Roberto Adrián González Domínguez

Low-temperature proton exchange membrane fuel cells (LT-PEMFCs) constitute a key technology in the context of global energy decarbonization that combines high electrochemical efficiency, low operating temperatures (60–80 °C), and use in transportation, distributed generation, and portable applications. Bipolar plates (BPPs) are the backbone of a fuel cell. They carry out many functions, including electrical conduction, distribution of reactants, thermal management, and mechanical support. BPPs contribute approximately 60–80% to the total system weight and 30–45% to the total system cost. There are many research gaps in BPPs. Previous studies on BPPs are fragmented, and many studies focus on material and geometric design of flow fields, but research on how these factors affect the overall performance of the cell is absent. This segmentation limits the options for developing durable and scalable technological solutions. This review proposes a conceptual approach around three axes: (i) materials and surface engineering (graphite, coated metals, and polymer composites), (ii) flow-field geometry (serpentine, parallel, interdigitated, and bio-inspired), and (iii) operating conditions (temperature, humidity, pressure, stoichiometry, and dynamic regimes). A systematic review of literature published between 2015 and 2025 identified 315 eligible studies, which were retained for qualitative synthesis; from these, 160 representative references were selected for citation based on scientific relevance, methodological rigor, impact, and contribution to the MGOC framework, prioritizing mainly Q1–Q2 publications indexed in Scopus, Web of Science, and ScienceDirect and providing evidence for identifying and elaborating research gaps related to integrated design, multi-objective optimization, and predictive modeling.

A firewood stove’s combustion chamber can withstand temperatures of 1500 °C. To prevent the deterioration of a firewood stove due to excessive heat, it is necessary to use thermal insulation materials that stop heat transfer to the walls. These materials must be economical and durable. This work examines the materials used in the construction of combustion chambers of firewood stoves in southern Mexico and Central America. This field study collects information and samples of materials used in the manufacture of firewood stoves. Heat transfer experiments are conducted, and the thermal properties of each material are analyzed. As a result, methodology and information is provided for the manufacture of future plancha-type firewood stoves used in the study area, such as pine wood (pinus chiapensis) which is mainly used as casing for firewood stoves in coniferous forest areas; in addition, the use of wood ash as thermal insulation material is proposed since it does not present direct costs and has a thermal conductivity between 0.10 and 0.20 W/m°C and a melting point greater than 1500 °C. The next layer proposed is hollow brick, a high-temperature-resistant material that can be used as support due to its mechanical strength and low thermal conductivity of 0.6 W/m°C. Finally, the use of calcium hydroxide as a coating material is proposed, applied in the form of a paste or paint to detail the imperfections of the combustion chamber construction as it resists temperatures above 1000 °C.

This paper presents the design, simulation, and experimental evaluation of a low-cost, fixed-step MPPT algorithm based on the incremental conductance technique for operation in a low-power photovoltaic (PV) system with a full-bridge DC-AC converter. The performance of the MPPT algorithm was improved by selecting an appropriate fixed perturbation step size and frequency, ensuring efficient power tracking. The implementation was further optimized by restructuring the conventional algorithm and adapting the DC-AC converter control parameters, which enhanced overall performance and optimized coupling for AC loads. The simulation was performed in Simulink/Matlab with a 560 Wp PV system and a resistive load, under variable irradiation conditions. The perturbation step size was set to 1%, and the perturbation frequency ranged between 2 Hz and 15 Hz, with the converter output at 60 Hz. Experimentally, it was validated at an irradiance of 1000 W/m² and an ambient temperature of 45 °C. The algorithm achieved simulation efficiencies of up to 98.93% and an average experimental efficiency of 96.76%. The response time improved by 86% with a perturbation frequency of 15 Hz. This developed MPPT algorithm demonstrates its reliability, accuracy, and feasibility for implementation.

At present, microgrids (μGs) are a focal point in both academia and industry due to their capability to sustain operations that are stable, resilient, reliable, and of high power quality. Power converters (PCs), a vital component in μGs, enable the decentralization of power generation. However, this decentralization introduces challenges related to power quality. This paper introduces a μG model, based on the IEEE 14-bus distribution system, with the objective of investigating power quality when the μG is operating in conjunction with the conventional power grid. The μG model was developed using MATLAB-Simulink^®, a tool specialized for electrical engineering simulations. The results obtained undergo thorough analysis and are compared with the compatibility levels set by the IEEE-519 standard. This method enables a precise evaluation of the μGs’ capacity to maintain acceptable power quality levels while interconnected with the conventional power grid. In conclusion, this study contributes significantly to the field of μGs by providing a detailed and quantitative assessment of power quality. This will assist in the design and optimization of μGs for effective implementation in real-world electric power systems.

Jet fuel production is a key element in the aviation industry’s strategy to reduce operating costs and environmental impacts. Alternatives are required, and bioturbosine obtained from biomass can replace significant amounts of jet fuel. In this investigation, the properties of the production of bioturbosine from coconut oil and mixtures of B5, B10, B20, B1OO, bottom, and jet fuel were measured according to the ASTM standards. A transesterification reaction between coconut oil and methanol was carried out using ultrasound, resulting in 99.93% conversion of triglycerides into bioturbosine at room temperature for 10 min, with a 6:1 molar ratio of methanol and oil. The catalyst concentration was 1.0 g/100 g of oil, and purification was carried out without water using an ion-exchange resin to remove impurities. The results obtained for density and viscosity were within the regulations. The temperature of the clogging point for a cold filter in the mixtures was up to −30 °C. The average caloric values of mixtures B5, B10, and B20 were 45,042, 44,546, and 43,611 MJ/Kg, respectively. In a copper corrosion test, the result for all samples was class 1A. It is expected that the results of this research may influence the use of bioturbosine in the aviation industry.

Currently, one of the main focuses of attention in academia is microgrids (MGs) and their ability to maintain stable, resilient, reliable and high–power quality operations. A crucial component in MGs is power converters (PC), which facilitate the decentralization of power generation. This decentralization, however, poses challenges in terms of power quality. This paper presents a microgrid model based on the IEEE 14–bus distribution system and aims to investigate the power quality when the MG operates while connected to the conventional power grid. The MG model has been developed using MATLAB–Simulink®, a specialized tool for electrical engineering simulations. The results obtained are subjected to rigorous analysis and compared with the compatibility levels established by the IEEE–519 standard. This approach allows an accurate assessment of the MGs’ ability to maintain power quality at acceptable levels while interconnected to the conventional power grid. Finally, this study provides a valuable contribution to the MGs’ field of microgrids by providing a detailed and quantitative assessment of power quality, which will aid in the design and optimization of MGs’ for effective implementation in real electric power systems.

Wind energy is one of the most relevant clean energies today, so wind turbines must have good health and be reliable in operation. Current wind turbines have slender and elastic structures that can be easily damaged through vibrations and compromise their health; therefore, vibration monitoring is essential to ensure safe operation. Here, we present a method for simple wind turbine vibration monitoring in the laboratory by means of an accelerometer placed on a weathervane under different scenarios, with recording of different amplitudes of vibrations caused at a constant speed of 10 km/h. The variables, trends, and data captured during vibration monitoring were then used to implement a prediction system of synthetic failure using machine learning methods such as: Medium Trees, Cubic SVN, Logistic Regression Kernel, Optimized Neural Network, and Bagged Trees, with the last demonstrating an accuracy of up to 0.87%.