Sustainable Energy Research Articles

Towards optimal energy efficiency: analysing generalized and tailored retrofitting decisions

A building’s energy performance, in terms of thermal comfort, energy demand, cost and CO2 emissions, is considerably affected by its envelope. Enhancing energy efficiency through maintenance and retrofitting is essential to reduce consumption and emissions, thereby mitigating climate change. However, selecting the most cost-effective retrofitting solution remains challenging for decision-makers. Analysing real data across multiple scenarios provides valuable insights, supporting informed decision-making. This study discusses the impact of thermal retrofitting decisions on the energy efficiency of an existing single-family home, by analysing multiple scenarios concerning the implementation of measures on external walls, roof and windows. Both generalized and tailored approaches, particularly for external walls, are evaluated. Options include different insulation materials for the roof and façades—with the latter employing an external thermal insulation composite system (ETICS)—and various framing materials with double-glazing for window replacement. Various scenarios are discussed based on thermal simulations, implementation costs, and cost-benefit analysis. Additionally, multi-criteria (MCA) and sensitivity (SA) analyses are conducted to determine the optimal retrofitting solution. The most effective combined strategy applies ETICS with rock wool on the external walls, extruded polystyrene panels on the roof, and aluminium-framed windows with a thermal break, balancing energy efficiency, costs, durability, and sustainability. Although not part of the optimal solution, tailored retrofitting of façade F2 presents a viable alternative under cost constraints.

Progressive Learning-Guided Discovery of Single-Atom Metal Oxide Catalysts for Acidic Oxygen Evolution Reaction.

The oxygen evolution reaction (OER) is a key bottleneck in clean energy conversion due to sluggish kinetics and high overpotentials. Transition metal single-atom catalysts offer great promise for OER optimization thanks to their high atomic efficiency and tunable electronic structures. However, intrinsic scaling relationships between adsorbed intermediates limit catalytic performance and complicate discovery through conventional machine learning (ML). To overcome this, we combined density functional theory (DFT) with a progressive learning strategy within an active learning framework. By first predicting adsorption energies as auxiliary features, our ML model achieved improved sensitivity to rare, high-activity candidates. High-throughput screening of 261 transition metal single-atom-doped metal oxides (MSA-MOx) identified nine top-performing catalysts (theoretical overpotential < 0.5 V), including MnSA-RuO2 and FeSA-TiO2 (theoretical overpotential < 0.3 V). Data mining revealed key theoretical descriptors governing OER activity, while electronic structure analysis pinpointed intermediate binding strength as the key performance driver. Further constant-potential DFT calculations and experimental evaluation of MnSA-RuO2 confirmed its low overpotential and excellent durability under acidic conditions. This integrated framework, which connects theoretical modeling, machine learning prediction, and experimental validation, accelerates the discovery of efficient OER catalysts and provides mechanistic insights for the rational design of materials in sustainable energy technologies.

Progressive Learning‐Guided Discovery of Single‐Atom Metal Oxide Catalysts for Acidic Oxygen Evolution Reaction

The oxygen evolution reaction (OER) is a key bottleneck in clean energy conversion due to sluggish kinetics and high overpotentials. Transition metal single‐atom catalysts offer great promise for OER optimization thanks to their high atomic efficiency and tunable electronic structures. However, intrinsic scaling relationships between adsorbed intermediates limit catalytic performance and complicate discovery through conventional machine learning (ML). To overcome this, we combined density functional theory (DFT) with a progressive learning strategy within an active learning framework. By first predicting adsorption energies as auxiliary features, our ML model achieved improved sensitivity to rare, high‐activity candidates. High‐throughput screening of 261 transition metal single‐atom‐doped metal oxides (MSA‐MOx) identified nine top‐performing catalysts (theoretical overpotential &lt; 0.5 V), including MnSA‐RuO2 and FeSA‐TiO2 (theoretical overpotential &lt; 0.3 V). Data mining revealed key theoretical descriptors governing OER activity, while electronic structure analysis pinpointed intermediate binding strength as the key performance driver. Further constant‐potential DFT calculations and experimental evaluation of MnSA‐RuO2 confirmed its low overpotential and excellent durability under acidic conditions. This integrated framework, which connects theoretical modeling, machine learning prediction, and experimental validation, accelerates the discovery of efficient OER catalysts and provides mechanistic insights for the rational design of materials in sustainable energy technologies.

