Pub Date : 2026-04-01Epub Date: 2026-01-20DOI: 10.1016/j.nxener.2026.100513
Hüseyin Bakır
This study focuses on parameter identification of various solar cell (SC) and PV module configurations, including the single diode SC, double diode SC, STP6-120/36, STM6-40/36, and Photowatt-PWP201. In this direction, seven state-of-the-art metaheuristic algorithms, including dynamic fitness-distance balance-based LSHADE (dFDB-LSHADE), nonlinear marine predator algorithm (NMPA), hippopotamus optimization (HO), marine predators algorithm (MPA), walrus optimizer (WO), exponential distribution optimizer (EDO), and manta-ray foraging optimization (MRFO) are employed to extract the unknown model parameters based on voltage-current measurement data. The optimum configuration of the SC parameters is identified by minimizing the root mean square error (RMSE) between the simulated and measured cell currents. The effectiveness of the algorithms was tested through extensive experimentation, incorporating statistical analysis, convergence analysis, box plots, and model validation. The optimization findings show that the dFDB-LSHADE produced the lowest RMSE and the most accurate predictions for all SC models. The box plots and statistical metric results clearly demonstrate that dFDB-LSHADE is a robust and reliable method for the SC parameter identification problem.
{"title":"Elevating PV model performance: Accurate and reliable parameter extraction of solar cell models with state-of-art metaheuristic algorithms","authors":"Hüseyin Bakır","doi":"10.1016/j.nxener.2026.100513","DOIUrl":"10.1016/j.nxener.2026.100513","url":null,"abstract":"<div><div>This study focuses on parameter identification of various solar cell (SC) and PV module configurations, including the single diode SC, double diode SC, STP6-120/36, STM6-40/36, and Photowatt-PWP201. In this direction, seven state-of-the-art metaheuristic algorithms, including dynamic fitness-distance balance-based LSHADE (<em>d</em>FDB-LSHADE), nonlinear marine predator algorithm (NMPA), hippopotamus optimization (HO), marine predators algorithm (MPA), walrus optimizer (WO), exponential distribution optimizer (EDO), and manta-ray foraging optimization (MRFO) are employed to extract the unknown model parameters based on voltage-current measurement data. The optimum configuration of the SC parameters is identified by minimizing the root mean square error (RMSE) between the simulated and measured cell currents. The effectiveness of the algorithms was tested through extensive experimentation, incorporating statistical analysis, convergence analysis, box plots, and model validation. The optimization findings show that the <em>d</em>FDB-LSHADE produced the lowest RMSE and the most accurate predictions for all SC models. The box plots and statistical metric results clearly demonstrate that <em>d</em>FDB-LSHADE is a robust and reliable method for the SC parameter identification problem.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100513"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039415","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 : 2026-04-01Epub Date: 2026-01-23DOI: 10.1016/j.nxener.2026.100515
Lennart Mesecke , Ina Meyer , Sascha Brechelt , Niclas Zerner , Marco-Nicolas Galati , Kiran Prabha , Christian Schröder , Volker Wesling , Stefan Kaierle , Henning Ahlers , Roland Lachmayer
Climate change necessitates the expansion of renewable energy systems, and sustainably produced hydrogen plays an important role in this expansion. For widespread use, there is a need for efficient hydrogen storage technologies. Ammonia facilitates the reversible storage of hydrogen, with the conversion occurring in catalytic reactors. This review proposes an integrated approach to enhance the efficiency of catalytic reactors through multi-material additive manufacturing (MMAM). It includes material development, process technology, and component design for both directed energy deposition and powder bed fusion MMAM processes. In this review, the current state of the literature in these areas is summarized, and the research needs are identified.
