Sustainable Energy Generation Research Articles

Multi-objective algorithm for hybrid microgrid energy management based on multi-agent system

In the dynamic landscape of renewable energies, microgrid systems emerge as a promising avenue for fostering sustainable local energy generation. However, the effective management of energy resources holds the key to unlocking their full potential. This study assumes the task of creating a multi-objective optimization algorithm for microgrid energy management. At its core, the algorithm places a premium on seamlessly integrating renewable energy sources and orchestrating efficient storage coordination. Leveraging the prowess of a multi-agent system, it allocates and utilizes energy resources. Through the combination of renewable sources, storage mechanisms, and variable loads, the algorithm promotes energy efficiency and ensures a steady power supply. This transformative solution is underscored by the algorithm's remarkable performance in practical simulations and validations across diverse microgrid scenarios, offering a prevue into the future of sustainable energy utilization.

Investigating the bacterial consortia properties of electrogenic anodic biofilm in a double-chamber microbial fuel cell: electrochemical, physical, biochemical and molecular characterization

This work evaluated the electrochemical, physical, biochemical, and molecular characterization of electrogens from a graphite felt anode when zinc oxide on activated carbon (ZnO/AC) was used as a cathodic electrocatalyst in a double-chambered microbial fuel cell (DCMFC). The electrochemical polarization behavior of the DCMFC showed that ZnO/AC had a higher power density (PDmax) of 89 mW m−2 with a corresponding cell current density (CD) of 248 mA m−2 and a voltage output of 395 mV, which was higher than those of the blank electrode used as a benchmark (PDmax of 68 mW m−2 at a CD of 161 mA m−2 and a voltage of 421 mV). Furthermore, scanning electron microscopy and transmission electron microscopy revealed that the morphology and interior properties of the strains varied among the rods (bacilli), spirals (vibrios), and spheres (diplococci, staphylococci and streptococci). In addition, biochemical characterization via the Vitek2 compact system and molecular analysis via 16 S rRNA and 18 S rRNA gene sequencing revealed the occurrence of nine prevalent species that were correlated with Sphingobacterium spiritivorum, Ochrobactrum anthropicus, Pseudomonas mendocina, Stenotrophomonas maltophilia, Leuconostoc mesenteroides, Staphylococcus equorum, Bacillus subtilis HQ334981.1, Kocuria kristinae KC581674.1 and Saccharomyces cerevisiae NR111007.1. Consequently, the present study outlines different characterization strategies for electrogenic microbes that play an important role in the overall performance of DCMFC for scaling up and managing existing environmental pollution for sustainable energy generation.

Integration of a Bismuth-Based Tris-Mononuclear Complex with 2D Functional Materials for Highly Efficient and Durable Aqueous Electrocatalytic Hydrogen Evolution.

The eminence of transitioning from traditional fossil fuel-based energy resources to renewable and sustainable energy sources is most evidently crucial. The potential of hydrogen as an alternative energy source has specifically focuses the electrocatalytic water splitting (EWS) as a promising technique for generating hydrogen. Development of efficient electrocatalysts to facilitate the EWS process while rationalizing the limitations of noble metal catalysts like platinum has become one of the daunting tasks. Consequently, porous functional materials such as metal complexes (MCs) and graphene oxide (GO) can act as potential catalysts for EWS. Therefore, a composite of GO and a mononuclear bismuth metal complex is synthesized through in situ facile synthesis, which is further utilized as an efficient electrocatalyst for the hydrogen evolution reaction (HER). Several potential electrocatalytic MC@GO composite (BMGO-3,5,7) materials were prepared with compositional variation of GO (3, 5, and 7 wt %). The experimental results demonstrate that the BMGO5 composite exhibits excellent HER activity with a low overpotential value of 105 mV at 10 mA cm-2 and a low Tafel slope of 44 mV dec-1 in 1 M KOH solution. Furthermore, a comprehensive investigation on the potentiality of the BMC-GO composite for hydrogen evolution from river water splitting was performed in order to address the issue of freshwater depletion. Inclusion of a mononuclear MC for facile synthesis of functional GO-based efficient electrocatalyst material is very scanty in the literature. This unique approach could assist future research endeavors toward designing efficient electrocatalysts for sustainable renewable energy generation. This is one of the first of its kind, where mononuclear MCs were utilized to develop GO-based functional composite materials for efficient electrocatalysis toward sustainable renewable energy generation.