Artificial Solid Electrolyte Interphase for Sodium Metal Batteries: Mechanistic Insights and Design Strategies

As the transition to renewable energy accelerates, sodium metal batteries have emerged as a viable and economical substitute for lithium‐ion technology. The unstable solid electrolyte interphase on sodium metal anodes continues to provide a significant challenge to attaining long‐term cycle stability and safety. Natural solid electrolyte interphase layers frequently demonstrate inadequate mechanical integrity and deficient ionic conductivity, resulting in dendritic formation, diminished Coulombic efficiency, and capacity degradation. Creating artificial solid electrolyte interphases has emerged as an essential remedy to address these restrictions. This review offers an extensive analysis of artificial solid electrolyte interphases techniques for sodium metal batteries, emphasizing their creation mechanisms, material selection, and structural design. The research highlights the significance of fluoride‐based materials, multi‐layered solid electrolyte interphase structures, and polymer composites in mitigating dendrite development and improving interfacial stability. Advanced characterization techniques, including microscopy and spectroscopy, are emphasized for examining the microstructure and ion transport properties of artificial solid electrolyte interphases layers. Additionally, density functional theory simulations are examined to forecast ideal material compositions and ion migration paths. This study seeks to inform future developments in artificial solid electrolyte interphases engineering to facilitate enhanced performance, safety, and market viability of sodium metal batteries. Artificial solid electrolyte interphases facilitate next‐generation sustainable energy storage systems through new interface designs and integrated analysis.

Evaluation of macro and meiobenthic community structure and distribution in the hybrid ocean thermal energy conversion discharge area of Port Dickson

Over the past two decades, the technology underlying Hybrid Ocean Thermal Energy Conversion (H-OTEC) power plants have progressively matured. These advancements position H-OTEC as a promising alternative energy source with significant potential to replace traditional power plants. The cold discharge from H-OTEC Pilot Plants reduces the temperature of receiving water bodies, thereby directly or indirectly impacting the marine ecological environment. A one-year study was conducted around a pilot-stage 1.0 MW H-OTEC Pilot Plant in Port Dickson to investigate the effects of cold discharge on macro- and meiobenthic communities across different seasons. Apart from the water temperature within a 5-meter range affected by the H-OTEC cold discharge, the impact on other water quality indicators is negligible. A total of 22 macrobenthic species belonging to 4 phyla and meiobenthic organisms belonging to 9 taxa were identified across 15 sampling points. This study demonstrated that cold emissions had a limited impact on the abundance and community structure of benthic organisms across different seasons. The abundance of benthic organisms exhibited a significant increase in Inter-monsoon, followed by a significant decrease in Dry season. Moreover, there was a positive correlation observed between the abundance of benthic organisms and the content of water temperature, conductivity, gavel, sediment pigments and total organic matter. This study identified significant seasonal variations in the structure of both macro- and meio-benthic communities. Specifically, Umbonium vestiarium and other gastropoda were the dominant taxa and primary contributors to the observed significant changes in the structure of macro- and meio-benthic communities, respectively. Nevertheless, further study with a higher discharge volume of the outfall is crucial to assist in the outfall pipe placement of the mega-scale OTEC electricity plant. This study provided crucial insights into the ecological impacts of cold emissions from H-OTEC Pilot Plants in tropical coastal areas.

Interpretable tree-based ensemble models for macroscopic characterization of biofuel sprays

The growing energy demand and strict emission policies are driving the exploration of sustainable fuels for internal combustion engines. Biofuels from vegetable oils and waste food are promising candidates for future advanced combustion technologies. Since fuel spray significantly impacts engine performance and emissions, optimizing fuel injection systems requires a thorough understanding of spray characteristics. This study leverages machine learning (ML) techniques to characterize biofuel sprays, addressing the complexities and costs of experimental setups. Using a dataset from direct imaging of sprays, tree-based models like extreme gradient boosting (XGBoost) and random forest (RF) are trained with fuel properties and operating conditions. Spray characterization focuses on spray penetration, cone angle, area, and velocity, with fuels varying by density, viscosity, cetane number, and caloric value. Injection pressure is fixed at 1800 bar, with analysis at chamber temperatures of 25°C and 100°C over a 600 µs injection duration. The dataset of 850 samples is split 4:1 for training and testing, with model performance assessed using coefficient of determination ( R 2 ) and mean squared error (MSE). The optimized XGBoost model achieves the highest performance, with R 2 and MSE values of 0.961 and 37.308, respectively. This study demonstrates ML’s effectiveness in analyzing biofuel spray variations, paving the way for injector designs that enhance engine efficiency, reduce emissions, and support environmental sustainability.

