Thermal Energy Storage (TES) systems play a crucial role in improving renewable energy utilization; however, the inherently low thermal conductivity of conventional phase change materials (PCMs) limits their charging and discharging performance. This review critically analyzes nano-enhanced phase change materials (NePCMs), where nanomaterials are integrated into PCMs to improve thermal transport and storage behavior. The reviewed studies demonstrate that carbon-based nanomaterials frequently provide the highest enhancements in thermal conductivity compared with other additives. For example, graphene-based composites have reported conductivity enhancements of about 150%, while carbon nanotube (CNT)-based systems exhibit improvements ranging from approximately 26% to 113% depending on loading and composite structure. Expanded graphite-based composites show even higher enhancement potential, with thermal conductivity increases reported up to 14.43 times relative to base materials in selected systems, although such improvements may be accompanied by reductions in latent heat capacity. Metal and metal-oxide nanoparticles also improve thermal performance, with studies reporting measurable conductivity enhancement and improved thermal stability depending on particle concentration and dispersion quality. This review synthesizes the influence of nanoparticle type, morphology, and concentration on thermophysical properties, phase stability, and thermal cycling performance across applications such as solar energy storage, HVAC systems, and industrial thermal management. Key challenges such as nanoparticle agglomeration, viscosity increase, latent heat reduction, and large-scale manufacturability are critically discussed. By consolidating quantitative performance trends and identifying material-specific trade-offs, this review provides clear guidance for selecting optimal NePCM formulations and outlines future research pathways toward high-efficiency, scalable, and economically viable TES systems.

Developing cost-effective and efficient bifunctional electrocatalysts for water splitting remains a significant challenge in sustainable energy research. Herein, we present the hydrothermal synthesis of nickel selenide (NiSe2) and copper-doped nickel selenide (NiCuSe2), followed by systematic evaluation of their electrocatalytic activity toward the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Introduction of copper into NiSe2 markedly reduces the overpotentials to 316 mV for HER and 248 mV for OER at 10 mA/cm2 and decreases the Tafel slopes, reflecting improved reaction kinetics. Morphological characterization shows that NiCuSe2 possesses a porous, nanogranular structure, which increases the electrochemically active surface area. Incorporation of copper into nickel selenide decreases overpotential values by modulating the electronic structure of NiSe2, thereby optimizing the free energy for hydrogen adsorption. In summary, the inclusion of copper within the NiSe2 lattice substantially modifies the electronic characteristics and enhances the exposure of electroactive sites, thereby improving catalytic efficiency. Moreover, the NiCuSe2 catalyst demonstrates excellent durability and low charge transfer resistance, underscoring its potential as a highly effective bifunctional electrocatalyst for water splitting in alkaline environments.

The depletion of fossil fuels and stricter emission norms have intensified the search for renewable and waste-derived CI engine fuels. Used transformer oil (UTO) biodiesel offers a low-cost alternative but suffers from poor atomization and combustion owing to its high viscosity and relatively low calorific value. This study experimentally evaluates the combined effects of hydrogen enrichment (5-15 LPM) and ZnO nano-additives (50, 100 ppm) on the performance and emissions of a CRDI engine fuelled with 20 % UTO biodiesel (UTO20). Results reveal that hydrogen and ZnO addition significantly enhance combustion efficiency: the optimum blend (UTO20 + 15 LPM H-2 + 100 ppm ZnO) achieved a 33.46 % BTE (+14.7 % vs diesel) and a 0.32 kg/kWh BSFC (-28.9 % vs UTO20). CO, HC, and smoke emissions declined by 45-54 %, though NOx rose by similar to 42 %. The study uniquely demonstrates the synergistic effects of catalytic ZnO and hydrogen enrichment on waste-derived biodiesel in a CRDI engine, achieving diesel-surpassing efficiency and reduced carbon emissions, thereby advancing pathways for sustainable CI engine operation.