Experimental Measurement of Fixed-Tilt Solar Panel and Vertical-Tilted Single-Axis Solar Tracker (VTSAT) Real-Time Solar Panel Output Power”

Renewable solar energy is becoming more and more vital, and photovoltaic (PV) solar panel efficiency optimization is essential for sustainable energy generation. This study describes an experiment that uses a solar panel multimeter to measure the output power of a fixed-tilt solar panel and a vertical-tilted single-axis solar tracker (VTSAT) in real time. This measurement indicates that solar panel performance varies with several external factors, including temperature fluctuations. Perform a comparison analysis in this study between a vertical-tilted single-axis solar tracker (VTSAT) and a traditional fixed-tilt solar panel. Two 50-watt solar panels' output data were measured using a solar panel multimeter. Utilizing this comparison analysis, assess these panels' real-world performance. Determining the individual efficiency of the two solar panels will be made easier by analyzing the output figures. Moreover, the goal is to compute the daily energy production produced by every kind of solar panel under standard climatic conditions.

A hybrid wind and raindrop energy harvesting operating on Savonius turbine

In offshore environments, abundant wind and rainfall resources await development, representing significant untapped energy potential for powering Internet of Things (IoT) devices. Triboelectric Nanogenerators (TENGs) are particularly suited for capturing continuous motion in offshore environments, presenting a reliable solution for sustainable energy generation in IoT applications. Herein, we propose a wind-droplet hybrid generator (WDHG) tailored for adaptive environmental energy harvesting, explicitly targeting the demanding conditions prevalent in offshore environments. The WDHG combines the efficiency of a helical Savonius TENG with the versatility of an interdigitated electrode TENG, enabling simultaneous wind and rain energy harvesting. The helical Savonius TENG exhibits superior aerodynamic performance, producing an output power ranging from 0.39 to 2.10 mW with wind speeds increasing from 0.75 to 6.84 m/s. Additionally, the droplet TENG generates a power output of 35.90 mW/m2 under rainfall at 70 mm/min. Optimizations include not only the comparative analysis of helical Savonius turbines and the variation in pole-pair numbers but also adjustments in fluorinated ethylene propylene (FEP) film thickness and electrode gap length for interdigitated electrode, alongside the development of a tailored power management circuit (PMC) featuring dual bridge rectifiers, a buck circuit module, and a smart load switch. The PMC enables continuous operation of a low-powered digital thermohygrometer under conditions of 4.5 m/s wind speed, and drives a 20-second wake BLE server under 7.2 m/s wind speeds. Integration of the WDHG with offshore wind and raindrop extends energy harvesting capabilities, exhibiting a great potential of providing reliable power source for IoT devices.

Adsorption of thorium (IV) from aqueous solutions using mesoporous TiO2: Kinetics, isotherm and thermodynamics

Thorium is considered as a promising resource for the production of nuclear power, with its abundance in the Earth's crust offering potential for sustainable energy generation to fulfill upcoming energy needs. As a fertile material, thorium has the capability to be converted into fissile uranium-233, which is essential for sustaining nuclear chain reactions. Therefore, the extraction of thorium from the Earth's crust is imperative for its utilization as fuel in nuclear reactors. This study presents the synthesis of titanium dioxide with a mesoporous structure using the solvothermal method for extracting Th(IV) from aqueous solutions. Batch adsorption experiments were carried out to evaluate the impact of solution pH, adsorption kinetics, adsorbent dosage, initial Th(IV) concentration, and temperature. The adsorption process is influenced by the pH, showing a maximum adsorption capacity of 101.4 mg per gram of mesoporous TiO2 adsorbent (MTA) at an initial Th(IV) concentration of 400 mg L−1 at pH 4.0. Results indicate a significant correlation with the pseudo-second-order and Freundlich adsorption isotherm models. Temperature-dependent investigations revealed that the adsorption of Th(IV) is spontaneous at room temperature and increases with higher temperatures (endothermic process). Moreover, the study highlights the rapid kinetics of Th(IV) adsorption by MTA, making it a favorable choice for upscaling the extraction process of Th(IV). In conclusion, the results suggest MTA as a promising adsorbent for the removal of Th(IV) from aqueous solutions.

