Pub Date : 2026-01-13DOI: 10.1016/j.nxener.2025.100505
Tekalign Aregu Tikish , Yared Worku , Nithyadharseni Palaniyandy , Eno E. Ebenso
The growing demand for green energy has made energy storage crucial in energy generation systems. Supercapacitors (SCs) are gaining popularity in energy storage due to their high-power density and long cycle life. Bimetallic cobalt oxides (MCo2O4) are promising electrode materials due to their enhanced electrochemical performance and synergistic effects. This review provides a unique and exclusive focus on the recent 5-year progress (2020–2025) in MCo2O4 materials for SC applications. It provides a detailed analysis of various synthesis processes, the relationship between crystal structure (particularly the stable spinel structure) and electrochemical activity, the inherent battery-like charge storage mechanism of cobalt oxides, and a comparative performance evaluation. It also analyzes the electrolyte in Bimetallic Metal Oxides and their composites. The review highlights the strategic inclusion of a secondary metal (M = Ni, Cu, Fe, Mn, Zn) into cobalt oxide, which enhances key metrics, including specific capacitance, rate capability, and cyclic stability. Furthermore, this review demonstrated the strategies for improving overall SC performance through composite formation with conductive additives (carbon materials, metal oxides, conducting polymers, and MOFs). Lastly, the review concludes by summarizing the advanced and outlining crucial future research pathways to guide the development of superior bimetallic cobalt oxide-based SCs.
{"title":"Recent advances in bimetallic-cobalt oxides and their composites as a potential candidate for supercapacitor electrode material","authors":"Tekalign Aregu Tikish , Yared Worku , Nithyadharseni Palaniyandy , Eno E. Ebenso","doi":"10.1016/j.nxener.2025.100505","DOIUrl":"10.1016/j.nxener.2025.100505","url":null,"abstract":"<div><div>The growing demand for green energy has made energy storage crucial in energy generation systems. Supercapacitors (SCs) are gaining popularity in energy storage due to their high-power density and long cycle life. Bimetallic cobalt oxides (MCo<sub>2</sub>O<sub>4</sub>) are promising electrode materials due to their enhanced electrochemical performance and synergistic effects. This review provides a unique and exclusive focus on the recent 5-year progress (2020–2025) in MCo<sub>2</sub>O<sub>4</sub> materials for SC applications. It provides a detailed analysis of various synthesis processes, the relationship between crystal structure (particularly the stable spinel structure) and electrochemical activity, the inherent battery-like charge storage mechanism of cobalt oxides, and a comparative performance evaluation. It also analyzes the electrolyte in Bimetallic Metal Oxides and their composites. The review highlights the strategic inclusion of a secondary metal (M = Ni, Cu, Fe, Mn, Zn) into cobalt oxide, which enhances key metrics, including specific capacitance, rate capability, and cyclic stability. Furthermore, this review demonstrated the strategies for improving overall SC performance through composite formation with conductive additives (carbon materials, metal oxides, conducting polymers, and MOFs). Lastly, the review concludes by summarizing the advanced and outlining crucial future research pathways to guide the development of superior bimetallic cobalt oxide-based SCs.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100505"},"PeriodicalIF":0.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145950282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.nxener.2025.100496
Binita Kumari , Dipanjali Ray , Ganeshkumar D. Rede , Soumik Ray , Shiwani Tiwari , Pradeep Mishra
This study aims to forecast methane (CH₄) and nitrous oxide (N₂O) emissions from cattle rearing in India, which contribute significantly to agricultural greenhouse gas (GHG) emissions. Data on these emissions was collected from the Food and Agricultural Organization for the years 1961–2022. Three time series models, namely, exponential smoothing (Holt-Winters), autoregressive integrated moving average (ARIMA), and trigonometric seasonality, Box-Cox transformation, ARMA errors, trend, and seasonal components (TBATS) were employed to predict future emissions. The dataset was partitioned into training (1961–2012) and testing (2013–2022) sets to evaluate model performance. Diagnostic metrics, including Akaike Information Criterion, root mean square error, mean absolute percentage error, and mean absolute scaled error, were used to assess accuracy. Results indicated that the ARIMA model outperformed the other 2 forecasting models by making over 90% accurate predictions. For N₂O, ARIMA (0,1,0) was identified as the optimal model, while ARIMA (2,1,2) was selected for CH₄. Thus, the study validates the use of ARIMA model in GHG forecasting. The study projects emissions up to 2030, providing critical insights for policymakers to design targeted mitigation strategies. The study also presses the need for implementing sustainable cattle management practices for cutting emissions in India.