Conventional active cooling systems depend on electricity, with high global warming risks, which affect Net-Zero Energy Buildings (NZEBs) goals. Whereas passive cooling strategies are energy-efficient, they remain constrained by climatic sensitivity, limited thermal regulation during peak loads, and short-term cooling effects. To overcome these limitations, nanotechnology has enabled the emergence of next-generation passive cooling techniques using Nano Cool Paints (NCPs) and Nano-enhanced Phase Change Materials (NePCMs). NCPs dispersed nanoparticles improve optical and thermophysical properties, increasing solar reflectance and mid-infrared emissivity to sustain radiative cooling under high solar flux. Whereas NePCMs improves thermal conductivity and energy storage potential compared with conventional PCMs. Subsequently, the current review critically analyses NCPs and NePCMs for NZEB, in specific to material design techniques, core mechanisms, system integration methods, and techno-economic feasibility. As follows, NCPs and NePCMs are emphasized for peak load reduction, continuous interior thermal control, dynamic comfort evaluation (PMV/PPD), and lifecycle environmental benefits across varied climatic zones. Furthermore, the study presents a combined architecture involving radiative and latent cooling strategies, along with defined thermal performance indicators, to facilitate NZEB. Additionally, the review offers a systematic integration framework, highlighting unresolved durability issues, standardization gaps, and policy hurdles that need to be addressed before widespread adoption of NCPs and NePCMs for NZEB.

Efficient thermal management is essential for ensuring the safety, performance, and lifespan of lithium-ion battery packs employed in electric vehicles (EVs). Among various battery thermal management approaches, immersion cooling has emerged as a promising solution owing to its superior heat dissipation capability and temperature uniformity. In the present work, a Computational Fluid Dynamics (CFD)-based comparative investigation was conducted to evaluate the thermal performance of three cooling fluids, namely water–glycol, Castrol DC 20, and 3 M Novec 7000, under immersion cooling conditions for cylindrical lithium-ion battery modules. Three-dimensional simulations were performed using ANSYS Fluent to analyze temperature distribution, heat transfer characteristics, and cooling effectiveness under identical operating conditions. The simulation results demonstrated that immersion cooling significantly enhanced battery thermal regulation compared to the no-cooling condition. The maximum battery cell temperature decreased from 330.24 K in the absence of cooling to 326.04, 321.00 and 312.60 K using water–glycol, Castrol DC 20, and 3 M Novec 7000, respectively. Similarly, battery pack temperature was reduced from 330.24 to 316.74, 310.80 and 304.32 K for the respective cooling fluids. Among the investigated coolants, 3 M Novec 7000 exhibited the highest Reynolds number (348,165) and convective heat transfer coefficient (1711 W m-2 K-1), resulting in superior heat removal capability despite its relatively lower thermal conductivity. The findings indicate that fluid hydrodynamics and convective heat transfer characteristics play a more significant role in immersion cooling performance than thermal conductivity alone. The study provides a comprehensive comparative assessment of commercially available dielectric fluids and establishes 3 M Novec 7000 as the most effective coolant for immersion-cooled EV battery thermal management systems. The outcomes offer valuable design insights for the development of advanced battery cooling technologies for next-generation electric vehicles.

The increasing global need for sustainable refrigeration has triggered a quest for Solar Adsorption Refrigeration Systems (SARS) that use solar energy and low-grade heat resources for environmentally sustainable cooling. This review focuses on the potential for SARS, an innovative alternative to traditional vapor-compression systems, to tackle pressing global problems such as energy transformation, global warming, and low-carbon cooling. The review covers an integrated analysis of experimental, simulation, and demonstration studies carried out for the past three decades, reviewing the performance, materials, and economic viability of SARS. Major findings include the importance of regular system maintenance, including solar collector cleaning, adsorbent monitoring, and replacement, for durability and efficient system operation. Among existing pairs, the zeolite 13X-water combination has the highest adsorption properties, heat tolerance, and lifespan for practical use. The major limitations still include low COPs (between 0.2 and 0.6) for low energy conversion, expensive costs, and zeolite degradation in different operation conditions for widespread adoption. The case studies support the electricity-consumption-reducing impacts (no <50 % in arid zones) for the use of SARS for both arid-zone and off-grid cooling, illustrating energy-sustained efficiency and enhanced global adaptability for future possibilities. The emphasis for future studies will continue to address gaps for techno-economic, cycle, and policy-scale support, accentuating further experimental efforts for advanced adsorbents, combination designs, and modeling algorithms to raise scalability and system efficiency for wider adoption. With global innovation, advancement, and supportive global policies, SARS could sustain along the global energy-climate transformation pathway for a sustainable future.