Optimal integration and planning of PV and wind renewable energy sources into distribution networks using the hybrid model of analytical techniques and metaheuristic algorithms: A deep learning-based approach

The optimal integration of Renewable Energy Sources (RESs) into distribution networks is crucial for sustainable energy generation. However, existing approaches utilized for optimal RES integration, including metaheuristic and analytical methods, exhibit limitations. Analytical methods are simple to implement, computationally efficient, and able to model power system mechanisms, yet they face scalability issues, making them inefficient for large-scale distribution systems. On the other hand, metaheuristic approaches, while flexible for complex problems, suffer from premature convergence problems, limiting their application in real distribution networks. To address these issues, this paper introduces a novel hybrid model that combines the strengths of analytical and metaheuristic techniques. The proposed model employs a deep learning-based method called bi-directional long short-term memory (B-LSTM) network is utilized to handle the uncertainties associated with the Photovoltaic (PV) and Wind Turbine (WT) generation and load demand. Subsequently, a distinctive analytical multi-objective index-based procedure is developed to minimize the real and reactive energy loss and voltage deviation by analyzing the branch current change caused by the RES penetration in distribution networks. The optimization problem is then solved using the binary particle swarm optimization (BPSO) technique. Importantly, the presented method considers the long-term nature of optimal RES integration and reflects the seasonal impacts of the load and RES output powers. The proposed approach is applied to the IEEE 33-bus test system. The simulation results show that the introduced hybrid model, by addressing the limitations of existing approaches, offers a promising solution for efficient and effective RES integration in distribution systems while requiring low computational effort, thereby advancing the transition towards sustainable energy infrastructure.

Nitrogen‐Rich Carbon Dot‐Mediated n→π* Electronic Transition in Carbon Nitride for Superior Photocatalytic Hydrogen Peroxide Production

AbstractSolar‐driven synthesis of hydrogen peroxide (H2O2) through photocatalysis stands out as a promising avenue for sustainable energy generation, marked by environmental friendliness and industrial feasibility. However, the inherent limitations of carbon nitride (CN) in photocatalytic H2O2 production significantly impede their performance. Herein, a novel 0D/2D carbon dots‐modified CN nanosheet heterojunction (CDsMCN) is introduced, synthesized through a hydrothermal‐calcination tandem strategy induced by CDs derived from melamine. This innovative technique enhances the n→π* electronic transition in CDsMCN, accelerating the separation efficiency of electron‐hole pairs, boosting oxygen adsorption, and promoting a highly selective 2e− ORR. Comparative to pristine CN, CDs10MCN exhibited a remarkable tenfold increase in H2O2 production, reaching an impressive 1.48 mmol L−1. Furthermore, CDs10MCN demonstrates exceptional stability, maintaining its catalytic efficiency at the initial level over four consecutive cycles. The notable achievement of a molar selectivity of H2O2 ≈80% at an onset potential of 0.6 V (vs RHE) underscores its exceptional ability to produce the desired product selectively. Advanced in situ characterization together with DFT calculations revealed that the ultrathin CDs10MCN nanosheet heterojunction with enhanced n→π* electronic transition improves its optical properties, reduces bandgap, facilitates fast charge migration, and increases photocatalytic H2O2 performance, thereby serving as a promising candidate for advanced catalytic applications.

Advanced thermoconversion technology for municipal solid waste energetic valorization

This paper presents a novel approach to tackle the dual challenges of energy scarcity and municipal solid waste management by exploiting the potential of municipal solid waste gasification. The performance of the modified updraft gasifier is evaluated using computational modeling with Aspen Plus V14 software. The reliability of the gasification model is confirmed by comparing it with experimental data, highlighting its accuracy in estimating key gas composition parameters. A detailed analysis of the percentage error between the gas's experimentally measured lower heating value and the model-estimated value is performed. Results show a 12.2 % error for municipal solid waste gasification, meaning the experimental lower heating value is higher than the model's estimation. On the other hand, a 27,2 % error is observed for pellet gasification, implying a lower experimental lower heating value than the model's estimation. These findings provide useful information for future optimization and improvement of the gasification process, further advancing the field of waste-to-energy conversion and promoting sustainable energy generation.