{"title":"Predicting methane and nitrous oxide emissions from Indian cattle farming using advanced time series techniques","authors":"Binita Kumari , Dipanjali Ray , Ganeshkumar D. Rede , Soumik Ray , Shiwani Tiwari , Pradeep Mishra","doi":"10.1016/j.nxener.2025.100496","DOIUrl":"10.1016/j.nxener.2025.100496","url":null,"abstract":"<div><div>This study aims to forecast methane (CH₄) and nitrous oxide (N₂O) emissions from cattle rearing in India, which contribute significantly to agricultural greenhouse gas (GHG) emissions. Data on these emissions was collected from the Food and Agricultural Organization for the years 1961–2022. Three time series models, namely, exponential smoothing (Holt-Winters), autoregressive integrated moving average (ARIMA), and trigonometric seasonality, Box-Cox transformation, ARMA errors, trend, and seasonal components (TBATS) were employed to predict future emissions. The dataset was partitioned into training (1961–2012) and testing (2013–2022) sets to evaluate model performance. Diagnostic metrics, including Akaike Information Criterion, root mean square error, mean absolute percentage error, and mean absolute scaled error, were used to assess accuracy. Results indicated that the ARIMA model outperformed the other 2 forecasting models by making over 90% accurate predictions. For N₂O, ARIMA (0,1,0) was identified as the optimal model, while ARIMA (2,1,2) was selected for CH₄. Thus, the study validates the use of ARIMA model in GHG forecasting. The study projects emissions up to 2030, providing critical insights for policymakers to design targeted mitigation strategies. The study also presses the need for implementing sustainable cattle management practices for cutting emissions in India.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"10 ","pages":"Article 100496"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.nxener.2025.100498
Ankur Srivastava, Arunabh Meshram
The valorisation of aluminium dross for hydrogen production offers a sustainable waste-to-energy pathway. This study examines hydrogen evolution from aluminium dross in aqueous NaOH and KOH (0.25–1.0 M) at 40–70 °C, measuring it as a function of time and using 3D contour mapping to determine initial rates and cumulative yields. Hydrogen evolution rises with alkali concentration and temperature; NaOH produces higher rates and volumes at lower conditions, while KOH exhibits steadier temperature-driven increases. The kinetic study reveals that the Avrami-Erofeyev model provides the best fit to the experimental data, showing excellent linearity (R2 = 0.95–0.99). The calculated reaction orders (n = 0.88–1.23) indicate a near first-order behaviour consistent with nucleation-growth mechanisms. Arrhenius analysis gives activation energies of 63.07 kJ/mol (0.5 M NaOH) and 73.62 kJ/mol (0.5 M KOH), highlighting differing mechanistic regimes. This work frames dross recycling as a strategy to convert hazardous residue into low-carbon fuel, advocating integration of such benign hydrogen-generation routes into industrial design and policy to close material loops and enhance resilience.
铝渣用于制氢的增值提供了一个可持续的废物转化能源的途径。本研究考察了在40-70 °C条件下，铝渣在NaOH和KOH水溶液（0.25-1.0 M）中的析氢，测量了其作为时间的函数，并使用3D等高线映射来确定初始速率和累积产率。析氢量随碱浓度和温度的升高而升高；NaOH在较低的条件下产生更高的速率和体积，而KOH则表现出稳定的温度驱动增长。动力学研究表明，Avrami-Erofeyev模型与实验数据拟合最佳，线性良好（R2 = 0.95-0.99）。计算的反应阶数（n = 0.88-1.23）表明接近一级的行为符合成核-生长机制。Arrhenius分析得出活化能分别为63.07 kJ/mol（0.5 M NaOH）和73.62 kJ/mol（0.5 M KOH）。本研究将垃圾回收作为一种将有害残留物转化为低碳燃料的策略，倡导将这种良性的产氢路线纳入工业设计和政策中，以关闭材料循环并增强弹性。
{"title":"Waste-to-hydrogen production: Recycling aluminium dross in alkali solutions","authors":"Ankur Srivastava, Arunabh Meshram","doi":"10.1016/j.nxener.2025.100498","DOIUrl":"10.1016/j.nxener.2025.100498","url":null,"abstract":"<div><div>The valorisation of aluminium dross for hydrogen production offers a sustainable waste-to-energy pathway. This study examines hydrogen evolution from aluminium dross in aqueous NaOH and KOH (0.25–1.0 M) at 40–70 °C, measuring it as a function of time and using 3D contour mapping to determine initial rates and cumulative yields. Hydrogen evolution rises with alkali concentration and temperature; NaOH produces higher rates and volumes at lower conditions, while KOH exhibits steadier temperature-driven increases. The kinetic study reveals that the Avrami-Erofeyev model provides the best fit to the experimental data, showing excellent linearity (R<sup>2</sup> = 0.95–0.99). The calculated reaction orders (n = 0.88–1.23) indicate a near first-order behaviour consistent with nucleation-growth mechanisms. Arrhenius analysis gives activation energies of 63.07 kJ/mol (0.5 M NaOH) and 73.62 kJ/mol (0.5 M KOH), highlighting differing mechanistic regimes. This work frames dross recycling as a strategy to convert hazardous residue into low-carbon fuel, advocating integration of such benign hydrogen-generation routes into industrial design and policy to close material loops and enhance resilience.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"10 ","pages":"Article 100498"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.nxener.2025.100497
