy generation. In this work, Ce(NDC)MOF@Bentonite nanocomposite was synthesized and used as a novel and effective catalyst for the green synthesis of hydrogen. The synthesized composite was characterized using X-ray diffraction (XRD), thermogravimetric analysis (TGA), N2-adsorption, Fourier- transform infrared (FT-IR), and scanning electron microscope (SEM) techniques. On the view of the characterized data, a pure and distinct crystalline phase with a highly specific surface area (SBET) of 58.5 m2/g was formed. Moreover, the morphology of the nanocomposite was monitored through scanning (SEM). SEM image which revealed a highly porous surface texture with some large particles with highly spherical morphology. The catalytic performance of the fabricated catalysts was propped via the hydrolysis of sodium borohydride. The effect of NaBH4 concentration, weight of the catalyst, and the reaction temperature on NaBH4 hydrolysis was investigated. The hydrogen generation rate HGR of 116.02 mL min−1g− 1 at 30 °C was achieved using 16 mg of the catalyst and 0.05 M NaBH4. Kinetic and thermodynamic functions including activation energy, enthalpy, entropy, and free energy changed were also estimated. This work suggests that such novel and highly active catalysts hold strong potential as advanced materials for hydrogen energy applications.