{"title":"Integrated approach to additive manufacturing of multi-material components for ammonia decomposition reactors: A review","authors":"Lennart Mesecke , Ina Meyer , Sascha Brechelt , Niclas Zerner , Marco-Nicolas Galati , Kiran Prabha , Christian Schröder , Volker Wesling , Stefan Kaierle , Henning Ahlers , Roland Lachmayer","doi":"10.1016/j.nxener.2026.100515","DOIUrl":"10.1016/j.nxener.2026.100515","url":null,"abstract":"<div><div>Climate change necessitates the expansion of renewable energy systems, and sustainably produced hydrogen plays an important role in this expansion. For widespread use, there is a need for efficient hydrogen storage technologies. Ammonia facilitates the reversible storage of hydrogen, with the conversion occurring in catalytic reactors. This review proposes an integrated approach to enhance the efficiency of catalytic reactors through multi-material additive manufacturing (MMAM). It includes material development, process technology, and component design for both directed energy deposition and powder bed fusion MMAM processes. In this review, the current state of the literature in these areas is summarized, and the research needs are identified.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100515"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039416","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 : 2026-04-01Epub Date: 2026-01-20DOI: 10.1016/j.nxener.2026.100512
Gabriel M. Veith , Ethan D. Boeding , Rachel J. Korkosz , Khryslyn G. Araño , Yeyoung Ha , Chanaka Kumara , Cailin Duggan , Amanda L. Musgrove , Thomas Zac Ward , Robert L. Sacci , Beth L. Armstrong
This work focuses on the inclusion of an insoluble fugitive phase during slurry processing to form composite battery electrodes. The fugitive phases consist of natural derived products like alginic acid, sucrose, rice and potato starch, and carrageenans such as Irish Moss and synthetic pore-formers based on polymethyl methacrylate. The fugitive phases can be anaerobically thermally removed (350 °C) during binder crosslinking and electrode drying steps, resulting in electrodes with low tortuosities (approaching theoretical Bruggemann limits for spherical particles) and high porosities approaching 80%. The resulting ∼3 mg/cm2 loaded electrodes suffer from poor electrical connectivity, lowering the effective material utilization, but represent an approach that could be utilized for the formation of solid-state batteries with infilling of materials into well-defined pores and optimized transport pathways.
{"title":"Not all fugitives are bad: The case for using them to form low tortuosity - high porosity electrodes","authors":"Gabriel M. Veith , Ethan D. Boeding , Rachel J. Korkosz , Khryslyn G. Araño , Yeyoung Ha , Chanaka Kumara , Cailin Duggan , Amanda L. Musgrove , Thomas Zac Ward , Robert L. Sacci , Beth L. Armstrong","doi":"10.1016/j.nxener.2026.100512","DOIUrl":"10.1016/j.nxener.2026.100512","url":null,"abstract":"<div><div>This work focuses on the inclusion of an insoluble fugitive phase during slurry processing to form composite battery electrodes. The fugitive phases consist of natural derived products like alginic acid, sucrose, rice and potato starch, and carrageenans such as Irish Moss and synthetic pore-formers based on polymethyl methacrylate. The fugitive phases can be anaerobically thermally removed (350 °C) during binder crosslinking and electrode drying steps, resulting in electrodes with low tortuosities (approaching theoretical Bruggemann limits for spherical particles) and high porosities approaching 80%. The resulting ∼3 mg/cm<sup>2</sup> loaded electrodes suffer from poor electrical connectivity, lowering the effective material utilization, but represent an approach that could be utilized for the formation of solid-state batteries with infilling of materials into well-defined pores and optimized transport pathways.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100512"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039418","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 : 2026-04-01Epub Date: 2026-01-31DOI: 10.1016/j.nxener.2026.100518
Vitória N. Silva Oliveira, Rafaelle Gomes Santiago, Moises Bastos-Neto, Célio L. Cavalcante Jr., F. Murilo T. Luna
In the energy transition scenario, Power-to-X processes play a crucial role by converting surplus electricity from renewable sources into fuels, chemicals, and other energy carriers. These technologies not only help to balance the supply and demand of energy but also promote decarbonization. In this study, the conversion of carbon dioxide and hydrogen into methanol (Power-to-Methanol) as a strategic solution to store and transport hydrogen was evaluated by modeling and simulation. The investigation addressed the rate laws governing the reactions in the hydrogenation of pollutant gases into methanol. Refitted and original Bussche-Froment (BF) and Graaf kinetic models were used to understand and identify the key factors in process efficiency for improving competitiveness compared to conventional processes. The sensitivity analysis revealed some similarities in both models; however, discrepancies in conversion predictions reached up to 49%, particularly at intermediate residence times, low temperatures, and high pressures. Selecting an appropriate residence time (below 0.1 h) proved critical to reducing divergences between models, providing actionable insight for reliable process design and optimization. These discrepancies between the models contribute to a broad theoretical optimal operating window for the process. Considering both models, the methanol production and CO2 conversions were higher in temperatures between 200 and 250 °C. In this optimal temperature range, increasing the pressure contributed to higher methanol production. Increasing H2/CO2 ratio favored CO2 conversion, achieving an average for both models of 44% with a ratio of 8:1. However, a ratio of 3:1 for the Graaf model and 2:1 for the BF model resulted in maximum methanol production. Finally, increasing the CO concentration raised the obtained methanol concentration but resulted in lower carbon dioxide conversion.