Exploring Titanium Dioxide Nanotubes: Pioneering Advanced Applications in Efficient Water Splitting and Sustainable Hydrogen Generation

This interdisciplinary study synthesizes findings from diverse fields including materials science, psychology, and environmental sustainability to explore the multifaceted challenges and opportunities associated with titanium dioxide (TiO2) nanotubes and social media use. In the realm of materials science, TiO2 nanotubes have garnered attention for their potential applications in sustainable energy generation, particularly in water splitting for hydrogen production. Synthesis methods such as hydrothermal synthesis and nitrogen doping have been investigated to enhance the photocatalytic performance and stability of TiO2 nanotubes. Concurrently, the proliferation of social media platforms has transformed communication patterns and behaviors worldwide, shaping individuals' interactions, perceptions, and mental health outcomes. While social media offers opportunities for connectivity and self-expression, excessive use has been associated with adverse mental health outcomes, including symptoms of depression, anxiety, and loneliness. Additionally, social media engagement has been linked to body image dissatisfaction, sleep disturbances, and academic performance, highlighting the complex interplays between online behaviors and psychological well-being. This study integrates insights from a comprehensive review of peer-reviewed literature, encompassing experimental research, observational studies, and survey-based investigations. The synthesis underscores the need for interdisciplinary collaboration and targeted interventions to address the complex societal challenges posed by TiO2 nanotubes and social media use. Recommendations include further research to optimize synthesis methods, enhance regulatory oversight, and promote responsible digital citizenship. By fostering dialogue and collaboration between researchers, policymakers, and practitioners, this study aims to inform evidence-based interventions and policy initiatives to promote sustainability, well-being, and equity in the digital age.

Optimizing the process of sewage sludge collection for bioenergy generation and biofertilizer production

The conversion of sludge from sewage treatment plants (STPs) into bioenergy is a promising approach to sustainable energy generation and effective waste management. This process occurs through anaerobic digestion in the biodigesters, resulting in biogas production. However, given that the sludge comes from various STPs in different cities, identifying the ideal locations requires careful selection due to the complexity of sludge management, associated costs, and transportation logistics. In this context, this work aims to formulate a mathematical optimization model to determine the best locations for the installation of sludge processing centers, considering the minimization of the economic, environmental, and social costs. The model is implemented in MATLAB software, and geospatial analysis of the regions of interest is performed using QGIS software. For model validation, three distinct instances are considered. The results obtained demonstrate significant potential in terms of biogas and electricity production, with the highest values for the third instance being 61,997,501 Nm³/year and 149,275 MWh/year, respectively. The proposed methodology represents a crucial step toward sustainability, as it promotes the generation and use of bioenergy from STP sludge and contributes to improved waste management and the prevention of air, soil, and water pollution.

Energy Production Optimization by Positioning of Solar Panels and Surveillance System with Cleaning of Panels

The pursuit of efficient and sustainable energy generation has led to the widespread adoption of solar panels. However, the optimal performance of these panels can be hindered by factors such as dirt accumulation, suboptimal orientation, and security concerns. To address these challenges, we propose an integrated Solar Panel Tracking, Cleaning, and Surveillance System (SPTCSS). Our system employs advanced tracking technology to optimize solar panel orientation, ensuring maximum exposure to sunlight throughout the day. Additionally, it incorporates automated cleaning mechanisms, utilizing water and brushes to remove dust, debris, and other contaminants, thereby maintaining peak efficiency. Furthermore, the surveillance aspect of the system provides real-time monitoring and security features, safeguarding against theft, vandalism, and unauthorized access. Through a network of sensors and cameras, any anomalies or suspicious activities can be promptly detected and addressed. By integrating tracking, cleaning, and surveillance functionalities into a single system, our solution offers a comprehensive approach to enhancing the performance, longevity, and security of solar panel installations. This not only maximizes energy generation but also minimizes maintenance requirements and mitigates risks, ultimately contributing to a more sustainable and reliable renewable energy infrastructure