Oyinbonogha Fred Agonga , Norazila Othman , Mohd Fairus Mohd Yasin
This study introduces a hybrid Computational Fluid Dynamics–Neural Network (CFD–NN) framework for real-time prediction of longitudinal thermo-acoustic instabilities in hydrogen-fueled gas turbine combustors—critical for future clean energy systems. A high-fidelity two-dimensional CFD model simulated hydrogen combustion and provided time-resolved pressure and heat release data. The Local Rayleigh Index (LRI) identified probe X2 as a strong instability zone (LRI ≈ 10⁷ Pa·W), while X1, X4, and X6 showed stable behavior. A feedforward neural network trained on early-stage data from probe X3 achieved high prediction accuracy (R² = 0.9998, Root Mean Square Error (RMSE = 924)) and delivered predictions ∼676× faster than CFD (∼334 predictions/s). By combining physics-based modeling with machine learning, this hybrid method enables real-time, physics-informed diagnostics, supporting smart combustor design and closed-loop control in next-gen hydrogen turbines.
{"title":"A hybrid CFD-neural network framework for the early prediction of longitudinal thermo-acoustic instabilities in hydrogen-fueled gas turbine combustors","authors":"Oyinbonogha Fred Agonga , Norazila Othman , Mohd Fairus Mohd Yasin","doi":"10.1016/j.nxener.2025.100497","DOIUrl":"10.1016/j.nxener.2025.100497","url":null,"abstract":"<div><div>This study introduces a hybrid Computational Fluid Dynamics–Neural Network (CFD–NN) framework for real-time prediction of longitudinal thermo-acoustic instabilities in hydrogen-fueled gas turbine combustors—critical for future clean energy systems. A high-fidelity two-dimensional CFD model simulated hydrogen combustion and provided time-resolved pressure and heat release data. The Local Rayleigh Index (LRI) identified probe X<sub>2</sub> as a strong instability zone (LRI ≈ 10⁷ Pa·W), while X<sub>1</sub>, X<sub>4</sub>, and X<sub>6</sub> showed stable behavior. A feedforward neural network trained on early-stage data from probe X3 achieved high prediction accuracy (R² = 0.9998, Root Mean Square Error (RMSE = 924)) and delivered predictions ∼676× faster than CFD (∼334 predictions/s). By combining physics-based modeling with machine learning, this hybrid method enables real-time, physics-informed diagnostics, supporting smart combustor design and closed-loop control in next-gen hydrogen turbines.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"10 ","pages":"Article 100497"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.nxener.2025.100499
Yanqiu Zhang
The escalating energy crisis and climate change necessitate a rapid transition to renewable energy, with solar photovoltaic (PV) technology emerging as a pivotal solution. This paper highlights the dual role of solar PV in reducing carbon emissions and enhancing energy security. It outlines the rapid, policy-driven expansion of solar PV in China, where installed capacity increased significantly from 43 GW in 2015 to 887 GW in 2024. A comparative analysis of PV deployment patterns and trends in the United States, India, and Brazil is also provided. The study further points out several challenges pertaining to geopolitical risks within critical mineral supply chains, the sustainable recycling of end-of-life PV modules, land-use conflicts, grid management, and durability that may impede the sustainability of solar PV. Furthermore, corresponding policy incentives, technological innovation, diversifying solar PV supply chains, multilateral cooperation, and circular principles are discussed to overcome these challenges; strategic integration of desert deployment, floating PV systems, and agrivoltaics exemplifies a spatially diversified approach to resolving land-energy conflicts. The paper further proposes a comprehensive sustainability framework for solar PV, emphasizing that factors such as maintaining policy stability and adaptive regulatory frameworks, assessments of lifecycle environmental impacts, and ensuring justice and equity in the energy transition are pivotal to achieving long-term sustainability. By aligning technological advancements with adaptive policies, solar PV can transition from exponential growth to sustainability, offering a viable pathway toward global carbon neutrality and resilient energy systems.
