Recent environmental assessments have highlighted the urgent need for efficient and sustainable technologies capable of degrading recalcitrant pharmaceutical pollutants in freshwater systems. Among photocatalytic materials, graphitic carbon nitride (g-C₃N₄) has garnered attention due to its visible light activity, chemical stability, and tunable properties. However, its performance is strongly influenced by the precursor material and synergistic modifications. This study explores the efficacy of g-C₃N₄ synthesized from melamine, urea, and thiourea—alongside biochar—as a composite system for the photodegradation of selected persistent pharmaceuticals.
The different sources of g-C₃N₄ were calcined at 550°C for two hours to obtain a slightly yellowish form of g-C₃N₄, which was then tested on methyl orange dye. Among the sources, urea demonstrated the highest degradability at 60.25%, followed by thiourea at 32.05% and melamine at 7.59%. To enhance the degradation efficiency, urea was blended with melamine and subsequently with thiourea, resulting in improved degradation attributed to the presence of urea. Further optimization involved blending g-C₃N₄ from these sources with biochar, enhancing both the adsorption capability and photodegradation activity, with urea maintaining a degradation rate of 59.7%. These preliminary investigations led to testing the urea-derived g-C₃N₄ with biochar on the recalcitrant pharmaceuticals, yielding degradation efficiencies of 92.59%, 84.44%, 68.11%, and 61.11% for tetracycline, cefixime, ciprofloxacin, and carbamazepine, respectively. These results indicate that the strategic enhancement of g-C₃N₄ with biochar offers promising potential for advanced water treatment applications targeting pharmaceutical contaminants.

The rapid expansion of electric vehicles and renewable energy systems has led to a surge in demand for lithium-ion batteries, raising urgent concerns over raw material scarcity, environmental impact, and end-of-life waste. Necessity of finding sustainable processes for the battery recycling, implementation of recovered materials in the battery loop and reuse of battery components in second-life applications are crucial topics in securing the energy sector for the coming years. This presentation explores the recovery of critical battery components from spent battery cells, with a focus on methods that enable their reuse in energy storage applications.

In our work, we evaluate hydrometallurgical approaches, such as leaching and electrochemical methods of Li-ion and alkaline batteries, to recover valuable resources for their further reuse in battery applications. Emphasis is placed on critical aspects in the recycling technology, such as scalable, low-impact recycling methods that preserve material integrity and performance. We explore the electrochemical behavior of the used batteries under varied conditions, with implementation of bio-based materials and advanced electrochemical techniques to investigate their reuse in novel batteries and secondary-life applications. By aligning material recovery with battery manufacturing needs, this work underscores the potential for a circular economy in battery technology, reducing dependence on natural resources and waste recovery while supporting sustainable energy goals.

Addressing water contamination with hazardous pollutants, particularly nitrites with strong carcinogenic potential, is an essential priority in environmental remediation efforts. This research explored the photoreduction of nitrate in drinking water, comparing plain ZnO nanoparticles (ZnO NPs) with Pd-Au-decorated ZnO NPs. In the first step, ZnO NPs were synthesized using a simple and environmentally friendly precipitation method using black tea solid waste extract. In the second step, bimetallic nanoparticles were obtained using a green chemistry method from Au and Pd precursors (at a 1:1 molar ratio) in a tannic acid solution. The Pd-Au/ZnO photocatalytic material was obtained by dispersing bimetallic nanoparticles, previously dissolved through sonication in deionized water, onto ZnO. Both the ZnO and Pd-Au/ZnO NPs were characterized in terms of their structural crystallinity and morphology using XRD, SEM-EDX, and TEM. The photocatalytic efficiency of the synthesized ZnO and Pd-Au/ZnO NPs was evaluated according to their ability to degrade nitrates from water, in the absence and presence of formic acid as a reducing agent and/or a hole scavenger, under UV irradiation with a 125 W Hg lamp. Compared to the ZnO NPs, the catalysts loaded with bimetallic Pd-Au nanoparticles demonstrated excellent N₂ selectivity in the photocatalytic reduction of nitrate in the presence of formic acid. The maximum photocatalytic activity and N₂ selectivity were achieved using the Pd–Au/ZnO NPs, with a total metal loading of 1 wt%. A possible photocatalytic mechanism was proposed. Upon UV irradiation, zinc oxide (ZnO) generates electrons that are transferred to the bimetallic nanoparticles. Subsequently, protons (H+) adsorbed onto the surface of the metal particles facilitate the reduction of nitrate ions (NO₃⁻). These materials are expected to exhibit a high photocatalytic performance by enhancing the rate of photoexcited electron capture and reducing recombination effects, thanks to the electron storage capacity of the palladium–gold (Pd-Au) nanoparticles.