Advancing solar PV panel power prediction: A comparative machine learning approach in fluctuating environmental conditions

Solar photovoltaic (PV) panels play a crucial role in sustainable energy generation, yet their power output often faces uncertainties due to dynamic weather conditions. In this study, a comparative machine learning approach is introduced, utilizing multivariate regression (MR), support vector machine regression (SVMR), and Gaussian regression (GR) techniques for precise solar PV panel power prediction. The investigation into the impact of environmental factors—solar radiation, ambient temperature, and relative humidity—on PV panel output reveals the superior predictive capabilities of SVMR models. With a mean squared error (MSE) of 0.038, a mean absolute error (MAE) of 0.17, and an R2 value of 0.99, SVMR outperforms GR and MR models. Conversely, Gaussian regression demonstrates comparatively weaker performance, yielding an R2 of 0.88, an MSE of 0.49, and an MAE of 0.63.This research underscores the reliability and enhanced accuracy of the proposed SVMR model in forecasting solar PV panel output. The outcomes presented herein carry significant implications for promoting the widespread adoption of PV panels in electricity generation, particularly in challenging environmental conditions. The findings offer valuable insights into optimizing solar PV deployment, ultimately contributing to the expansion of solar power generation in the national energy landscape. Moreover, the comparative analysis provides insights into how anticipated PV power generation can adapt to varying weather conditions, encompassing factors such as temperature, humidity, and solar radiation.

A Comprehensive Review of Modern Methods for Load Prediction in the Smart Grid

: Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elastic demand, climate and consumer response, load analysis, and sustainable energy generation predictive modelling are used. As both supply and demand fluctuate, and weather and power prices can rise significantly during peak periods, accurate load forecasting becomes critical for utilities. By providing brief demand forecasts, load forecasting can assist in estimating load flows and making decisions that prevent overloading. Therefore, load forecasting is crucial in helping electric utilities make informed decisions related to power, load switching, voltage regulation, switching, and infrastructure development. Forecasting is a methodology used by electricity companies to forecast the amount of electricity or power production needed to maintain constant supply as well as load demand balance. It is required for the electrical industry to function properly. The smart grid is a new system that enables electricity providers and customers to communicate in real-time. The precise energy consumption sequence of the consumers is required to enhance the demand schedule. This is where predicting the future comes into play. Forecasting future power system load (electricity consumption) is a critical task in providing intelligence to the power grid. Accurate forecasting allows utility companies to allocate resources and assume system control in order to balance the same demand and availability for electricity. In this article, a study on load forecasting algorithms based on deep learning, machine learning, hybrid methods, bio-inspired techniques, and other techniques is carried out. Many other algorithms based on load forecasting are discussed in this study. Different methods of load forecasting were compared using three performance indices: RMSE (Root Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and Accuracy. Machine learning-based techniques showed a reduction of 9.17% in MAPE, 0.0429% in RMSE, and 5.23% in MSE, and achieved 90% accuracy. Deep learning-based techniques resulted in a 9.61% decrease in MAPE and achieved 91% accuracy. Bioinspired techniques provided a reduction of 9.66% in MAPE, 0.026% in RMSE, and 5.24% in MSE, and achieved 95% accuracy. These findings concluded that optimization techniques are more encouraging in predicting load demand and, as a result, can represent a reliable decision-making tool.

Hydrogen production by water electrolysis driven by a photovoltaic source: A review.

