Energy nexus最新文献

{"title":"Transport mechanism in chemically reactive nanofluid dynamics containing gyrotactic micro-organisms using artificial neural network approach","authors":"Saleem Nasir,&nbsp;Abdallah S. Berrouk,&nbsp;Kyriaki Polychronopoulou,&nbsp;Asim Aamir","doi":"10.1016/j.nexus.2026.100809","DOIUrl":"10.1016/j.nexus.2026.100809","url":null,"abstract":"<div><div>In many scientific and engineering fields, such as process engineering, energy systems, and biomedical applications, heat and mass transfer are crucial, as they significantly affect the effectiveness and performance of fluid-flow and transport-based systems. This study examines the effects of Cattaneo Christov heat flux, generalized Fick's law, thermal radiation, and gyrotactic microorganisms on three-dimensional flow, heat, and mass transmission in a microorganism-laden non-Newtonian fluid. To represent these coupled transport pathways, a thorough mathematical model of the Maxwell fluid is developed. The Levenberg Marquardt back-propagation artificial neural network (LMB-ANN) scheme is used to build a machine-learning-based framework that combines fluid dynamics with advanced computational intelligence to address this difficult problem. A high-fidelity reference dataset for LMB-ANN is created using the bvp4c numerical method, enabling precise predictions of velocity, temperature, concentration, and microbe profiles across variations in five important parameters across four parametric cases. The LMB-ANN for the designed fluid model is then trained and validated using the resultant dataset. With mean squared error (MSE) values as low as 4.3753E^-08, 2.007E^-09, 2.7109E^-06, 8.8966E^-10, 7.3867E^-11 at 1000, 650, 1000, 449, and 616 epochs, respectively. The correctness and resilience of the LMB-ANN approach are confirmed by the limited error histograms, near-ideal regression indices (R²=0.999998), and low MSE values. The LMB analysis indicates that reduced Nusselt and Sherwood numbers, which are largely determined by the <em>Pr</em> and <em>Sh</em>, are significant indicators of both qualitative and quantitative system performance. In industrial contexts where maximizing heat and mass transfer efficiency is a key design and operational goal, these findings are highly relevant.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"23 ","pages":"Article 100809"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148958824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Hazard Resilience Framework for DER-Integrated Distribution Systems with Hierarchical Coordination and Sustainable Energy Markets","authors":"Devareddy Harsha,&nbsp;Pabitra Kumar Biswas,&nbsp;Sudhakar Babu Thanikanti,&nbsp;T. Yuvaraj,&nbsp;Mohammad Khishe","doi":"10.1016/j.nexus.2026.100806","DOIUrl":"10.1016/j.nexus.2026.100806","url":null,"abstract":"<div><div>The increasing frequency of compound extreme weather events, such as cyclones and floods, significantly threatens the resilience of active distribution systems by causing cascading infrastructure failures, prolonged outages, and communication disruptions. This study presents a multi-stage cyber–physical restoration framework for enhancing the resilience of DER-integrated radial distribution systems (RDSs) under compound multi-hazard conditions. The framework integrates probabilistic wind–flood fragility modeling, hierarchical decentralized restoration, microgrid islanding, inter-zone network reconfiguration, battery energy storage systems (BESS), electric vehicles (EVs), blockchain-enabled peer-to-peer (P2P) energy trading, repair crew scheduling with accessibility constraints, and AC power flow validation. A multi-objective optimization model is formulated to simultaneously minimize Energy Not Served (ENS), operational cost, Conditional Value-at-Risk (CVaR), voltage deviation, blockchain latency, and restoration unfairness while maximizing DER trading profit. The optimization problem is solved using a Modified Mountain Gazelle Optimization Algorithm (MMGOA) with a Dynamic Adaptive Weight Updating Mechanism (DAWUM). Topology-aware blockchain zonal sharding provides secure market coordination, transaction validation, and settlement between resilience zones. The proposed hierarchical coordination framework provides a flexible way to prioritize the objectives and restore the large-scale distribution systems in a computationally efficient manner. The proposed framework is validated on a modified IEEE 118-bus RDS under three representative compound hazard scenarios and compared with MGOA, PSO, GWO, and DE. The results show that MMGOA achieves the lowest average ENS (0.48 MWh), highest cumulative DER trading profit (38.42 k$), best overall multi-objective function (0.372), highest Hypervolume (0.891), and lowest GD (0.041) and IGD (0.048). Furthermore, the blockchain framework maintains an average transaction latency of 214 ms with 96.3% confirmation reliability while demonstrating stable scalability from IEEE 33-bus to IEEE 118-bus systems. These results confirm that the proposed framework effectively improves restoration resilience, economic performance, cyber–physical coordination, and computational scalability, providing a practical solution for resilient DER-integrated active distribution systems under compound multi-hazard conditions.