The polymer electrolyte system of methylcellulose (MC) doped with various sodium bromide (NaBr) salt concentrations is prepared in this study using the solution cast technique. FTIR and XRD were used to identify the structural changes in solid films. Sharp crystalline peaks appeared at the XRD pattern at 40 and 50 wt.% of NaBr salt. The electrical impedance spectroscopy (EIS) study illustrates that the loading of NaBr increases the electrolyte conductivity at room temperature. The DC conductivity of 6.71 × 10⁻⁶ S/cm is obtained for the highest conducting electrolyte. The EIS data are fitted with the electrical equivalent circuit (EEC) to determine the impedance parameters of each film. The EEC modeling helps determine the circuit elements, which is decisive from the engineering perspective. The DC conductivity tendency is further established by dielectric analysis. The EIS spectra analysis shows a decrease in bulk resistance, demonstrating free ion carriers and conductivity boost. The dielectric property and relaxation time confirmed the non-Debye behavior of the electrolyte system. An incomplete semicircle further confirms this behavior model in the Argand plot. The distribution of relaxation times is related to the presence of conducting ions in an amorphous structure. Dielectric properties are improved with the addition of NaBr salt. A high value of a dielectric constant is seen at the low frequency region.

Recirculating Aquaculture Systems (RAS) have emerged as a sustainable approach to intensive fish production. In our previous work, we demonstrated the efficacy of a Deep Deterministic Policy Gradient (DDPG) reinforcement learning approach for optimizing feeding rates and maintaining water quality in RAS. However, the “black box” nature of deep reinforcement learning algorithms often impedes their adoption by fish farmers who lack specialized knowledge in artificial intelligence. This study addresses this gap by developing and evaluating methodologies to enhance the interpretability and explainability of DDPG-based control systems specifically for aquaculture practitioners. We employ decision tree approximation techniques to transform complex neural network policies into transparent rule-based systems while preserving performance. Our approach achieves 92.7% policy fidelity with only a 5.8% performance drop compared to the original DDPG controller. The resulting decision rules were validated with fish farmers, with 87% reporting improved understanding of system recommendations and 78% expressing increased trust in the technology. Feature importance analysis revealed dissolved oxygen, ammonia levels, and biomass as the primary decision factors. This work demonstrates how advanced artificial intelligence techniques can be made accessible to end-users through interpretable approximations, potentially accelerating the adoption of intelligent control systems in aquaculture and contributing to more sustainable food production practices.

Anise oil was prepared in its nanoemulsion form to facilitate the penetration of microbial walls, causing microbe mortality. The penetration occurred easily owing to the reduction in its size (nm). Nanoemulsions with different concentrations of anise oil were prepared using lecithin as an emulsifying agent with the aid of an ultra-sonification process. Their morphological and chemical properties were then characterized. The promising constituents were l-Menthone (11.22%), Gurjunene (6.78%), Geranyl acetate (4.03%), Elemene (3.93%), Geranyl tiglate (3.53%), geraniol (3.48%), linalool (0.17%) as well as camphene (0.12%). Different concentrations of prepared anise oil in micro and nanoemulsions were tested as antimicrobial agents against Gram-positive bacteria (Staphylococcus aureus), Gram-negative bacteria (Escherichia coli), yeast (Candida albicans) and fungi (Asperigillus niger). The findings illustrated that the anise oil-based nanoemulsion exhibited better results. Different biochemical and biological evaluations of anise oil nanoemulsions were conducted, including determining killing times, antioxidant activities (using three different methods), and total phenolics. A trial to estimate the mode of action of anise oil-based nanoemulsion as an antimicrobial agent against S. aureus and C. albicans was performed via studying the release of reducing sugars and protein and conducting scanning electron microscopy.

Ferene is the most commonly used chromogenic agent for the determination of serum iron in blood. In this work we have successfully synthesized Ferene-S-conjugated silver nanoparticles (Ferene-S-AgNPs) for the first time characterized by UV-visible, Fourier-Transform Infrared Spectroscopy (FTIR), and Matrix-Assisted Laser Desorption/Ionization-Time Of Flight (MALDI-TOF) mass spectrometry techniques. Particle size of the synthesized nanoparticles was determined by atomic-force microscopy and scanning electron microscopy techniques with size ranges from 10–90 nm in diameter. Ferene-S-AgNPs were explored for their chemosensing potential with various metal ions such as Sb³⁺, Pb²⁺, Ca²⁺, Fe²⁺, K⁺, Co²⁺, Ba²⁺, V⁵⁺, Cu⁺, Cd²⁺, Hg²⁺, Ni²⁺, Cu²⁺, Fe³⁺, Mg²⁺, Mn²⁺, Al³⁺, and Cr³⁺. Ferene-S-AgNPs were found to show selective quenching effects and slight bathochromic shifts to the surface plasmon resonance absorption band after treatment with Fe²⁺. Furthermore, the developed chemosensor also exhibited substantial selectivity towards Fe²⁺ in the presence of other competitive ions. We observed that Ferene-S-AgNPs mimic the selectivity of the parent compound of Ferene towards Fe²⁺. The system obeyed Beer’s law over concentration ranges of 110–190 nM. The detection limit was found to be 110 nM.