In the last few decades, investigations of low-cost, stable, highly active, and fast-synthesizing electrocatalysts for the oxygen evolution and reduction reaction (OER and ORR) have been the main focus of electrochemical examinations. These two reactions are key half-reactions in rechargeable metal–air batteries (MABs) and unitized regenerative fuel cells (URFCs), which are considered eco-friendly and promising energy storage technologies. URFCs can function in two modes: in electrolysis mode, water is split into hydrogen and oxygen via the hydrogen evolution reaction (HER) and OER; in fuel cell mode, hydrogen and oxygen are used to generate electricity and water through the hydrogen oxidation reaction (HOR) and ORR. Similarly, MABs rely on OER and ORR as the primary reactions occurring at the air cathode during charging and discharging cycles. Although noble metal-based catalysts such as iridium or ruthenium oxides (IrO₂/RuO₂) and platinum (Pt) are regarded as standard electrocatalysts for OER and ORR, their use is limited by sluggish kinetics when applied in the opposite reaction—Pt for OER and IrO₂/RuO₂ for ORR.

In this work, Co/rGO was synthesized via chemical synthesis, characterized by scanning electron microscopy, and tested for OER/ORR in alkaline media. The Co/rGO electrodes demonstrated comparable or superior performance to state-of-the-art OER/ORR catalysts, with enhanced stability and cost-effectiveness, highlighting their potential as practical alternatives in metal–air batteries and fuel cells.

Acknowledgments

The authors acknowledge the financial support from the Science Fund of the Republic of Serbia, grant number 250, High-performance NANosize Oxygen Electrodes: transition metals deposited ON reduced graphene oxide vs. high-entropy alloy alternatives-NANO-E-ON (Diaspora: Support for Visits of Diaspora Scientists programme).

The increasing global adoption of renewable energy technologies, including photovoltaics, wind power, tidal energy, and fuel cells, is primarily driven by the imperative to address pressing environmental and resource challenges. These include the finite nature of fossil fuels, particularly petroleum depletion, and the escalating threat of anthropogenic climate change. The inherent variability and intermittency of individual renewable energy sources, primarily driven by unpredictable meteorological conditions, present significant challenges to reliable energy supply. Consequently, the integration of an energy storage system is increasingly being favored to ensure consistent power delivery and minimize disruptions for end consumers.
This strategy effectively mitigates the limitations of any single renewable energy source, enhancing overall system resilience and reliability; however, the complex operational dynamics of microgrids present considerable challenges to achieving optimal performance and resource utilization.

In this paper, the application of fuzzy logic control as a strategy for optimizing energy management within DC microgrids including a photovoltaic array and battery storage system is discussed. Fuzzy logic control is particularly well suited for this application due to its flexibility, robustness when facing disturbances, and inherent adaptability to nonlinear and uncertain systems, a crucial advantage when addressing the significant variability and intermittency associated with renewable energy sources.
Simulations using MATLAB/Simulink are used to evaluate the performance and efficacy of the proposed fuzzy logic-based energy management system within DC microgrid environments. This study significantly contributes to the field by empirically demonstrating the viability of DC microgrids as robust and efficient energy solutions.

This work investigates the development of copper-based metal organic framework (CuMOF)–chitosan (CS)–hydroxyapatite (HAp) hybrid catalysts to enhance the photocatalytic degradation of ciprofloxacin (CIP) in contaminated water. The integration of these materials resulted in a hybrid catalyst with exceptional photocatalytic activity, showing promise for addressing antibiotic-induced water pollution. Characterization techniques like Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS), Fourier Transform Infrared Spectroscopy (FTIR), and X-ray Diffraction (XRD) confirmed that ultrasound-assisted synthesis improved the physicochemical properties of the catalyst, including higher surface area, better morphology, and greater structural stability. Photocatalytic tests demonstrated that both conventional and ultrasound-assisted catalysts effectively degraded CIP, with the latter achieving an 83.49% degradation rate at 1 gram catalyst loading after 120 min of exposure, particularly at lower initial CIP concentrations. Increasing the catalyst loading from 0.5 g to 1.0 g provided more active sites, accelerating pollutant breakdown. Key factors such as catalyst loading, pollutant concentration, and exposure time were identified as critical to the degradation process, with statistical analysis underscoring the importance of optimizing these variables for maximum efficiency. The study highlights the potential of CuMOF-CS-HAp hybrid catalysts in environmental remediation, particularly for tackling antibiotic pollution in aquatic environments. It also emphasizes the role of ultrasound-assisted synthesis in boosting photocatalytic performance, contributing to the development of advanced materials for water purification.