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"23 ","pages":"Article 100806"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148854051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling of higher heating value using Physico-chemical properties of biomass: Statistical and machine learning approaches with validation on Bangladeshi biomass","authors":"Kiron Akter,&nbsp;Dilshad Zahan Ethen,&nbsp;Md. Rostom Ali,&nbsp;Tumpa R. Sarker,&nbsp;Moutusee Amir Meem,&nbsp;Bristy Sarker","doi":"10.1016/j.nexus.2026.100737","DOIUrl":"10.1016/j.nexus.2026.100737","url":null,"abstract":"<div><div>The global shift toward renewable energy underscores the importance of biomass as a sustainable and carbon-neutral energy source. Bangladesh relies heavily on biomass for rural energy, and agricultural residues have a massive energy potential. Modelling of the Higher Heating Value (HHV) of biomass is an important and time-demanding issue to accurately predict energy content based on basic compositional information of biomass. This study aimed to develop precise empirical and machine learning (ML)-based models to predict the higher heating value of agricultural residues and herbaceous biomass using physicochemical properties. A dataset comprising 150 biomass samples, including proximate, ultimate, and combined compositional analyses with experimentally measured HHVs, was compiled from the literature and used for model development. Traditional multiple linear regression methods and advanced ML algorithms were applied, and their performances were assessed using R², root mean square error, mean absolute error, and mean absolute percentage error. The developed regression models demonstrated strong predictive capability, with the best-fitting regression models achieving R² of 92.7%, MAPE below 4%, and RMSE within 1.2. Among the ML approaches, non-linear ML models, particularly Random Forest, providing the highest predictive accuracy across proximate (R² = 89.7%), ultimate (R² = 91.3%), and combined (R² = 92.0%) datasets. Independent experimental validation was performed using seven biomass samples collected in Bangladesh, including rice husk, rice straw, wheat straw, maize residue, sugarcane bagasse, banana peel, and coconut husk. Comparison with published HHV prediction correlations demonstrated that the developed models provided improved predictive accuracy and lower prediction errors. The findings demonstrate that machine learning models, particularly ensemble methods such as Random Forest, provide a reliable, effective, and scalable alternative to conventional HHV determination methods, such as calorimetric measurements and can support biomass resource assessment, feedstock screening, bioenergy system design, and renewable energy planning. The developed models were trained using a globally sourced biomass database and validated using Bangladeshi biomass samples, demonstrating their potential applicability across diverse biomass resources and geographical regions.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"23 ","pages":"Article 100737"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148853969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy shocks and food price volatility: A multi-period CGE analysis, 1997–2023","authors":"Tetsuji Tanaka","doi":"10.1016/j.nexus.2026.100805","DOIUrl":"10.1016/j.nexus.2026.100805","url":null,"abstract":"<div><div>This study examines structural changes in the transmission of crude oil and agricultural supply shocks to food and headline consumer price indices by comparing four benchmark years: 1997, 2007, 2014, and 2023. We develop a stochastic multi-regional, multi-sectoral Computable General Equilibrium (CGE) model calibrated to four benchmark databases from the Global Trade Analysis Project (GTAP). The model introduces Monte Carlo simulations of productivity shocks to crude oil, oilseeds, and soybeans, with shock magnitudes calibrated from historical production volatility using EIA and FAOSTAT data. To identify the structural mechanisms behind changing price responses across the four benchmark years, we also combine the CGE simulations with a Leontief inverse-based cumulative input analysis that measures embodied petroleum dependency in food supply chains, including direct fuel use and indirect effects through transport and chemical inputs. Results reveal a dramatic structural shift: food CPI responsiveness to oil shocks surged between 1997 and 2007 by a factor of 3.53 in the USA and 4.74 in Brazil, driven by global deepening of cumulative petroleum dependency. Variance decomposition confirms oil shocks became the dominant contributor by 2007 relative to agricultural productivity shocks. Although 2023 data suggest emerging resilience in China and Brazil through reduced energy intensity, energy-food linkages remain significantly stronger than in the late 1990s, especially in resource-importing regions. Food price stability is thus increasingly contingent on the energy architecture of agricultural supply chains.