The integration of water electrolyzers and photovoltaic (PV) solar technology is a potential development in renewable energy systems, offering new avenues for sustainable energy generation and storage. This coupling consists of using PV-generated electricity to power water electrolysis, breaking down water molecules into hydrogen and oxygen. While oxygen is a useful byproduct, the created hydrogen is used as a clean, storable energy carrier or feedstock for numerous businesses. It is possible to operate the device with or without battery storage. When solar energy is combined with batteries, excess solar energy may be stored for later use, maximizing energy efficiency and guaranteeing a steady supply of electricity even in the absence of direct sunlight. On the other hand, battery-free systems depend on the electrolyzer’s continuous power generation to convert solar energy into hydrogen during the day. In addition to allowing for the production of renewable hydrogen,this hybrid PV-solar and water electrolyzer setup contributes to grid stability by offering demand-side flexibility. Moreover, the modularity of these systems enables scalability to meet diverse energy requirements, spanning from residential to industrial applications, thereby fostering a cleaner and more sustainable energy landscape. This review delves into various topologies for PV-driven electrolysis and conducts a thorough exploration of the dynamics of low-temperature water electrolyzers. Specifically, it examines their integration with three primary technologies: Proton Exchange Membrane, Alkaline, and Anion Exchange Membrane, shedding light on their implications for the broader integration landscape. Through detailed analysis and insights, this study enriches the understanding of the potential and challenges inherent in the convergence of PV solar, water electrolysis, and renewable energy systems.

Repurposing Co-Fe LDH and Co-Fe LDH/Cellulose micro-adsorbents for sustainable energy generation in direct methanol fuel cells

Spent adsorbents need to be recycled as well as valorized to obtain long-term benefits from the adsorption of contaminants from wastewater. Co-Fe LDH and Co-Fe LDH/cellulose-layered double hydroxide (LDH) were prepared for crystal violet (CV) adsorption. FT-IR, XRD, SEM, TEM, and, zeta potential analyses confirmed the micro-composites. The parameters examined in this study were the initial pH, initial concentration, adsorbent dose, contact time, adsorption isotherm, and adsorption kinetics. The removal percent was 97.5 % and 88 % for Co-Fe LDH and Co-Fe LDH/cellulose, respectively. A maximum adsorption capacity was 69.4 and 50.0 mg g−1 for Co-Fe LDH and Co-Fe LDH/cellulose, respectively, at pH = 9. The Langmuir-Freundlich model can adequately explain the isotherm processes of LDH and LDH/cellulose; R2 was 0.96 and 0.95, respectively. The spent adsorbents (Co-Fe LDH/CV and Co-Fe LDH/Cellulose/CV) were applied once again as catalysts for electro-oxidation in direct-methanol fuel cells. Due to its high stability in the basic medium (NaOH), the waste material was exploited as an active catalyst to support methanol oxidation. It displayed a better efficiency of 74.75 % and a current density of 76 mA/cm2 at 50 mV/s in 1 M methanol for Co-Fe LDH/CV. The suggested technique opens up a completely new channel for reusing waste into high-value materials for application in electrocatalytic energy generation.

Design of adaptive protection coordination scheme using SVM for an AC microgrid

Microgrids have gathered a significant amount of attention within the past decade and becoming an essential asset in the energy industry. The ability to integrate sustainable renewable energy generation into the distribution network is one of the main reasons for microgrids' popularity. Besides technical advantages, renewable energy sources integration with the grid poses many technical challenges. Due to randomness in generation and different modes of operation, integrating renewable energy sources into power systems will be challenging. Reliable operation of renewable energy sources integrated microgrids even when faults occur, requires an adaptive protection scheme and relay coordination. In order to tackle those issues, this paper developed an adaptive protection coordination scheme using numerical overcurrent relays and support vector machines with a particle swarm optimization approach. Renewable energy sources based on microgrids use power electronics systems that require relays to operate in milliseconds, and the power system will be safe. particle swarm optimization's program execution time exceeds the primary relays. The machine learning algorithm is faster in computation time. This article proposes a method, particle swarm optimization, to estimate the optimum relay operating time. The machine learning model having a support vector machine is proposed to estimate the relay operating time. The proposed research is developed and tested on an IEEE12BUS and IEEE35BUS system. The support vector machine -based on adaptive protection coordination schemes estimates the operating time of directional overcurrent relays, with a lesser program execution time and a low percentage error rate. The derived result defined that the proposed approach is well-suitable for real-time applications of an AC microgrid system.