In response to the growing demand for efficient and sustainable energy storage solutions, various methods have been investigated, with thermal energy storage emerging as a particularly promising option. Among these, latent heat energy storage plays a pivotal role. This chapter provides a comprehensive overview of energy storage, with a focus on thermal energy storage and the significant advancements in phase change materials (PCMs). PCMs stand out for their ability to absorb and release latent heat during phase transitions, offering unique advantages in energy storage and retrieval. Researchers have explored a wide range of PCMs, from organic to inorganic compounds, each possessing distinct thermal properties tailored to specific applications. This chapter reviews the latest developments in PCM research, highlighting its potential in various sectors, including solar energy storage, building heating, ventilation and air conditioning system, and waste heat recovery. Despite the remarkable progress, challenges remain in fully understanding and optimizing phase change mechanisms. As research continues to unfold, PCMs are poised to play a critical role in unlocking sustainable energy solutions for the future.

The Sustainable Development Goals (SDGs) provide a global framework for addressing interconnected challenges in energy access, environmental sustainability, and social equity. Energy policy is central to this agenda because it shapes access, affordability, decarbonization, and the broader social and economic conditions required for sustainable development. This paper presents a structured narrative and policy-oriented review of the alignment between energy policies and the SDGs. The particular emphasis of this study is on SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action) while also examining links with equity, economic growth, governance, and sustainable consumption. Drawing on selected peer-reviewed studies and policy-relevant literature published from 1992 through early 2026, the review identifies recurring policy gaps, cross-regional differences, and major trade-offs in policy design and implementation. The paper moves beyond descriptive cataloguing by 1) comparing contradictory findings, 2) tracing the evolution of energy–SDG policy thinking, and 3) outlining future pathways for more coherent and inclusive governance. This review highlights the need for integrated policy frameworks, stronger institutional coordination, and context-sensitive strategies that can better align national energy planning with broader sustainability objectives.

Tin-based perovskite solar cells (TPSCs) offer a promising alternative for sustainable and lead-free photovoltaics. However, their low power conversion efficiency (PCE) and stability hinder their commercialisation. Finding the relations between fabrication parameters is challenging, which can affect the performance of the TPSCs. This work presents a novel integrated machine learning (ML) framework that combines two ML models to identify complex relationships among fabrication process parameters, thereby enhancing TPSC performance. Selecting a suitable ML model manually is difficult. This work developed a framework for selecting the best models based on the coefficient of determination (R-2). Random forest models outperformed all the regression models. These random forest models were trained on two experimental datasets of >160 features of the fabrication process to predict both PCE and stability accurately. A weighted combined pipeline was introduced to rank the device, unlike traditional single metric optimization. These models achieve high R-2 values of 0.84 for PCE and 0.87 for stability. Shapley additive explanations (SHAP) analysis reveals key features, including the antisolvent volume, perovskite precursors, and charge-transport-layer processing conditions. The combined score of distribution analysis and t-distributed stochastic neighbour embedding (t-SNE) visualization demonstrates the clustering of high-performance devices, suggesting reproducible fabrication pathways with enhanced PCE and stability. This work establishes a reproducible ML-driven framework for multi-objective optimization in TPSCs. Shifting from a PCE-centric design to a balanced dual-objective optimization provides actionable guidelines for fabricating TPSCs with enhanced PCE and stability.

Parabolic Trough Collectors (PTCs) are of pivotal importance in harnessing solar power for thermal energy. Nevertheless, the performance of PTCs has been largely impaired by thermal loss and inefficient solar irradiance absorption. The main objective of the present study was to propose a numerical simulation of a PTC system utilizing a novel hybrid nanofluid and new flow inserts. In the present study, the fundamental equations for mass, momentum, and energy conservation were numerically solved using the k-ε turbulence model. A second-order scheme was used in the numerical simulation. The mesh independence study was conducted, and the numerical simulation was validated using experimental measurements. The isolated and interaction effects of Al2O3; SiO2; TiO2 + MWCNT/water as nanofluids and compared them to twisted tape, corrugated tube, and fin-type inserts. Results showed that the TiO2 + MWCNT/water nanofluid coupled with a fin insert posted better thermal performance, with improvements by 29.34% and 20.40% in heat transfer over traditional water-based systems, supported by CFD simulations and experiments. In addition, the Nusselt number and friction factor peaks increased by 15.26% and 10.86%, and the size of the collector was minimized by 39.33%. The combined CFD-experimental approach developed during this study offers a dependable approach to nanofluid-based PTC optimization as well as useful design recommendations for high-efficiency solar thermal systems of the future.

