Reinaldo Souza

Wastewater electrochemical processes and electrolysis—defined here as direct electrochemical water splitting using wastewater as the electrolyte, without biological hydrogen-producing pathways or external organic oxidation processes—have emerged as compelling dual-function strategies for renewable hydrogen generation and advanced wastewater treatment. Unlike broader electrochemical and bioelectrochemical systems such as microbial electrolysis cells or photoelectrochemical reactors, wastewater electrolysis relies solely on anodic and cathodic electrochemical reactions driven by applied potential, while leveraging the intrinsic conductivity and ionic/organic constituents of wastewater to reduce energy demand. This review synthesizes recent progress in hydrogen production across diverse wastewater matrices, including municipal effluents, agro-industrial wastewaters (olive mill, vinegar fermentation, and cheese whey), and industrial streams like metal-plating effluents. Emerging wastewater types—textile and dairy effluents, landfill leachate, tannery and pharmaceutical effluents, petrochemical wastewater, acid mine drainage, and desalination brine—are evaluated for their suitability as electrolytes. Key parameters governing performance, including conductivity, organic load, pH, and the presence of metals and inhibitory species, are examined alongside electrode materials, fouling and corrosion mechanisms, energy efficiency, and environmental implications. Challenges such as wastewater variability, electrode degradation, and chlorine evolution in saline matrices are discussed with emphasis on future research needed to integrate wastewater electrolysis within the circular economy. In essence, wastewater-driven hydrogen production represents a promising route toward sustainable, distributed energy systems while delivering co-benefits for water quality improvement.

Optoelectronic devices are emerging as a cornerstone of advanced renewable energy technologies, offering innovative routes for energy harvesting, conversion, and management with high efficiency and versatility. This review summarizes recent advances in the semiconductor materials engineering field, device configurations, and light–matter interaction mechanisms that underpin advanced optoelectronic systems for solar energy harvesting, solar-driven chemical conversion, and smart grid integration, among others. Emphasis is placed on the breakthroughs achieved in the perovskite and hybrid photovoltaics, photoelectrochemical energy conversion, and nanostructured optoelectronic platforms that enable much-increased light absorption, reduced recombination losses, and scalable large-scale fabrications. Moreover, the challenges closely linked with long-term stability, environmental durability and benevolence, and worldwide deployment are critically addressed, together with the emerging opportunities in AI design, tandem device technological solutions, integrated energy systems, and machine learning approaches for optimizing device performance, thermal management, and energy storage capabilities. Finally, the present review concludes by outlining the future research directions that could accelerate the transition toward high-performance, cost-effective, and sustainable optoelectronic solutions responsive to global renewable energy requirements.

The ionic liquids are increasingly used as versatile media capable of reshaping the electrochemical environment for hydrogen production. Their wide electrochemical windows, thermal stability, and customizable solvation structures enable these liquids to tailor the electrode–electrolyte interface in such a way that the traditional alkaline and polymer-membrane systems cannot. These features allow for reductions in the hydrogen evolution overpotentials, improved catalyst stability, and effective suppression of gas crossover, positioning the ionic liquids as promising components for advanced electrolysis systems. Despite these benefits, their broader deployment remains constrained by certain challenges. The elevated viscosity and associated mass-transport limitations complicate the cell design and energy efficiency, whereas the cost and long-term stability of many ionic liquids limit their competitiveness in industrial hydrogen production. Also, the hydrolysable anions and other reactive species increase the burden, particularly in environments where moisture and anodic potential are present. As a result, the ionic liquids electrolysis has its most promising prospects in niche and hybrid configurations like the renewable integrated systems and configurations where the tailored interfacial chemistry and long operational lifetimes outweigh the investment cost and maintenance requirements. Future progress will depend on the development of greener, task-specific ionic liquids with improved stability and lower synthesis costs, alongside hybrid electrolyte designs that balance the unique interfacial benefits of ionic liquids with the practicality of aqueous systems. Advancing these materials from laboratory research to large-scale sustainable hydrogen production will require coordinated advances in the materials compatibility, device and infrastructural architecture, and techno-economic optimization.

This study aims to enhance the efficiency of hydrogen production through alkaline water electrolysis by analyzing hydrogen bubble dynamics using high-speed image processing and machine learning algorithms. The experiments were conducted to evaluate the effects of electrical current and ultrasound oscillations on the system performance. The bubble formation and detachment process were recorded and analyzed using two segmentation models: Ilastik, a GUI-based tool, and U-Net, a deep learning convolutional network implemented in PyTorch. v. 2.9.0. Both models were trained on a dataset of 24 images under varying experimental conditions. The evaluation metrics included Intersection over Union (IoU), Root Mean Square Error (RMSE), and bubble diameter distribution. Ilastik achieved better accuracy and lower RMSE, while U-Net. U-Net offered higher scalability and integration flexibility within Python environments. Both models faced challenges when detecting small bubbles and under complex lighting conditions. Improvements such as expanding the training dataset, increasing image resolution, and adopting patch-based processing were proposed. Overall, the result demonstrates the automated image segmentation can provide reliable bubble characterization, contributing to the optimization of electrolysis-based hydrogen production.

This study investigates the heat transfer performance of three types of heat exchangers: one made of pure polydimethylsiloxane (PDMS), another incorporating recycled graphite (PDMS + Graphite 30 mass%), and a third using commercial aluminium nanoparticles (PDMS + Aluminium 30 mass%). Thermal performance was evaluated by measuring the thermal conductivity of the materials, analysing experimental convection tests with deionized water in a single-phase regime and using a thermal camera to obtain temperature profiles of the different surfaces. The results revealed that the composites formed with PDMS matrix and recycled graphite showed elevated thermal conductivity, approximately 2.7 times higher than pure PDMS. The heat transfer coefficient performance was 2.5 times superior to that of the heat exchanger made with commercial aluminium nanoparticles and up to 5 times higher compared to pure PDMS. The thermal analysis highlighted the benefits of the composites, showing a more uniform temperature distribution both in the serpentine channel and along the sides of the PDMS. The study aimed to provide an economical alternative that also contributes to waste valorisation. These findings validate the effectiveness of recycled particles in improving heat transfer performance in heat exchangers made from a combination of PDMS matrix and recycled, economical materials.

Phase change materials (PCMs) have emerged as promising solutions for improving thermal management in residential buildings by enhancing thermal storage capacity and reducing energy consumption. This article aims to provide a comprehensive analysis of the application of PCMs in residential construction, focusing on their thermal properties, benefits, and limitations. A systematic literature review was conducted following PRISMA guidelines, primarily covering studies published between 2015 and 2025. However, key studies published outside this period were also considered due to their relevance and significant contribution to the understanding of PCM performance and application. This analysis explores key parameters affecting PCM performance, including phase transition temperature, thermal conductivity, and material stability. The results highlight that optimized PCM integration can reduce energy consumption by up to 30% and improve indoor thermal comfort. However, challenges such as low thermal conductivity and phase separation still limit their large-scale adoption. The findings provide insights into the advantages and barriers associated with PCM-based systems and propose strategies to enhance their performance, including the use of nanocomposites and improved encapsulation techniques.