Advancing Next-Gen Energy Storage with Single-Atom Materials.

Single-atom materials (SAMs) are a fascinating class of nanomaterials with exceptional catalytic properties, offering immense potential for energy storage and conversion. This work explores their advantages, challenges, and underlying mechanisms, providing valuable insights for rational design. By precisely controlling active sites, SAMs enable efficient charge and energy transfer, ultimately enhancing system performance. In applications such as metal-ion batteries, supercapacitors, metal anodes, Li-S batteries, Na-S batteries, and metal-air batteries, SAMs effectively address key challenges, including volume change, dendrite formation, and capacity fading. Their unique electronic and structural properties also make them highly efficient electrocatalysts, demonstrating remarkable activity and selectivity in lithium polysulfide, oxygen reduction, and carbon dioxide reduction reactions. Finally, the challenges and future prospects of SAMs in the energy storage field are discussed. With ongoing research and development, SAMs are poised to revolutionize the field, serving as foundational elements in the transition to sustainable and clean energy.

Methanol Electrochemical Upgrading to Formate for Energy Applications.

The electrochemical oxidation of methanol to formate (MTF) has emerged as a promising route for sustainable chemical production and energy storage. Despite its potential, the development of efficient MTF systems faces significant challenges, including insufficient mechanistic understanding and suboptimal catalyst design. This review highlights recent advances in MTF electrocatalysis, focusing on three key aspects: 1) reaction mechanisms at molecular level, 2) rational catalyst design strategies, and 3) practical applications in energy systems. Critical factors governing catalytic performance, including active site engineering, intermediate stabilization, and reaction pathway modulation are systematically analyzed. The integration of MTF with hydrogen production and carbon utilization technologies is also discussed as a potential approach for sustainable energy cycles. Finally, current limitations are identified in product selectivity and system efficiency, while proposing future research directions to advance this field. This work provides valuable insights for developing next-generation electrocatalysts and optimized MTF processes.

Integrated MPPT and bidirectional DC DC converter with reduced switch multilevel inverters for electric vehicles applications

The necessity for a clean and sustainable Renewable Energy Source (RES) is fueled by the intensifying environmental issue and steady decline of fossil resources. Additionally, expanding use of Electric Vehicles (EVs) across the globe is a result of rising carbon emissions and oil consumption. PV powered EV charging system has the ability to substantially reduce greenhouse emissions when compared with conventional sources-based EV charging system. However, existing PV based EV charging systems lack efficient approaches for adapting optimally to varying environmental conditions. Moreover, the power conversion efficiency may not be optimized leading to lower energy output. Hence, in this work, a Single Ended Primary Inductance Converter (SEPIC) Integrated Isolated Flyback Converter (SIIFC) and Machine Learning Radial Basis Function Neural Network Maximum Power Point Tracking (ML RBFNN MPPT) are used to maximize PV power extraction. EV motor and the grid are powered by a reduced switch 31 level inverter and a 1 Voltage Source Inverter (VSI). In order to effectively synchronize the grid voltage and guarantee that the EV motor runs at the desired speed, an adaptive proportional integral (PI) controller is used. For validating the effectiveness of proposed PV based EV charging station, MATLAB simulations and experimental validations are used. Experimental results demonstrate that the proposed SIIFC and RBFNN MPPT offer an efficiency of 95.4% and 96% respectively. Moreover, the proposed 31-level inverter design increases the reliability and reduces the THD to 2.16%.