Data centers face new cooling challenges due to the growth of digital infrastructure as well the rise in computing needs. This makes it urgent to find more sustainable ways to manage heat. To address this challenge, this proposed study surveys the chilled water-based hybrid cooling systems as a practical way to meet higher cooling demands while being environmentally responsible. To achieve this, the proposed research uses several methods: 1) it reviews cooling basics, 2) looks at recent technology, 3) studies control systems, and 4) examines real-world case studies. By reviewing current literature and industry examples, the proposed study highlights key performance and sustainability measures that could show the benefits of this choice towards hybrid cooling. Results show that combining water and air cooling with smart sensors, the Internet of Things (IoT), and artificial intelligence (AI) control can greatly improve energy efficiency. This would also make operations more resilient to climate change. The utilization of advanced chillers (chilled water-based hybrid cooling), heat exchangers, and phase-change materials helps transfer heat more efficiently, while using renewable energy can lower carbon emissions. Though there are challenges, such as saving water and working with older systems, the long-term savings and environmental gains make these hybrid systems more important for sustainable data centers. This proposed paper also offers useful insights for industry professionals who are working to adopt greener cooling solutions while keeping data centers reliable and high-performing.

Organic phase change materials (PCMs) are widely used for thermal energy storage (TES) applications. Nevertheless, the low thermal conductivity and low thermal stability hinder their use in TES applications. Hence, Hybrid nanoparticles are widely used to overcome the aforementioned problems due to their dual functionality. In this study, a facile method is employed to synthesize coconut shell waste particles that are mixed with MWCNT particles and developed into a hybrid nanoparticle with a 1:1 (coconut shell: MWCNT) ratio. The hybrid nanoparticle was dispersed with the RT50 to develop a composite via a two-step method. The primary purpose of using the hybrid nanoparticle with RT50 is to enhance a low thermal conductivity and thermal stability of base PCM. Coconut shell particles are highly porous and have a 3-D structure, as determined by SEM analysis, which helps improve the intermolecular bonding between the particles and the PCM, resulting in improved thermal stability. MWCNT is a highly conductive particle that helps improve phonon movement, thereby overcoming a low thermal conductivity of RT50. The experimental results of the research work demonstrated that the composite exhibits a 109.52 % increase in thermal conductivity and a 5.20 % increase in latent heat compared to the base PCM. Additionally, after conducting 500 thermal cycles, the composite shows negligible changes in its thermal properties. Based on the experimental results of the developed composites is suitable for the advanced accelerated heat transfer rate during use in the TES applications.

The integration of Machine Learning (ML) into energy storage systems (ESSs) represents a paradigm shift to enhance efficiency, reliability, and sustainability in the global energy sector. This comprehensive review systematically explores the widespread applications of advanced ML across different ESS technologies, including electrochemical, thermal, and mechanical systems. By highlighting significant advancements, this work clarifies the efficacy of ML in applications such as real-time monitoring, fault prediction, thermal management, battery state estimation, and optimization of thermophysical properties. In-depth case studies demonstrate significant improvements in operational performance, highlighting the potential of ML to accelerate materials discovery, optimize systems, and enable predictive maintenance.Furthermore, the study conducts a rigorous bibliometric analysis to map global research trends, influential contributors, and emergent themes at the intersection of ML and ESS. Despite the positive results, several key challenges have been identified, including data scarcity, model interpretability, computational complexity, and deployment within existing infrastructure. The review discusses these challenges in depth and provides practical suggestions to overcome them using advanced techniques such as physics-informed neural networks (PINNs), transfer learning, multimodal data fusion, and AutoML. The paper concludes by proposing a robust roadmap for future research, emphasizing open data initiatives, explainable AI frameworks, hybrid modeling approaches, and interdisciplinary collaboration. Ultimately, this review serves as an essential reference for researchers, industry practitioners, and policymakers aiming to leverage ML technologies to advance ESS in alignment with global sustainability and climate goals.