{"title":"Solar PV in the 21st century: Aligning technological growth with sustainability","authors":"Yanqiu Zhang","doi":"10.1016/j.nxener.2025.100499","DOIUrl":"10.1016/j.nxener.2025.100499","url":null,"abstract":"<div><div>The escalating energy crisis and climate change necessitate a rapid transition to renewable energy, with solar photovoltaic (PV) technology emerging as a pivotal solution. This paper highlights the dual role of solar PV in reducing carbon emissions and enhancing energy security. It outlines the rapid, policy-driven expansion of solar PV in China, where installed capacity increased significantly from 43 GW in 2015 to 887 GW in 2024. A comparative analysis of PV deployment patterns and trends in the United States, India, and Brazil is also provided. The study further points out several challenges pertaining to geopolitical risks within critical mineral supply chains, the sustainable recycling of end-of-life PV modules, land-use conflicts, grid management, and durability that may impede the sustainability of solar PV. Furthermore, corresponding policy incentives, technological innovation, diversifying solar PV supply chains, multilateral cooperation, and circular principles are discussed to overcome these challenges; strategic integration of desert deployment, floating PV systems, and agrivoltaics exemplifies a spatially diversified approach to resolving land-energy conflicts. The paper further proposes a comprehensive sustainability framework for solar PV, emphasizing that factors such as maintaining policy stability and adaptive regulatory frameworks, assessments of lifecycle environmental impacts, and ensuring justice and equity in the energy transition are pivotal to achieving long-term sustainability. By aligning technological advancements with adaptive policies, solar PV can transition from exponential growth to sustainability, offering a viable pathway toward global carbon neutrality and resilient energy systems.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"10 ","pages":"Article 100499"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.nxener.2025.100493
Sanjay R. Kumbhar , Sanjay T. Satpute , Yogesh S. Patil
India has emerged as one of the global frontrunners in renewable energy (RE) deployment, driven by ambitious national targets, progressive policies, and rapid technological adoption. This study presents a comprehensive, data-driven assessment of India’s RE transition between 2010 and 2025, integrating secondary datasets from the Ministry of New and Renewable Energy, Central Electricity Authority, International Energy Agency, and National Institution for Transforming India Aayog. Using compound annual growth rate (CAGR), correlation, and trend analysis, the study evaluates the performance and inter-sectoral dynamics of solar, wind, hydro, and bioenergy segments. The findings reveal that India’s total installed renewable capacity increased from 17 GW in 2010–190 GW in 2025, with solar energy exhibiting the highest growth (CAGR ≈ 24.8%), followed by wind (≈ 10.3%). Regression analysis indicates a strong positive correlation (r = 0.91) between gross domestic product growth and renewable capacity expansion, emphasizing the sector’s economic significance. A novel contribution of this research lies in its multi-dimensional analytical framework, combining policy mapping, financial trends, and comparative benchmarking against Brazil, Russia, India, China, and South Africa nations to identify structural bottlenecks and feasible interventions. Key challenges such as grid integration, financing constraints, and intermittency are prioritized through a risk–impact matrix, while opportunities in green hydrogen, artificial intelligence/internet of things integration, offshore wind, and export potential are evaluated using a strengths, weaknesses, opportunities, and threats-based market feasibility model. The study concludes that achieving India’s 500 GW non-fossil target by 2030 requires annual investments exceeding USD 25–30 billion, regulatory harmonization, and digital optimization of energy systems. By synthesizing quantitative insights with policy analysis, this paper bridges the gap between descriptive reviews and empirical assessments, offering actionable guidance for policymakers, investors, and researchers engaged in India’s RE transition.