{"title":"Power-to-methanol process assessment using enhanced kinetic models","authors":"Vitória N. Silva Oliveira, Rafaelle Gomes Santiago, Moises Bastos-Neto, Célio L. Cavalcante Jr., F. Murilo T. Luna","doi":"10.1016/j.nxener.2026.100518","DOIUrl":"10.1016/j.nxener.2026.100518","url":null,"abstract":"<div><div>In the energy transition scenario, Power-to-X processes play a crucial role by converting surplus electricity from renewable sources into fuels, chemicals, and other energy carriers. These technologies not only help to balance the supply and demand of energy but also promote decarbonization. In this study, the conversion of carbon dioxide and hydrogen into methanol (Power-to-Methanol) as a strategic solution to store and transport hydrogen was evaluated by modeling and simulation. The investigation addressed the rate laws governing the reactions in the hydrogenation of pollutant gases into methanol. Refitted and original Bussche-Froment (BF) and Graaf kinetic models were used to understand and identify the key factors in process efficiency for improving competitiveness compared to conventional processes. The sensitivity analysis revealed some similarities in both models; however, discrepancies in conversion predictions reached up to 49%, particularly at intermediate residence times, low temperatures, and high pressures. Selecting an appropriate residence time (below 0.1 h) proved critical to reducing divergences between models, providing actionable insight for reliable process design and optimization. These discrepancies between the models contribute to a broad theoretical optimal operating window for the process. Considering both models, the methanol production and CO<sub>2</sub> conversions were higher in temperatures between 200 and 250 °C. In this optimal temperature range, increasing the pressure contributed to higher methanol production. Increasing H<sub>2</sub>/CO<sub>2</sub> ratio favored CO<sub>2</sub> conversion, achieving an average for both models of 44% with a ratio of 8:1. However, a ratio of 3:1 for the Graaf model and 2:1 for the BF model resulted in maximum methanol production. Finally, increasing the CO concentration raised the obtained methanol concentration but resulted in lower carbon dioxide conversion.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100518"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079381","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 : 2026-04-01Epub Date: 2026-01-14DOI: 10.1016/j.nxener.2025.100509
Nadjia Khatir , Safia Nait-Bahloul
This study proposes an evidential fusion framework for classifying visual defects in solar panels using convolutional neural networks (CNNs) and Dempster-Shafer theory (DST). Three pretrained CNN models: ResNet50, MobileNetV2, and EfficientNetB0 are fine-tuned to detect multiple defect types, and their outputs are fused at the logit level using DST. Unlike conventional ensemble strategies such as majority voting, the proposed method explicitly accounts for uncertainty and conflict among predictions by assigning belief masses to sets of hypotheses. Experimental evaluations conducted on a multiclass solar panel dataset demonstrate that DST fusion consistently outperforms individual models and majority voting across all macro-averaged metrics, particularly in underrepresented or visually ambiguous classes such as Physical-Damage and Dusty. These findings underscore the potential of uncertainty-sensitive model fusion to enhance the robustness and interpretability of automated photovoltaic inspection systems.