Hydrogen leakage localization technology in hydrogen refueling stations combining RL and hidden Markov models

With the global energy structure shifting towards clean and efficient hydrogen energy, the safety management issues of hydrogen refueling stations are becoming increasingly prominent. To address these issues, a hydrogen leak localization algorithm for hydrogen refueling stations based on a combination of reinforcement learning and hidden Markov models is proposed. This method combines hidden Markov model to construct a probability distribution model for hydrogen leakage and diffusion, simulates the propagation probability of hydrogen in different grid cells, and uses reinforcement learning to achieve fast and accurate localization of hydrogen leakage events. The outcomes denoted that the training accuracy reached 95.2%, with an F1 value of 0.961, indicating its high accuracy in hydrogen leak localization. When the wind speed was 0.8 m/s, the mean square error of the raised method was 0.03, and when the wind speed was 1.0 m/s, the mean square error of the raised method was 0.04, proving its good robustness. After 50 localization experiments, the proposed algorithm achieves a localization success rate of 93.7% and an average computation time of 42.8 s, further demonstrating its high accuracy and computational efficiency. The proposed hydrogen leakage location algorithm has improved the accuracy and efficiency of hydrogen leakage location, providing scientific basis and technical guarantee for the safe operation of future hydrogen refueling stations.

Sustainable Production of Biofuels from Lignocellulosic Biomass Using Microbial Applications: Status, Challenges and Prospects.

The global pursuit of sustainable and renewable energy sources has intensified interest in biofuel production from lignocellulosic biomass. There are challenges for achieving sustainable biofuel production and utilization of lignocelluloses through microbial applications. The feedstock selection, pretreatment techniques, enzymatic hydrolysis, fermentation, and purification are important considerations from a sustainability perspective. Non-edible biomass sources, including agricultural and forest residues, are highlighted for their potential to reduce competition with food crops and minimize environmental impacts. Various pretreatment methods are explored for their efficacy in breaking down the complex lignocellulosic structure and enhancing enzymatic accessibility. Advances in enzyme technologies, metabolic engineering, and microbial biotechnology have significantly improved the efficiency of enzymatic hydrolysis and fermentation processes, resulting in increased sugar release and higher biofuel yields. The review emphasizes sustainability aspects, including energy security, reduced greenhouse gas emissions, and the utilization of renewable resources, in the context of microbial applications. However, overcoming technical and economic challenges, scaling up production, and ensuring commercial viability require further research and development. Continual advancements in microbial processes, coupled with innovation and comprehensive sustainability assessments, hold substantial promise for the sustainable production of biofuels from lignocellulosic biomass, contributing to a greener and more resilient energy future.

The Opportunity for Utilizing End‐of‐Life Scrap to Meet Growing Copper Demand

ABSTRACTAs electrification trends and clean energy deployment drive up copper demand, there will be pressure on copper supply chains. With annual copper demand expected to grow by 50% and reach 49 Mt by 2035, the world will continue to need additional sources of copper supply. While expanding mining projects could increase copper production, given the significant stock of material, secondary copper can play a vital role in meeting demand. We analyze the opportunity to meet growing copper demand via increased scrap collection and improved technical recycling efficiencies. We use an economic model of the global copper system—with China analyzed separately from the rest of the world—to quantify supply evolution by incorporating price feedback between demand and supply. The model quantifies the impact of the increased collection on the displacement of mining production and demonstrates how increasing recycling can modulate supply risks and copper prices. Aligned with recent literature on future copper flows, we find that there is an opportunity to increase scrap supply in 2040 by 46% (6.3 Mt) compared with the baseline.

Exploring green hydrogen production from the Jebba Hydropower Station for Nigeria's clean energy transition

ABSTRACT Green hydrogen (GH2) production from hydroelectricity could enhance clean energy transition. However, hydroclimatic variability could impact the hydropower generation and reliability of GH2 production. To evaluate this opportunity, we use statistical analysis methods to (i) analyze trends and correlations in Jebba dam's hydroclimatic variables and energy generation, (ii) translate the annual and quarterly energy generation into hydrogen using five scenarios, (iii) estimate the re-electrification potential, and (iv) quantify the amount of petrol that could be replaced, and the CO2 and CO emissions that would be prevented. The trend analysis shows that hydropower generation has increased significantly in the station. The estimated GH2 production from the first scenario indicated that the highest potential was 59,111 t and had a re-electrification potential of 1,182 GWh, which could replace 0.224 million liters of petrol, preventing 0.52 million kg of CO2 and 0.92 thousand kg of CO emissions in the year 2021. The study concludes that hydroclimatic variability influences hydropower generation, which showed a linear relationship with GH2 production. While we demonstrate that hydropower to hydrogen could serve as a long-storage solution and contribute to achieving the country's fossil fuel replacement goal and rural re-electrification, many more dams would be needed to achieve substantial contributions.