{"title":"Harnessing green power: A comprehensive analysis of India's renewable energy growth and future outlook","authors":"Sanjay R. Kumbhar , Sanjay T. Satpute , Yogesh S. Patil","doi":"10.1016/j.nxener.2025.100493","DOIUrl":"10.1016/j.nxener.2025.100493","url":null,"abstract":"<div><div>India has emerged as one of the global frontrunners in renewable energy (RE) deployment, driven by ambitious national targets, progressive policies, and rapid technological adoption. This study presents a comprehensive, data-driven assessment of India’s RE transition between 2010 and 2025, integrating secondary datasets from the Ministry of New and Renewable Energy, Central Electricity Authority, International Energy Agency, and National Institution for Transforming India Aayog. Using compound annual growth rate (CAGR), correlation, and trend analysis, the study evaluates the performance and inter-sectoral dynamics of solar, wind, hydro, and bioenergy segments. The findings reveal that India’s total installed renewable capacity increased from 17 GW in 2010–190 GW in 2025, with solar energy exhibiting the highest growth (CAGR ≈ 24.8%), followed by wind (≈ 10.3%). Regression analysis indicates a strong positive correlation (r = 0.91) between gross domestic product growth and renewable capacity expansion, emphasizing the sector’s economic significance. A novel contribution of this research lies in its multi-dimensional analytical framework, combining policy mapping, financial trends, and comparative benchmarking against Brazil, Russia, India, China, and South Africa nations to identify structural bottlenecks and feasible interventions. Key challenges such as grid integration, financing constraints, and intermittency are prioritized through a risk–impact matrix, while opportunities in green hydrogen, artificial intelligence/internet of things integration, offshore wind, and export potential are evaluated using a strengths, weaknesses, opportunities, and threats-based market feasibility model. The study concludes that achieving India’s 500 GW non-fossil target by 2030 requires annual investments exceeding USD 25–30 billion, regulatory harmonization, and digital optimization of energy systems. By synthesizing quantitative insights with policy analysis, this paper bridges the gap between descriptive reviews and empirical assessments, offering actionable guidance for policymakers, investors, and researchers engaged in India’s RE transition.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"10 ","pages":"Article 100493"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This review explores the role of nanoadditives in improving the performance of biodiesel used in compression ignition (CI) engines. The central idea is that introducing nanoparticles (NPs) into biodiesel can enhance combustion, engine efficiency, and emission control without requiring significant engine modifications. The study focuses on widely used NPs such as Al₂O₃, TiO₂, CeO₂, Fe₃O₄, carbon nanotubes, and graphene oxide, examining their catalytic, thermal, and stabilization effects. Biodiesel blends are typically prepared from non-edible oils, purified, and infused with NPs through ultrasonication, sometimes with surfactants to maintain dispersion stability. Key fuel properties, including viscosity, density, calorific value, and oxidation stability, are assessed before engine testing. The review highlights how NPs improve fuel atomization, oxidation reactions, and heat transfer, leading to better ignition and more efficient combustion. Results from the literature show that nanoadditives enhance brake thermal efficiency, reduce fuel consumption, and significantly lower emissions of carbon monoxide, UHCs, and particulate matter. Oxygen-donating NPs like CeO₂ and TiO₂ promote complete combustion and soot reduction, while carbon-based NPs strengthen blend stability and atomization quality. The novelty of this review lies in its systematic analysis of the mechanisms, physicochemical improvements, and performance outcomes of nanoadditives in biodiesel. It also identifies key research gaps, including optimal NP dosage, long-term durability, and large-scale engine validation, offering valuable direction for sustainable CI engine development.