{"title":"Evidential multi-model CNN integration for visual fault detection in solar panels","authors":"Nadjia Khatir , Safia Nait-Bahloul","doi":"10.1016/j.nxener.2025.100509","DOIUrl":"10.1016/j.nxener.2025.100509","url":null,"abstract":"<div><div>This study proposes an evidential fusion framework for classifying visual defects in solar panels using convolutional neural networks (CNNs) and Dempster-Shafer theory (DST). Three pretrained CNN models: ResNet50, MobileNetV2, and EfficientNetB0 are fine-tuned to detect multiple defect types, and their outputs are fused at the logit level using DST. Unlike conventional ensemble strategies such as majority voting, the proposed method explicitly accounts for uncertainty and conflict among predictions by assigning belief masses to sets of hypotheses. Experimental evaluations conducted on a multiclass solar panel dataset demonstrate that DST fusion consistently outperforms individual models and majority voting across all macro-averaged metrics, particularly in underrepresented or visually ambiguous classes such as <em>Physical-Damage</em> and <em>Dusty</em>. These findings underscore the potential of uncertainty-sensitive model fusion to enhance the robustness and interpretability of automated photovoltaic inspection systems.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100509"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981498","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 : 2026-04-01Epub Date: 2026-01-29DOI: 10.1016/j.nxener.2026.100517
Umang Bedi
Fuel cells are gaining popularity as sustainable alternatives to traditional energy sources due to their low environmental impact and high efficiency. Among these, direct alcohol fuel cells, proton exchange membrane fuel cells, and solid oxide fuel cells are highly promising. Fuel cells convert fuels, such as alcohols and hydrogen, into electricity with considerably higher efficiency than combustion engines, producing only water as the primary by-product. This clean energy pathway supports the United Nations Sustainable Development Goal (SDG) 7 (Affordable and Clean Energy) and advances SDG 13 (Climate Action) by reducing greenhouse gas emissions and dependency on fossil fuels. However, significant challenges remain before widespread implementation of fuel cells, including financial feasibility, long-term reliability, and scalability. Therefore, this review aims to address the existing gap in understanding how recent modeling and design advancements can overcome these limitations across different types of fuel cells. This review provides a comprehensive and current synthesis of recent fuel cell modeling and design, uniquely integrating bibliometric trends, experimental advances, and computational methods across all major types of fuel cells. Emphasis is placed on numerical optimization strategies, advancements in multi-physics simulations, sustainable material innovations, and emerging approaches such as artificial intelligence-assisted modeling and integrated multi-scale frameworks. The review offers a cross-disciplinary roadmap to improve the performance, durability, and commercial viability of next-generation fuel cell technologies.
{"title":"Recent advances in fuel cell design and modeling: A comprehensive review","authors":"Umang Bedi","doi":"10.1016/j.nxener.2026.100517","DOIUrl":"10.1016/j.nxener.2026.100517","url":null,"abstract":"<div><div>Fuel cells are gaining popularity as sustainable alternatives to traditional energy sources due to their low environmental impact and high efficiency. Among these, direct alcohol fuel cells, proton exchange membrane fuel cells, and solid oxide fuel cells are highly promising. Fuel cells convert fuels, such as alcohols and hydrogen, into electricity with considerably higher efficiency than combustion engines, producing only water as the primary by-product. This clean energy pathway supports the United Nations Sustainable Development Goal (SDG) 7 (Affordable and Clean Energy) and advances SDG 13 (Climate Action) by reducing greenhouse gas emissions and dependency on fossil fuels. However, significant challenges remain before widespread implementation of fuel cells, including financial feasibility, long-term reliability, and scalability. Therefore, this review aims to address the existing gap in understanding how recent modeling and design advancements can overcome these limitations across different types of fuel cells. This review provides a comprehensive and current synthesis of recent fuel cell modeling and design, uniquely integrating bibliometric trends, experimental advances, and computational methods across all major types of fuel cells. Emphasis is placed on numerical optimization strategies, advancements in multi-physics simulations, sustainable material innovations, and emerging approaches such as artificial intelligence-assisted modeling and integrated multi-scale frameworks. The review offers a cross-disciplinary roadmap to improve the performance, durability, and commercial viability of next-generation fuel cell technologies.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100517"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079396","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}
Standalone power systems in remote areas have traditionally relied on continuously operating fossil fuel generators, leading to high operational costs, reduced efficiency, and substantial carbon emissions. Standalone direct current (DC) microgrids have emerged as a promising alternative due to their lower conversion losses, improved integration of renewable energy sources (RES), and enhanced compatibility with modern DC-native loads and storage technologies. Despite these advantages, the planning, operation, and uncertainty management of standalone DC microgrids remain technically challenging. Intermittent RES generation, stochastic load behaviour, lack of mature standards, and complex control requirements introduce significant design and operational challenges. While numerous studies have proposed methods to address issues in sizing, optimisation, control, energy management, and uncertainty management, a comprehensive and structured review that connects these aspects across the full lifecycle of DC microgrid development is still lacking. This article addresses this gap by providing a systematic review of the state-of-the-art in planning methodologies, operational strategies, and uncertainty management techniques for standalone DC microgrids. The review synthesises theoretical frameworks and practical implementations, critically evaluates existing approaches by identifying their strengths and limitations, and highlights the interdependencies among planning, real-time operation, and uncertainty mitigation. Finally, the article outlines key research challenges and future opportunities to support the reliable, cost-effective, and sustainable deployment of standalone DC microgrids. The novelty of this study lies in its integrated perspective spanning planning, operational control, and uncertainty management, offering valuable guidance for researchers, system designers, and practitioners.