Recent Progress and Prospects in Organic Solar Cells Processed with Non-halogenated Solvents.

Against the backdrop of the global energy transition and sustainable development initiatives, organic solar cells (OSCs) have emerged as a promising clean energy technology that requires urgent transition to environmentally benign manufacturing processes. From environmental, health, and safety (EHS) perspectives coupled with industrial scalability requirements, there exists a critical need to replace hazardous halogenated solvents, like chloroform (CF) which is conventionally employed in high-performance OSC fabrication, with more environmentally friendly non-halogenated alternatives. Current challenges in room-temperature processing using non-halogenated solvents primarily stem from three interrelated factors: inadequate solubility of photoactive materials, excessive molecular aggregation, and disordered stacking morphology, all of which collectively degrade device performance. This review systematically examines the processing of non-halogenated solvents for OSCs in the following three critical aspects: First, this review focuses on the classification and selection of processing solvents and the impact on the morphology of the active layer. Subsequently, we categorize and analyze recent progress in photoactive material design (particularly small molecule acceptors (SMAs)) and device engineering strategies that enhance OSC processability in non-halogenated solvents. Finally, we propose the challenges for the OSCs towards more environmentally friendly processing and prospects for future applications.

Multi-purpose optimization with response surface methodology of plastic waste cables to clean energy with graphene nanoparticles.

Multi-purpose optimization with response surface methodology of plastic waste cables to clean energy with graphene nanoparticles.

Implementation of advanced control with artificial intelligence in anhydrous ethanol production

The growing demand for cleaner energy and the reduction of greenhouse gas emissions have highlighted anhydrous ethanol as a key renewable fuel due to its applications as a fuel, gasoline additive, and biodiesel reactant. Traditional dehydration via azeotropic distillation with cyclohexane is energy-intensive and environmentally harmful, prompting the search for safer alternatives. This study modeled and simulated the ethanol dehydration process using monoethylene glycol in Aspen Plus v12.1, implementing conventional control strategies optimized by Luyben’s method, which identified tray 31 as the system’s most sensitive point. Dynamic simulations demonstrated process robustness, with rapid recovery from disturbances in feed and heat duty, maintaining 99.999 wt% ethanol purity and solvent recovery. Additionally, artificial intelligence (AI) models—specifically decision tree, random forest, and LightGBM—were developed to control top product composition using easily measurable variables. These models significantly outperformed linear regression, with the decision tree achieving R² = 0.9970, MAE = 4.19 × 10⁻⁷, and RMSE = 2.20 × 10⁻⁶, maintaining ethanol molar fractions above 0.9996 even under dynamic disturbances. Despite their strong performance, the industrial adoption of AI-based controllers is still limited. However, with the implementation of real-time validation, significant advancements in computational processing capacity, and improvements in techniques to prevent overfitting, artificial intelligence models can become a promising tool for industrial process control.Graphical abstract

Recent Advancements in Nanomaterial-Decorated Biomass-Derived Carbon for High-Performance Supercapacitor Applications.

In the 21st century, the depletion of non-renewable energy sources has driven the pursuit of sustainable, efficient, and environmentally friendly energy conversion and storage devices to meet the growing global demands. Electrochemical supercapacitors have emerged as the leading choice owing to their high energy density, long durability, and eco-friendly nature. This review highlights the potential of nanomaterial-decorated biomass-derived carbon (BDC) as an advanced electrode material in high-performance supercapacitors (SCs). BDC is generally recognized as a good SC material. When nanomaterials, including heteroatoms, metal compounds, and conducting polymers, are introduced into BDC, they enhance their properties, making it a compelling choice due to its renewable nature, abundant availability, remarkable surface area, and excellent electrochemical performance. Based on these advantages, this study explores various synthesis methods and strategies to optimize the specific power, durability, and electrochemical efficiency of nanomaterial-decorated BDC, as BDC is a cost-effective precursor. These attributes make BDC a promising candidate for sustainable energy storage. Additionally, this review addresses current challenges and proposes innovative approaches to overcome them, offering new directions for future research and industrial development. This work underscores the role of nanomaterial-decorated BDC in advancing eco-friendly, high-performance energy storage technologies for a sustainable future.