{"title":"Survey of cleaner combustion in compression ignition engine fueled with nanoadditive-laded biodiesel","authors":"Priyanka Singh , Nathi Ram Chauhan , Ajay Singh Verma","doi":"10.1016/j.nxener.2025.100500","DOIUrl":"10.1016/j.nxener.2025.100500","url":null,"abstract":"<div><div>This review explores the role of nanoadditives in improving the performance of biodiesel used in compression ignition (CI) engines. The central idea is that introducing nanoparticles (NPs) into biodiesel can enhance combustion, engine efficiency, and emission control without requiring significant engine modifications. The study focuses on widely used NPs such as Al₂O₃, TiO₂, CeO₂, Fe₃O₄, carbon nanotubes, and graphene oxide, examining their catalytic, thermal, and stabilization effects. Biodiesel blends are typically prepared from non-edible oils, purified, and infused with NPs through ultrasonication, sometimes with surfactants to maintain dispersion stability. Key fuel properties, including viscosity, density, calorific value, and oxidation stability, are assessed before engine testing. The review highlights how NPs improve fuel atomization, oxidation reactions, and heat transfer, leading to better ignition and more efficient combustion. Results from the literature show that nanoadditives enhance brake thermal efficiency, reduce fuel consumption, and significantly lower emissions of carbon monoxide, UHCs, and particulate matter. Oxygen-donating NPs like CeO₂ and TiO₂ promote complete combustion and soot reduction, while carbon-based NPs strengthen blend stability and atomization quality. The novelty of this review lies in its systematic analysis of the mechanisms, physicochemical improvements, and performance outcomes of nanoadditives in biodiesel. It also identifies key research gaps, including optimal NP dosage, long-term durability, and large-scale engine validation, offering valuable direction for sustainable CI engine development.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"10 ","pages":"Article 100500"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.nxener.2025.100506
Naeem Ullah, Tufail Ahmad, Asad Ullah, Sufaid Khan, Muhammad Nafees, Mehboob Ali, Yousra Noor, Fawad Ahmad Khan, Baseena Sardar, Majid Khan
Supercapacitors (SCs) are critical for sustainable energy storage due to their high power density and rapid charge-discharge capabilities, making them essential for renewable energy integration and electric vehicle applications. This study explores the solvothermal synthesis of spinel ferrites XFe2O4 (X = Mn, Co, Ni) as electrode materials for SCs. Structural characterization through X-ray diffraction confirmed phase-pure cubic structures with lattice parameters of 0.851 nm (MnFe2O4), 0.839 nm (CoFe2O4), and 0.834 nm (NiFe2O₄), and crystallite sizes of 13.72 nm, 20.72 nm, and 11.86 nm, respectively. Scanning electron microscopy revealed agglomerated nanoparticles for MnFe2O4 and CoFe2O4, and densely packed aggregates for NiFe2O4. Fourier-transform infrared spectroscopy identified a conductive carbonaceous layer from residual ethylene glycol, while UV-Vis spectroscopy determined bandgaps of 2.7 eV (CoFe2O4), 3.12 eV (MnFe2O4), and 3.7 eV (NiFe2O4). Electrochemical assessments using cyclic voltammetry, galvanostatic charge-discharge, and electrochemical impedance spectroscopy showed CoFe2O4 achieving a specific capacitance of 1518 F/g at 0.5 A/g with 99.9% retention after 5000 cycles, outperforming MnFe2O4 and NiFe2O4. Symmetric devices based on CoFe2O4 delivered a specific capacitance of 668 F/g at 1 A/g, an energy density of 33.38 Wh/kg, and a power density of 150 W/kg. These results position CoFe2O4 as a promising material for next-generation SCs, advancing energy storage for sustainable systems.
超级电容器（SCs）由于其高功率密度和快速充放电能力，对可持续能源存储至关重要，使其成为可再生能源集成和电动汽车应用的必要条件。本研究探讨了溶剂热合成尖晶石铁氧体XFe2O4 （X = Mn, Co, Ni）作为SCs电极材料的方法。通过x射线衍射表征，确定了相纯立方结构，晶格参数分别为0.851 nm （MnFe2O4）、0.839 nm （CoFe2O4）和0.834 nm （NiFe2O₄），晶粒尺寸分别为13.72 nm、20.72 nm和11.86 nm。扫描电镜显示，MnFe2O4和CoFe2O4为球状纳米颗粒，而NiFe2O4为密集堆积的团聚体。傅里叶变换红外光谱在残余乙二醇中发现了导电碳质层，紫外可见光谱测定了2.7 eV （CoFe2O4）、3.12 eV （MnFe2O4）和3.7 eV （NiFe2O4）的带隙。利用循环伏安法、恒流充放电法和电化学阻抗谱进行的电化学评价表明，在0.5 a /g下，CoFe2O4的比电容达到1518 F/g，循环5000次后保持率达到99.9%，优于MnFe2O4和NiFe2O4。基于CoFe2O4的对称器件在1 a /g时的比电容为668 F/g，能量密度为33.38 Wh/kg，功率密度为150 W/kg。这些结果将CoFe2O4定位为下一代超导材料的有前途的材料，推进可持续系统的能量存储。