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"23 ","pages":"Article 100805"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148854048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Thermal Performance Prediction of Triple-Diffusive Tangent Hyperbolic Nanofluid Flow with Activation Energy and Chemical Reaction Using Computationally Aided Advanced Neural Networks","authors":"Saleem Nasir,&nbsp;Ahmed Elkashef,&nbsp;Abdallah S. Berrouk,&nbsp;Zeeshan Khan,&nbsp;Asim Aamir","doi":"10.1016/j.nexus.2026.100810","DOIUrl":"10.1016/j.nexus.2026.100810","url":null,"abstract":"<div><div>Neural networks are frequently employed to address technical and real-world challenges due to their effectiveness and versatility. The current study investigates the intricate interaction between mixed convection, magnetohydrodynamic effects, and tangent hyperbolic nanofluid flow across an extended medium. It uses a sophisticated Gaussian neural network integrated with a hybrid Cuckoo Search method. The study examines and documents the behavior of model physical factors, revealing a direct correlation between buoyancy forces and flow dynamics, as well as an inverse correlation between solute transportation mechanisms and fluid temperature. Notable relationships were discovered with the Prandtl, Schmidt numbers, Soret, Brownian motion, and thermophoresis parameters. Also, water is treated as the base fluid flowing over a stretchable surface, incorporating <em>TiO₂</em> nanoparticles. The validity and precision of the suggested procedure are demonstrated by the essential statistical indicators that are emphasized, such as the Absolute Errors range as <span><math><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>12</mn></mrow></msup></math></span>to <span><math><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>05</mn></mrow></msup></math></span>, the Mean Squared Errors range as <span><math><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>07</mn></mrow></msup></math></span>to <span><math><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>02</mn></mrow></msup></math></span>, the Theil's Inequality Coefficient range as <span><math><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>07</mn></mrow></msup></math></span>to <span><math><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>01</mn></mrow></msup></math></span>, and the Error in Nash-Sutcliffe Efficiency values range as <span><math><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>15</mn></mrow></msup></math></span>to <span><math><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>05</mn></mrow></msup></math></span>. By implementing the recommended strategy, numerical computations become considerably more precise and accurate. In several fields, including energy production, environmental engineering, and chemical processing, ensuring optimal development performance is crucial.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"23 ","pages":"Article 100810"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148958823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Greenhouse gases, globalization, and energy use nexus: The heterogenous roles of tech-innovations in environmental sustainability of world’s top-3 emitters","authors":"Mahmut Sami Duran,&nbsp;Stephen Taiwo Onifade,&nbsp;Şeyma Bozkaya,&nbsp;Ilham Haouas","doi":"10.1016/j.nexus.2026.100811","DOIUrl":"10.1016/j.nexus.2026.100811","url":null,"abstract":"<div><div>Climate change exerts significant threat to global environmental sustainability. As such, curtailing greenhouse gases (GHG) to limit global average temperature below 1.5 °C has become a major climate agenda in the 21^st century. In this study, we examine how tech-innovation, renewable energy, and globalization shape this sustainability goal via a tripartite investigation of three important pollutants (2 GHG: carbon dioxide-CO₂ &amp; Methane-CH₄, and 1 harmful air pollutant: nitrogen dioxide-NO₂) among the world’s top 3 emitting countries, namely China, the USA, and India. FMOLS and DOLS empirical techniques help to control for income differences among the countries while simulating the long-run coefficients of the impacts of tech-innovation, renewable energy, and globalization on these pollutants within a three-phase model using data between 1990 and 2022. Additionally, Dumitrescu-Hurlin's approach reveals the direction of causality relationship in the model. Following the results, we obtained evidence of the environmental gains from tech-innovations and renewable energy as the duo significantly curtail both CO₂ and CH₄ emissions. On the contrary, the emission-triggering impacts of globalization were observed, thus showcasing the negative environmental side of globalization. This evidence was further reinforced by the bidirectional causality between globalization and NO₂. Hence, for the authorities of these top-3 pollutant-emitting countries, these findings are suggestive of the need for more actions to strategically bolster the integrated deployment of both tech-innovation and renewables to accelerate the progress toward SDG-13 of climate action.