Sustainable energy generation: High-performance NiCo2S4@S-g-C3N4 bifunctional electrocatalyst advances water splitting efficiency

Efforts to create cost-effective electrocatalysts from abundant materials, for the utilization of water electrolysis reactions, are viewed as promising sources for sustainable clean energy generation. This article describes the synthesis of cobalt sulphide (CoS) nanoparticles using a straightforward and environmentally friendly one-pot hydrothermal process. The series of nickel-doped CoS (NiCo2-x S4) introduced with varying the weight percentage of Ni (1, 2, 4, 8, 6, and 10 wt%), and the composite Ni-dopped CoS on varying the weight percentage (10, 30, 50, 70, and 90 wt %) of sulfur-doped graphitic carbon nitride (NiCo2S4@S-g-C3N4) was created. Utilizing X-ray diffraction analysis (XRD), EDS, FTIR, and scanning electron microscopy (SEM) synthesized electrocatalysts were characterized. Doped with Fluorine After being changed with an electrocatalyst, tin oxide was used as the working electrode to monitor the electrochemical activity of the whole water splitting process using chronopotentiometry, cyclic voltammetry (CV), linear sweep voltammetry (LSV), and EIS. NiCo2S4@S-g-C3N4 exhibited higher electrocatalytic performance with an overpotential 370 mV for OER and 415 mV for HER at the current intensity of 10 mAcm−2. Transition metal sulfide electrocatalysts exhibit good electrolytic performance, due to their high surface areas and intrinsically high catalytic activity. These findings suggest that the NiCo2S4@S-g-C3N4 composite possesses a significant role for use in energy conversion and energy storage applications.

Optimizing microwave frequencies for hydrogen and electromagnetic shielding materials production through methane pyrolysis over biochar

Clean hydrogen production through methane pyrolysis over renewable biochar offers a promising avenue for sustainable energy generation. Herein, an innovative approach that utilizes variable microwave frequencies to facilitate methane pyrolysis is introduced. The frequency optimization study reveals that a frequency of 4225 MHz maximizes methane conversion, yielding an impressive rate of 90.7 % at 100 W microwave power. However, prolonged pyrolysis under the same frequency for 60 min leads to a diminished conversion, resulting in a reduced efficiency of 43.9 %. Notably, by alternating microwave frequencies to 4400, 5150, 4600, and 4620 MHz, the reactivation of deactivated biochar is achieved, consistently restoring efficiency to over 97 % for five consecutive cycles. The transition from polarization-driven loss to conduction-induced loss is identified as the key mechanism responsible for the in-situ reactivation of spent biochar, which is controlled by varying microwave frequencies. Moreover, the spent biochar exhibits a heightened microwave-absorbing capacity, characterized by an exceptionally low reflection loss of −73.1 dB at a thickness of 2.1 mm. Hence, adjusting microwave frequencies to enhance methane pyrolysis not only promotes clean hydrogen production but also yields high-performance electromagnetic shielding materials. This dual outcome optimizes sustainable methane pyrolysis for efficient hydrogen production.

Recent advances in photothermal catalysis: Coupling hydrogen evolution and organic conversion

Photothermal catalysis, as a green and sustainable technology, has attracted increasing attention in the fields of clean energy and green organic synthesis. Integrating photothermal catalysis with solar-driven hydrogen production and organic synthetic chemistry has become a promising strategy for sustainable energy generation with great research and practical value. In this review, we present an overview of the background of solar-driven photothermal catalytic reactions, introduce the fundamental principles of photocatalysis, thermal catalysis, and photothermal reduction of hydrogen coupled with selective oxidation of organic compounds, and summarize the recent advances in photothermal semiconductor catalysts such as carbon-based materials, titanium-based materials, noble metal composites, and metal sulfides. We also highlight the recent advances in the application of these catalysts in photothermal catalytic water splitting for hydrogen generation and selective oxidation of organic compounds. Finally, we address the challenges and opportunities faced by solar-drive photothermal catalysts in the synergism of photocatalytic hydrogen production and selective oxidation of organic compounds. The aim is to provide guiding insights for the rational design of this dual-function catalytic system, offering a green pathway for the development of clean energy and selective oxidation in organic synthesis.