{"title":"High-performance spinel ferrites for supercapacitors: Solvothermal synthesis and electrochemical evaluation","authors":"Naeem Ullah, Tufail Ahmad, Asad Ullah, Sufaid Khan, Muhammad Nafees, Mehboob Ali, Yousra Noor, Fawad Ahmad Khan, Baseena Sardar, Majid Khan","doi":"10.1016/j.nxener.2025.100506","DOIUrl":"10.1016/j.nxener.2025.100506","url":null,"abstract":"<div><div>Supercapacitors (SCs) are critical for sustainable energy storage due to their high power density and rapid charge-discharge capabilities, making them essential for renewable energy integration and electric vehicle applications. This study explores the solvothermal synthesis of spinel ferrites XFe<sub>2</sub>O<sub>4</sub> (X = Mn, Co, Ni) as electrode materials for SCs. Structural characterization through X-ray diffraction confirmed phase-pure cubic structures with lattice parameters of 0.851 nm (MnFe<sub>2</sub>O<sub>4</sub>), 0.839 nm (CoFe<sub>2</sub>O<sub>4</sub>), and 0.834 nm (NiFe<sub>2</sub>O₄), and crystallite sizes of 13.72 nm, 20.72 nm, and 11.86 nm, respectively. Scanning electron microscopy revealed agglomerated nanoparticles for MnFe<sub>2</sub>O<sub>4</sub> and CoFe<sub>2</sub>O<sub>4</sub>, and densely packed aggregates for NiFe<sub>2</sub>O<sub>4</sub>. Fourier-transform infrared spectroscopy identified a conductive carbonaceous layer from residual ethylene glycol, while UV-Vis spectroscopy determined bandgaps of 2.7 eV (CoFe<sub>2</sub>O<sub>4</sub>), 3.12 eV (MnFe<sub>2</sub>O<sub>4</sub>), and 3.7 eV (NiFe<sub>2</sub>O<sub>4</sub>). Electrochemical assessments using cyclic voltammetry, galvanostatic charge-discharge, and electrochemical impedance spectroscopy showed CoFe<sub>2</sub>O<sub>4</sub> achieving a specific capacitance of 1518 F/g at 0.5 A/g with 99.9% retention after 5000 cycles, outperforming MnFe<sub>2</sub>O<sub>4</sub> and NiFe<sub>2</sub>O<sub>4</sub>. Symmetric devices based on CoFe<sub>2</sub>O<sub>4</sub> delivered a specific capacitance of 668 F/g at 1 A/g, an energy density of 33.38 Wh/kg, and a power density of 150 W/kg. These results position CoFe<sub>2</sub>O<sub>4</sub> as a promising material for next-generation SCs, advancing energy storage for sustainable systems.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"10 ","pages":"Article 100506"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Renewable energy sources (RESs) hold a significant share in modern electrical networks, particularly in Microgrids (MGs). The inertia of the MG is significantly reduced due to the substitution of traditional synchronous generators with RESs. Frequency control of MG integrated with RESs is a challenging task. This research proposes a robust solution to enhance the frequency stability of an islanded MG by applying virtual inertia control (VIC) and damping strategies. A multistage proportional integral derivative (PID) ([PDF]-[1+PI]) controller optimized through a modified golf optimization algorithm (mGOA) in coordination with an energy storage system (ESS) is implemented as VIC. The mGOA algorithm performance is compared using various standard benchmark test functions with the original golf optimization algorithm (GOA) and with 10 other well-known optimization algorithms, particle swarm optimization, gravitational search algorithm, and genetic algorithm. To verify the effectiveness of the proposed mGOA algorithm, it is compared with the original GOA, grey wolf optimization (GWO), and whale optimization algorithm (WOA). It is demonstrated that the objective function value decreases by 53.07%, 56.01%, and 60.53% when compared with the original GOA, WOA, and GWO, respectively. The performance of the proportional derivative with filter (PDF)-(1+PI) controller was compared with that of conventional proportional integral (PI) controllers and PID controllers based on mGOA for random load fluctuation, parametric uncertainty, reduced capacity of ESS, and various renewable generation scenarios. The simulation result indicates that the mGOA-tuned multistage controller offers improved performance of 85.65% and 82.62% in terms of minimum objective function value in comparison to the mGOA-tuned PI and PID controllers, respectively. The performance of the proposed controller is evaluated under cyber attacks like false data injection attacks and denial of service attacks, as well as time latency. Performance of the proposed controller is tested by Hardware-In-The-Loop simulation, in OPAL-RT platform.