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"23 ","pages":"Article 100811"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148958822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Turning trash into power: bioenergy, economics, and carbon offsets from hydrothermal technologies","authors":"Chih-Chun Kung,&nbsp;Clovis Awah Che,&nbsp;Philippe M. Heynderickx","doi":"10.1016/j.nexus.2026.100807","DOIUrl":"10.1016/j.nexus.2026.100807","url":null,"abstract":"<div><div>Thermal decomposition of biomass is a carbon-negative renewable energy pathway to improve energy sustainability and mitigate climate change. Nevertheless, conventional thermal technologies, such as pyrolysis and co-firing, require drying feedstocks, which will significantly increase production costs. This study provides a pilot analysis of the conditions for advanced hydrothermal technology to be economically feasible for large-scale development and to what extent it substitutes for existing technologies. The energy conversion rate, processing modes, break-even condition, and uncertain market conditions of municipal solid waste, such as food waste, wood chips, and paper waste, are examined. Results indicate that (1) hydrothermal technology is relatively more cost-effective than pyrolysis and possibly reduces investment and operating risks of producers, (2) the use of hydrochar results in a significant trade-off between energy output and agricultural sustainability, (3) for food waste, 76.49% to 80.55% of profits comes from energy sales, whereas for wood chips and paper waste, 39.65–89.06% of profit comes from environmental benefits, (4) depending on hydrothermal modes, a 32.4%-54.7% decrease in energy output or energy price leads to loss-making conditions, and (5) profitability is relatively insensitive to changes in emission prices.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"23 ","pages":"Article 100807"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148854049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement learning for optimal power dispatch and reliability analysis of hybrid DG-PV-wind-battery and grid system in farming applications","authors":"chima tansi uwaezuoke,&nbsp;Nnamdi I. Nwulu","doi":"10.1016/j.nexus.2026.100808","DOIUrl":"10.1016/j.nexus.2026.100808","url":null,"abstract":"&lt;div&gt;&lt;div&gt;This paper proposes a reinforcement learning-based framework for optimal electrical power dispatch and reliability analysis in hybrid renewable energy systems designed for agricultural applications. The system integrates photovoltaic (PV) panels, wind turbines, battery energy storage, grid connectivity, and a diesel generator (DG) as a dispatchable backup source. Proximal Policy Optimisation (PPO) is employed to develop adaptive control strategies for these complex hybrid configurations. Five system configurations of increasing renewable integration complexity are systematically evaluated, ranging from a DG-Grid baseline (Case I) to a fully off-grid DG-PV-Wind-Battery arrangement (Case V), using a mathematical optimisation framework formulated by Gbadamosi and Nwulu (2020) as a comparative benchmark, implemented here via a MILP solver in AIMMS.&lt;/div&gt;&lt;div&gt;The power dispatch strategy adopts a weighted multi-objective formulation. A sensitivity analysis across six weighting scenarios identified optimal weights of 0.5 for system reliability, 0.2 for operational cost, and 0.3 for renewable energy penetration, corresponding to the configuration that simultaneously achieved the lowest LOLP (0.010) and the highest renewable generation (77.17 MWh/year) among all scenarios tested. Compared to the AIMMS MILP baseline, the PPO-based approach in the most comprehensive configuration (Case V) reduced operational cost from $3,186/year to $2,738 ± $566/year (a 14.1% reduction), increased renewable utilisation from 80.3 MWh/year to 86.5 ± 19.30 MWh/year (a 7.7% improvement), and reduced the Loss-of-Load Probability (LOLP) from 0.027 to 0.010 ± 0.003 (a 63.0% improvement). Across all five configurations, LOLP improvements ranged from 41.9% to 63.0% and LOLE improvements from 40.6% to 50.8%, with the Cost of Energy Not Served (CENS) reduced in four of the five cases. These absolute improvements, together with mean ± standard deviation metrics derived from 200 Monte Carlo simulation runs, provide a fully interpretable and statistically grounded measure of practical benefit.