Phase change materials are recognized for their capability to absorb and release significant amounts of heat during phase transformations and demonstrate compact uniqueness in thermal management applications. To establish sustainable energy storage techniques, phase change materials research has shifted its attention more and more towards bio-based phase change materials (BPCMs) as an alternative to traditional ones. This paper critically investigates a cutting-edge approach that harnesses the potential of BPCMs in thermal energy storage. Besides preparing BPCMs for thermal energy storage applications, the authors also outline innovative methods for material preparation. In addition, the present study thoroughly investigated thermophysical properties and surface morphology to ensure BPCM’s incorporation and optimal performance in thermal energy storage applications. The bio-waste-derived materials within the organic and bio-based PCM matrix resulted in variations in thermal conductivity and heat storage enthalpy. The results demonstrated that the composites exhibited a significant improvement in thermal properties and latent heat as compared to the base PCM. Further, the current obstacles and research gaps associated with BPCMs are comprehensively analyzed, laying the groundwork for further research. Moreover, an in-depth overview of the environmental impacts of BPCMs, their challenges, and future directions is comprehensively presented

Waste agricultural residues can be turned into energy-rich syngas through biomass gasification, a sustainable way to generate energy. Despite progress, challenges remain in optimizing the gasification process and predicting how much syngas will be produced. This study combines a Support Vector Machine (SVM) machine learning approach with experimental analysis of rice husk gasification. In the laboratory, a custom-built fluidized bed gasifier operated at feed rates of 10 to 18 kg/h, reaching a maximum cold gas efficiency of 64 % and a low heating value (LHV) of 5.2 MJ/Nm³ at 740 °C. The syngas produced powered a 7.5 kW diesel engine in dual-fuel mode, replacing up to 68 % of diesel fuel without significant loss of efficiency. The SVM model proved highly accurate, with a mean absolute error of 0.15 MJ/m³ and an R-squared value of 0.93. This paper offers the first comprehensive validation of an integrated rice husk gasification system with machine learning as a control method, implemented on an operating dual-fuel engine. It was established that machine learning methods could enhance biomass power plants considerably, and that the utilization of syngas from rice husk gasification could reduce CO₂ emissions by approx 12.76 metric tons per year.

It is urgent to turn to the broader utilization of renewable energies instead of fossil fuels to effectively tackle this widely recognized challenge of transition to sustainable energy. The present study aims to provide insight into existing understanding and develop approaches toward advances in the working fluids, namely nanofluids, along with turbulators for enhancing heat transfer processes related to energy applications. It gives a general introduction with an overview of the existing information from the literature, then addresses outstanding issues for implementing new ideas in energy systems and solar collectors to enhance the heat transfer rate, efficiency measures, and design for the future. This visionary paper outlines the key hurdles to be conquered if such technologies significantly impact future sustainable energy systems. These are inclusively outlined as novel material development, performance enhancement, long-term stability, life cycle methodology, and cost reduction in implementing innovative technologies into large-scale industrial applications. The present work concludes with the design of a road map that integrates these advanced technologies into sustainable energy systems and identifies huge potential in these technologies to make considerable contributions towards the global transition towards renewable energy sources.

ABSTRACTThe pursuit of sustainable energy solutions is crucial in meeting global sustainable development goals (SDGs). This experimental study explores enhancing a box‐type solar cooker's thermal performance through the integration of a hybrid nano‐enhanced phase change material (PCM). Specifically, multi‐walled carbon nanotubes (MWCNTs) and silicon oxide (SiO2) nanoparticles were incorporated into the PCM at a 2% concentration, with 1% each of MWCNT and SiO2. The hybrid nano‐PCM was meticulously prepared using ultrasonication to ensure optimal dispersion and homogeneity. This innovative approach significantly improved the cooker's efficiency, achieving a peak PCM temperature of 128.9°C, a cooking power of 47.6 W, an average efficiency of 28.5%, and an energy efficiency of 6.2%. Notably, the cooking time was halved, from 36.3 min to just 18 min, demonstrating the ultrafast capabilities of the solar cooker. These findings underscore the potential of the MWCNT/SiO2 hybrid nano‐PCM in revolutionizing solar cooking technology, offering a cost‐effective, environmentally friendly, and highly efficient solution for sustainable energy harvesting.