{"title":"Virtual inertia control for enhanced frequency stability in islanded microgrids: A multistage PID and modified golf optimization approach","authors":"Mihira Kumar Nath , N. Bhanu Prasad , Asini Kumar Baliarsingh","doi":"10.1016/j.nxener.2025.100503","DOIUrl":"10.1016/j.nxener.2025.100503","url":null,"abstract":"<div><div>Renewable energy sources (RESs) hold a significant share in modern electrical networks, particularly in Microgrids (MGs). The inertia of the MG is significantly reduced due to the substitution of traditional synchronous generators with RESs. Frequency control of MG integrated with RESs is a challenging task. This research proposes a robust solution to enhance the frequency stability of an islanded MG by applying virtual inertia control (VIC) and damping strategies. A multistage proportional integral derivative (PID) ([PDF]-[1+PI]) controller optimized through a modified golf optimization algorithm (mGOA) in coordination with an energy storage system (ESS) is implemented as VIC. The mGOA algorithm performance is compared using various standard benchmark test functions with the original golf optimization algorithm (GOA) and with 10 other well-known optimization algorithms, particle swarm optimization, gravitational search algorithm, and genetic algorithm. To verify the effectiveness of the proposed mGOA algorithm, it is compared with the original GOA, grey wolf optimization (GWO), and whale optimization algorithm (WOA). It is demonstrated that the objective function value decreases by 53.07%, 56.01%, and 60.53% when compared with the original GOA, WOA, and GWO, respectively. The performance of the proportional derivative with filter (PDF)-(1+PI) controller was compared with that of conventional proportional integral (PI) controllers and PID controllers based on mGOA for random load fluctuation, parametric uncertainty, reduced capacity of ESS, and various renewable generation scenarios. The simulation result indicates that the mGOA-tuned multistage controller offers improved performance of 85.65% and 82.62% in terms of minimum objective function value in comparison to the mGOA-tuned PI and PID controllers, respectively. The performance of the proposed controller is evaluated under cyber attacks like false data injection attacks and denial of service attacks, as well as time latency. Performance of the proposed controller is tested by Hardware-In-The-Loop simulation, in OPAL-RT platform.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"10 ","pages":"Article 100503"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.nxener.2025.100501
Gerald Enos Shija
The escalating challenges of non-agricultural accumulation and global energy demands underscore the need for innovative waste-to-energy solutions that mitigate environmental impacts and address food-fuel conflicts. This review advances the field by exploring non-agricultural biomass- municipal solid waste, forestry residues, industrial organic waste, algal biomass, textile waste, and invasive plant species as sustainable feedstocks for bioenergy production, supporting waste management, energy security, and circular bioeconomies. Their physicochemical properties, conversion technologies (pyrolysis, gasification, anaerobic digestion, and hydrothermal liquefaction), and challenges, like feedstock heterogeneity and high moisture content, are evaluated. Advanced pretreatments enhance conversion efficiencies, while technologies yield significant environmental benefits, including methane emission reductions and carbon sequestration. Socio-economic advantages include job creation, reduced fossil fuel dependency, and alignment with sustainable development goals for clean energy and sustainable cities. To address scalability gaps, this review introduces three novel contributions: (1) an AI-integrated urban biorefinery framework leveraging plasma gasification and AI-driven sorting to optimize heterogeneous feedstocks; (2) valorization strategies for understudied feedstocks like invasive species, enhancing bioenergy outputs through hybrid systems; and (3) scalable pathways tailored to urban and rural waste systems. Policy incentives, such as carbon taxes, are critical for economic viability, enabling these strategies to support global net-zero emissions goals by 2050 through sustainable waste-to-energy systems.
{"title":"Environmentally significant non-agricultural biomass for sustainable bioenergy: Sources, conversion, and environmental benefits","authors":"Gerald Enos Shija","doi":"10.1016/j.nxener.2025.100501","DOIUrl":"10.1016/j.nxener.2025.100501","url":null,"abstract":"<div><div>The escalating challenges of non-agricultural accumulation and global energy demands underscore the need for innovative waste-to-energy solutions that mitigate environmental impacts and address food-fuel conflicts. This review advances the field by exploring non-agricultural biomass- municipal solid waste, forestry residues, industrial organic waste, algal biomass, textile waste, and invasive plant species as sustainable feedstocks for bioenergy production, supporting waste management, energy security, and circular bioeconomies. Their physicochemical properties, conversion technologies (pyrolysis, gasification, anaerobic digestion, and hydrothermal liquefaction), and challenges, like feedstock heterogeneity and high moisture content, are evaluated. Advanced pretreatments enhance conversion efficiencies, while technologies yield significant environmental benefits, including methane emission reductions and carbon sequestration. Socio-economic advantages include job creation, reduced fossil fuel dependency, and alignment with sustainable development goals for clean energy and sustainable cities. To address scalability gaps, this review introduces three novel contributions: (1) an AI-integrated urban biorefinery framework leveraging plasma gasification and AI-driven sorting to optimize heterogeneous feedstocks; (2) valorization strategies for understudied feedstocks like invasive species, enhancing bioenergy outputs through hybrid systems; and (3) scalable pathways tailored to urban and rural waste systems. Policy incentives, such as carbon taxes, are critical for economic viability, enabling these strategies to support global net-zero emissions goals by 2050 through sustainable waste-to-energy systems.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"10 ","pages":"Article 100501"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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