Agricultural biomass residues, such as palm kernel shells (PKSS), empty fruit bunches (EFBS), and palm kernel fibers, are produced in substantial quantities from oil palm processing, particularly in tropical regions like West Africa. These residues constitute a significant yet underutilized renewable resource for sustainable bioenergy production. This systematic review seeks to assess the intellectual structure of knowledge related to the bioenergy potential and thermochemical conversion of these residues. A structured literature search was conducted using the Scopus database, focusing on peer-reviewed studies published between 2020 and 2024, adhering to PRISMA guidelines. This study reports the proximate and ultimate analysis, calorific value, moisture and ash content, and combustion and pyrolysis.

VOS viewer software was utilized for bibliometric analysis to identify research trends and knowledge clusters. The mapping of keyword co-occurrences demonstrated strong thematic connections among "palm kernel shell," "pyrolysis," "heating value," and "bioenergy," thereby highlighting key research areas. The frequency of research currently attracting the most attention in this field is also presented to consolidate the current knowledge on oil palm biomass residues and propose an actionable model for optimizing their use in domestic and industrial thermal energy systems. It also highlights critical gaps and opportunities for further research in developing localized, scalable biofuel technologies to accelerate the clean energy transition in biomass-rich developing regions.

This research examines the effect of using electrocoagulation as a means of treating the lead and cadmium content in the Payatas leachate using aluminum electrodes. The Payatas leachate is a threat to the environment and the health of the people residing near the facility. Three factors (initial pH, voltage, and time) are analyzed to determine the most effective treatment condition that would ensure the heavy metal content remains within the minimum standards of DAO Class C water effluent. The experimental factors are varied based on the following: initial pH (5, 8, and 11), applied voltage (4.0 V, 6.0 V, 8.0 V), and electrocoagulation (EC) time (30 min, 60 min, 90 min). The initial and final metal concentrations are determined with the help of inductively coupled plasma optical emission spectroscopy (ICP-OES). The results showed that the optimized values of the parameters based on the percent reduction of cadmium and lead content are pH 8, 8.0 V at 90 min, and pH 5, 6.0 V at 30 min, respectively. From an economic perspective, the cost-effectiveness of the process is assessed based on the energy consumption in terms of the total cost of electricity used. Based on the economic evaluation, the most effective setting for the electrocoagulation process is pH 11, 6.0 V at 90 min, and pH 8, 4.0 V, at 30 min for cadmium and lead, respectively. This work elucidated the potential of the electrocoagulation process as an alternative technique for the treatment of toxic leachate from the Payatas dumpsite. The sustainability of the process can be improved further through the integration of a renewable energy source into the EC system.

Photovoltaic (PV) energy has become a central pillar in the global transition toward clean and sustainable power sources, offering a renewable and environmentally friendly alternative to conventional electricity generation. This study focuses on optimizing the parameters of a single-diode photovoltaic model to improve its accuracy in representing real-world behavior. A Laguerre-inspired optimization (LO) method is employed, which adjusts each parameter individually. The optimization process relies on minimizing the mean squared error (MSE) between the experimental current values and those predicted by the model, using the first and second derivatives of the objective function. The Laguerre approach's parameters are contrasted with those found in current research using analytical methods, iterative approaches, and metaheuristic algorithms. The root mean square error (RMSE), mean absolute error (MAE), and individual absolute error (IAE) are among the statistical error metrics that are computed to evaluate the performance of the suggested technique. The efficiency of the Laguerre technique in photovoltaic parameter extraction is demonstrated by this methodical approach, which enables perfect calibration of the model in line with experimental data. The suggested LO technique's robustness and dependability in this field are confirmed by the comparison study, which shows that the results produced by this approach have lower error rates than those obtained by other optimization methods.

Accurate photovoltaic (PV) cell modeling is critical to the analysis, diagnostics, and design of solar energy systems. At its center is the extraction of unknown parameters of the single-diode or double-diode models, which are nonlinear in nature and have high sensitivity to initial guesses. In this paper, we propose an efficient and powerful optimization method using Simulated Annealing (SA) for PV parameter extraction at standard test conditions. SA, a probabilistic metaheuristic inspired by the annealing process in metallurgy, is employed in minimizing the deviation of the experimental current–voltage (I-V) data from the model-generated curves.
The algorithm is tested on benchmark PV modules with real I-V characteristics, and its performance is verified in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R² score. A comparison with other optimization techniques such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) confirms the effectiveness and reliability of the SA-based approach. The results show that the SA algorithm leads to fast convergence, avoids local minima, and provides highly accurate parameter estimates with very good agreement with the experimental data.
This research demonstrates the potential of SA optimization as a flexible and reliable approach to PV modeling, especially for researchers and engineers who need precision and strength in solar energy system simulations.