{"title":"Standalone DC microgrids: Planning, operation and uncertainty management","authors":"Hasith Jayasinghe , Kosala Gunawardane , Md. Alamgir Hossain , Ramon Zamora","doi":"10.1016/j.nxener.2026.100511","DOIUrl":"10.1016/j.nxener.2026.100511","url":null,"abstract":"<div><div>Standalone power systems in remote areas have traditionally relied on continuously operating fossil fuel generators, leading to high operational costs, reduced efficiency, and substantial carbon emissions. Standalone direct current (DC) microgrids have emerged as a promising alternative due to their lower conversion losses, improved integration of renewable energy sources (RES), and enhanced compatibility with modern DC-native loads and storage technologies. Despite these advantages, the planning, operation, and uncertainty management of standalone DC microgrids remain technically challenging. Intermittent RES generation, stochastic load behaviour, lack of mature standards, and complex control requirements introduce significant design and operational challenges. While numerous studies have proposed methods to address issues in sizing, optimisation, control, energy management, and uncertainty management, a comprehensive and structured review that connects these aspects across the full lifecycle of DC microgrid development is still lacking. This article addresses this gap by providing a systematic review of the state-of-the-art in planning methodologies, operational strategies, and uncertainty management techniques for standalone DC microgrids. The review synthesises theoretical frameworks and practical implementations, critically evaluates existing approaches by identifying their strengths and limitations, and highlights the interdependencies among planning, real-time operation, and uncertainty mitigation. Finally, the article outlines key research challenges and future opportunities to support the reliable, cost-effective, and sustainable deployment of standalone DC microgrids. The novelty of this study lies in its integrated perspective spanning planning, operational control, and uncertainty management, offering valuable guidance for researchers, system designers, and practitioners.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100511"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039417","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}
Perovskite solar cells (PSCs) commonly utilize organic materials as a hole-transport layer (HTL) to enhance hole extraction to the back electrode, thereby boosting device performance. These organic HTL are chemically and thermally unstable, degrading when exposed to air and moisture. This work, for the first time, attempts to explore the possibility of replacing the conventional organic HTL with the thermally and chemically stable, high hole mobility, inorganic III-nitride p-InGaN material as an HTL in PSC. The p-InGaN HTL-based PSC achieved a power conversion efficiency (PCE) of 21.6%, which is reasonable when compared to the PCE of 32.9% and 34.5% delivered by the conventional organic Spiro-OMeTAD HTL and the HTL-free configurations. These can be attributed to the assisted hole extraction by the HTL and the direct contact of the MAPbI3 perovskite absorber with the optimal higher work-function Pt back contact for the Spiro-OMeTAD HTL-based and HTL-free-based devices, respectively. We observed that variations in the back metal contact have a significant impact on the PCE of Spiro-OMeTAD HTL-based and HTL-free PSC, respectively. Although the p-InGaN and Spiro-OMeTAD HTL-based PSCs demonstrate equivalent values of PCE at all high temperatures, 400–700 K. The HTL-free cell shows higher thermal resilience compared to its HTL-based counterpart devices. Our work reveals that utilizing the p-InGaN HTL increases longevity due to material stability, whereas eliminating the HTL can deliver higher PCE and reduced costs.