&lt;/div&gt;&lt;div&gt;Statistical robustness was ensured through 200 Monte Carlo simulation runs in which load demand and renewable resource availability were independently varied. Mean performance metrics and associated uncertainty bounds were computed across all simulation outcomes to confirm that reported improvements are not attributable to random variance.&lt;/div&gt;&lt;div&gt;The PPO agent was implemented using a three-layer feedforward neural network comprising two hidden layers of 128 neurons each with ReLU activation functions, and an output layer with a tanh policy head. Training was conducted using the Adam optimiser with a learning rate of 3 × 10⁻⁴, a discount factor (γ) of 0.99, a clipping ratio (ε) of 0.2, and a minibatch size of 64 over 2,000 time steps. Convergence was assessed through average episodic reward trajectories and policy stability metrics.&lt;/div&gt;&lt;div&gt;These findings demonstrate that r","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"23 ","pages":"Article 100808"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148854050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nanocatalysts for sustainable biodiesel production: Reaction parameters, catalyst performance, and future prospects","authors":"Molly Mlambo,&nbsp;Neha Anil,&nbsp;Bappi Paul","doi":"10.1016/j.nexus.2026.100812","DOIUrl":"10.1016/j.nexus.2026.100812","url":null,"abstract":"<div><div>The depletion of fossil fuel supplies and growing environmental concerns have accelerated the search for sustainable alternatives, such as biodiesel produced through the transesterification process. Even though biodiesel is renewable, biodegradable and environmentally friendly, conventional catalysts used in transesterification often result in low catalytic efficiency, difficult recovery, limited reusability, soap formation and high-energy requirement. The present review critically evaluates the role of nanocatalysts in overcoming these encounters, highlighting on the influence of particle size, surface functionalization, morphology, catalyst stability and reaction conditions in biodiesel production. It covers recent literature analysis which specifies that nanocatalysts significantly improve biodiesel synthesis owing to the particle size ranging from10–100 nm and an increased surface-to-volume ratio of 50–300 m²g⁻¹ as compared to (&lt;50 m²g⁻¹) from conventional catalysts size. This research reviews the methods in the synthesis of nanocatalysts, comparative analysis of nanocatalysts, catalytic performance in biodiesel production and effects on reaction parameters. It compares data from different papers and experiments in order to show the reaction kinetics that provide the best yield and energy efficiency. The review also deliberates on how metal oxide maintains yields above 90% in terms of performance in comparison to, magnetic, supported, bifunctional, immobilized enzymes and carbon-based nanocatalysts. It further outlines the number of cycles (&gt;3) the nanocatalysts can achieve because of the morphology and surface functionalization of the catalysts. Results show high biodiesel conversion from 90–99% under optimised relative conditions of (45- 65 °C, 1–3 h, 2–6 wt% catalyst loading, and oil-to-methanol ratio of 1:6 -1:15) and <strong>&gt;</strong>5 reusability cycles using nanocatalysts<strong>.</strong> This biodiesel yield is compared to conventional heterogeneous catalyst recording even below 90% yield, with longer reaction time of 3 h, high reaction temperatures up to 80 °C and high catalyst loadings above 6 wt% and low reusability (&lt;2 cycles). Despite these advantages, highlights pose emerging challenges including nanoparticle agglomeration, which lowers the reactive surface area by 20–60%, with biodiesel yield losses of 5–20% depending on catalyst composition and reaction conditions. Issues of catalyst leaching, high synthesis costs, scalability constraints and environmental concerns continue to limit commercial application. This review opens a gap for future perspectives to focus on development of hybrid nanocatalyst systems using cost-effective synthesis routes to enhance stability and reusability.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"23 ","pages":"Article 100812"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148958729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Measuring the climate–energy–land nexus: Integrating energy and land use transition costs in Indonesia’s net-zero pathway” [Energy Nexus 22 (100733) 2026]","authors":"Nur Firdaus,&nbsp;Akihisa Mori","doi":"10.1016/j.nexus.2026.100745","DOIUrl":"10.1016/j.nexus.2026.100745","url":null,"abstract":"","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"23 ","pages":"Article 100745"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148958730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}