Photocatalytic Aqueous Reforming of Methyl Formate.

Green hydrogen is critical to establish a sustainable energy future as it offers a clean, renewable, and a versatile alternative for decarbonizing industries, transportation, and power generation. However, the limitations of current methods significantly restrict the scope and hinder many of the envisioned applications. This study aims to report on the first example of a 3d-metal-based (Cu) heterogeneous photocatalytic system to produce green hydrogen via dehydrogenation of methyl formate (MF), a reaction previously known to require 4d/5d transition metals. Employing a Cu-based atomically dispersed heterogeneous photocatalyst supported on aryl-amino-substituted graphitic carbon nitride (d-gC3N4), the protocol offers numerous key advantages, including the recyclability of the photocatalyst for >10 cycles without significant activity loss, sustained hydrogen production (>15 days!) with high hydrogen yield (19.8 mmol gcat -1) and negligible CO emission, following an operationally simple, sustainable, and efficient catalytic pathway. Furthermore, the photocatalyst is characterized (using HAADF-STEM, SS-NMR, XAS, EPR, and XPS), all of which clearly demonstrated the presence of single atomic Cu-site. Additionally, comprehensive mechanistic investigations together with DFT calculations allow for a thorough mechanistic rationale for this reaction. It is strongly believed that this atomically dispersed heterogeneous photocatalytic approach will open new avenues for establishing liquid organic hydrogen career (LOHC) technologies.

Joule-Heating-Synthesized Iodine-Nitrogen Metal-Free Nanofiber for H2O2 Electroproduction via a Coordination Microenvironment Regulation Strategy.

The increasing demand for hydrogen peroxide (H2O2) necessitates greener production methods. In response, we developed an iodine-nitrogen-codoped metal-free carbon catalyst using the coordination microstructure regulation strategy, combined with rapid Joule heating for precise iodine and nitrogen doping. This approach allows precise control over the formation of active iodine-nitrogen coordination sites, significantly enhancing the catalytic performance for H2O2 production via the two-electron oxygen reduction reaction. Among the synthesized catalysts, I-N4 demonstrated superior catalytic activity, achieving a high H2O2 selectivity of 90-96% and a production rate of 1265 mg L-1 h-1 at -0.4 V vs reversible hydrogen electrode. Operando Raman spectroscopy confirmed the dynamic evolution of intermediates during the reaction, while chronoamperometric tests showed long-term stability. This scalable, energy-efficient synthesis method offers significant potential for sustainable energy and environmental applications.

Comprehensive review of artificial intelligence applications in renewable energy systems: current implementations and emerging trends

As the world faces pressing climate and energy challenges, Artificial Intelligence is proven as a transformative force in advancing renewable energy systems. This study reviews the current and future applications of Artificial Intelligence in renewable energy, highlighting its transformative role in enhancing the efficiency, reliability, and scalability of renewable energy systems. The study draws from over 400 recent publications, selected based on their relevance to Artificial Intelligence and renewable energy systems. We discuss the use of Artificial Intelligence techniques including machine learning, deep learning, and reinforcement learning models for optimizing energy production, forecasting demand, predictive maintenance, and managing decentralized energy systems. Emerging fields such as quantum machine learning and Artificial Intelligence-augmented reality are also considered because of their potential to transform energy infrastructures. The survey reviews significant innovations in wind and solar energy, energy storage, and smart grid technologies, focusing on how Artificial Intelligence addresses challenges like intermittency and variability. Furthermore, we discuss the importance of big data, the Internet of Things, and real-time analytics in advancing Artificial Intelligence models, along with the evolving landscape of Artificial Intelligence-driven policy and market modeling for renewable energy adoption. Real-world case studies, like Google’s collaboration with DeepMind for optimizing wind energy output and Australia’s National Electricity Market integrating Artificial Intelligence for grid stability, underscore the practical impact of Artificial Intelligence in renewable energy. This paper highlights challenges that are hindering Artificial Intelligence adoption in renewable energy systems and offers recommendations for improving the available technology to maximize Artificial Intelligence’s potential in promoting sustainable energy and addressing climate change.