{"title":"Performance optimization of perovskite solar cells using p-InGaN as a hole transport layer: A numerical comparison with spiro-OMeTAD and HTL-free designs","authors":"P.R. Jubu , B.J. Akeredolu , S.J. Ikwe , K.O. Ighodalo , O.S. Obaseki , Z.S. Mbalaha , S.K. Omotayo , Y. Yusof , M.Z. Pakhuruddin","doi":"10.1016/j.nxener.2025.100510","DOIUrl":"10.1016/j.nxener.2025.100510","url":null,"abstract":"<div><div>Perovskite solar cells (PSCs) commonly utilize organic materials as a hole-transport layer (HTL) to enhance hole extraction to the back electrode, thereby boosting device performance. These organic HTL are chemically and thermally unstable, degrading when exposed to air and moisture. This work, for the first time, attempts to explore the possibility of replacing the conventional organic HTL with the thermally and chemically stable, high hole mobility, inorganic III-nitride p-InGaN material as an HTL in PSC. The p-InGaN HTL-based PSC achieved a power conversion efficiency (PCE) of 21.6%, which is reasonable when compared to the PCE of 32.9% and 34.5% delivered by the conventional organic Spiro-OMeTAD HTL and the HTL-free configurations. These can be attributed to the assisted hole extraction by the HTL and the direct contact of the MAPbI<sub>3</sub> perovskite absorber with the optimal higher work-function Pt back contact for the Spiro-OMeTAD HTL-based and HTL-free-based devices, respectively. We observed that variations in the back metal contact have a significant impact on the PCE of Spiro-OMeTAD HTL-based and HTL-free PSC, respectively. Although the p-InGaN and Spiro-OMeTAD HTL-based PSCs demonstrate equivalent values of PCE at all high temperatures, 400–700 K. The HTL-free cell shows higher thermal resilience compared to its HTL-based counterpart devices. Our work reveals that utilizing the p-InGaN HTL increases longevity due to material stability, whereas eliminating the HTL can deliver higher PCE and reduced costs.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100510"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981497","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}
The indicators of electric vehicle performance such as state of charge (SOC), remaining useful life (RUL), and charge demand need to be accurately forecasted to ensure maximum energy control and battery life. The models used are usually not able to capture the spatial and temporal correlation of battery data and be robust to the presence of noisy measurements. In this study, we model a sequential attention-based deep learning structure with convolutional neural networks, gated recurrent units, and an attention mechanism that can ultimately understand the local features, temporal relationships, and dynamic significance of various features in sequential battery data. The hybrid architecture of this model allows it to extract local spatial features, long-term sequential dependencies and dynamically find the importance of the critical time steps. We also develop a hybrid loss that is an accumulation of Huber loss and Mean Squared Error, which is much more resilient to outliers and at the same time has high prediction accuracy. It is experimentally proven that the proposed model has R2 values of 0.9575, 0.9558, and 0.9199 on SOC, RUL, and charge demand, respectively, which are better than the current single-architecture methods.
{"title":"Predicting electric vehicle performance metrics using a convolution neural network-gated recurrent unit-attention based deep learning architecture","authors":"Shivi Sharma , Neetha S.S. , Pranav Arya , Chandra Prakash","doi":"10.1016/j.nxener.2026.100514","DOIUrl":"10.1016/j.nxener.2026.100514","url":null,"abstract":"<div><div>The indicators of electric vehicle performance such as state of charge (SOC), remaining useful life (RUL), and charge demand need to be accurately forecasted to ensure maximum energy control and battery life. The models used are usually not able to capture the spatial and temporal correlation of battery data and be robust to the presence of noisy measurements. In this study, we model a sequential attention-based deep learning structure with convolutional neural networks, gated recurrent units, and an attention mechanism that can ultimately understand the local features, temporal relationships, and dynamic significance of various features in sequential battery data. The hybrid architecture of this model allows it to extract local spatial features, long-term sequential dependencies and dynamically find the importance of the critical time steps. We also develop a hybrid loss that is an accumulation of Huber loss and Mean Squared Error, which is much more resilient to outliers and at the same time has high prediction accuracy. It is experimentally proven that the proposed model has R2 values of 0.9575, 0.9558, and 0.9199 on SOC, RUL, and charge demand, respectively, which are better than the current single-architecture methods.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100514"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039075","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 : 2026-04-01Epub 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.
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