Solar Energy Advances最新文献

{"title":"Comparison of temperature models for bifacial photovoltaic modules in Germany and Brazil","authors":"Oliver Pfeiffer,&nbsp;Aline Kirsten Vidal de Oliveira,&nbsp;Isadora Maciel Queiroz,&nbsp;Marília Braga,&nbsp;Helena Flávia Naspolini,&nbsp;Ingo Stadler,&nbsp;Ricardo Rüther,&nbsp;Ulf Blieske","doi":"10.1016/j.seja.2026.100140","DOIUrl":"10.1016/j.seja.2026.100140","url":null,"abstract":"<div><div>Accurate temperature modeling of bifacial photovoltaic (PV) modules is essential for precise energy yield prediction and system design optimization. This study compares the performance of three widely used steady-state thermal models — Ross, Faiman, and PVsyst — under two contrasting climatic conditions: Cologne, Germany (temperate oceanic), and Florianópolis, Brazil (humid subtropical). Identical bifacial glass–glass minimodules equipped with integrated thin-film thermocouples enabled direct cell temperature measurements, reducing uncertainties associated with conventional external surface sensors. Each model was evaluated using both literature-based and locally calibrated coefficients, with prediction accuracy assessed by root mean square error (RMSE) and mean error (ME). Results show that locally calibrated coefficients consistently outperformed literature defaults across all models and both locations, with RMSE values ranging from 2.06 K to 2.47 K after calibration. In Brazil, the PVsyst model with Uc=40 W/m²K achieved the lowest error (RMSE=2.30 K), while in Germany the Faiman model with locally derived coefficients yielded the best performance (RMSE=2.06 K). Despite the contrasting climates, calibrated model performance was comparable across both sites, suggesting that coefficient calibration is more influential than climatic region on prediction accuracy. This study further demonstrates that temperature sensor location systematically affects coefficient calibration: models calibrated on rear-glass surface measurements underestimated true cell temperature, increasing RMSE by up to 0.6 K compared to cell-surface calibration. These findings highlight the importance of direct cell temperature measurement and site-specific coefficient calibration for reliable thermal modeling of bifacial PV systems.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"6 ","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148539744","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":"Long-Term Experimental Performance of a PV–Battery-Powered Air-Conditioning System for Tropical Tourism Accommodation","authors":"Made Ery Arsana,&nbsp;I Dewa Made Cipta Santosa,&nbsp;I Gusti Agung Bagus Wirajati,&nbsp;Luh Putu Ike Midiani,&nbsp;Pande Putu Agus Santoso,&nbsp;Yudhy Kurniawan,&nbsp;Takahiko Miyazaki","doi":"10.1016/j.seja.2026.100143","DOIUrl":"10.1016/j.seja.2026.100143","url":null,"abstract":"<div><div>This study presents a long-term experimental performance analysis of a photovoltaic (PV)–battery-powered air-conditioning (AC) system operating in a tropical tourism accommodation simulator where cooling load represents the primary energy demand. The experimental system consisted of a 1.74 kWp monocrystalline PV array, a 3.6 kW hybrid inverter, a 5.12 kWh LiFePO₄ battery, and a fixed-speed split-type air-conditioning unit with a cooling capacity of 7000 BTU/h serving as the primary cooling load. System operation was continuously monitored over six months, from October 2024 to March 2025, using an IoT-based monitoring platform for real-time data acquisition and long-term operational observation. The results indicate that the PV–battery-powered cooling system achieved an average PV Energy Utilization Ratio (EUR) of 83.64%, with all monthly values remaining above 80%. The highest EUR was recorded in January 2025 (90.36%), while the lowest occurred in March 2025 (80.33%) due to reduced solar availability during the rainy season. Monthly surplus PV energy ranging from 13.9 to 27.5 kWh suggests opportunities for additional load integration or expanded battery storage capacity. Battery performance tests conducted after six months of operation demonstrate that the LiFePO₄ battery reliably supported nighttime air-conditioning operation for approximately 9–10 hours while maintaining a minimum State of Charge (SoC) of 20%. The experimental results demonstrate stable long-term operation and reliable battery-supported cooling performance under tropical conditions.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"6 ","pages":"Article 100143"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148539746","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":"Techno-economic assessment of a solar heat pump system with pit thermal energy storage for industrial steam generation","authors":"Puneet Saini,&nbsp;Cordin Arpagaus,&nbsp;Frédéric Bless","doi":"10.1016/j.seja.2026.100151","DOIUrl":"10.1016/j.seja.2026.100151","url":null,"abstract":"<div><div>This study proposes and evaluates a solar-assisted high-temperature heat pump system in which pit thermal energy storage (PTES) acts as a large-scale, low-temperature solar heat buffer on the heat-pump evaporator side for continuous industrial steam generation at 150 °C. The main contribution is the assessment of this configuration for achieving high annual heat fractions using representative industrial load profiles derived from measured data from 22 facilities. The study also contributes a systematic three-step methodological framework comprising; (a) derivation of representative industrial load profiles using k-means clustering based on real data from 22 industrial facilities, (b) development of a Python-based pre-sizing optimisation tool to explore the design space and identify near-optimal system configurations, and (c) detailed simulations of the selected configuration from the previous step using a dynamic model developed in TRNSYS. To capture a broad range of solar irradiation and operating conditions, simulations were performed for three European climates: Davos (Switzerland), Würzburg (Germany), and Seville (Spain). The analysed hybrid system supplied 83–99% of the annual heating demand in all cases. The most competitive case was obtained in Seville, with a levelised cost of heat (LCOH) of 71 €/MWh at a 5% discount rate and a 15 year economic lifetime. This corresponds to a 7.5-year discounted payback period at a baseline natural-gas heating cost of 70 €/MWh, including a CO₂ tax of 100 €/tCO₂. Electricity price strongly influenced system economics; achieving a 10-year payback in Würzburg and Davos requires electricity prices below 60 €/MWh and 100 €/MWh, respectively. CO₂ reduction, relative to a natural gas baseline, ranged from approximately 34% to over 90%, with the highest benefits achieved in Davos due to its low-carbon electricity mix. The results demonstrate that integrating pit thermal energy storage with solar collectors and a high-temperature heat pump enables high renewable fractions and cost-competitive decarbonisation pathways for industrial steam.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"6 ","pages":"Article 100151"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149043962","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":"Electron transport layer optimization and parametric sensitivity analysis of α-SrZrS₃ chalcogenide perovskite solar cells: A SCAPS-1D study","authors":"Tabassum E Nur,&nbsp;Syed Khalid Rahman,&nbsp;Galib Hashmi","doi":"10.1016/j.seja.2026.100154","DOIUrl":"10.1016/j.seja.2026.100154","url":null,"abstract":"<div><div>Perovskite solar cells have rapidly emerged as a leading third-generation photovoltaic technology, driven by their high power-conversion efficiencies and compatibility with low-temperature processing. However, dominant lead–halide perovskite systems continue to face significant challenges related to toxicity and limited operational stability. Scientists are therefore investigating lead-free alternatives, such as the all-inorganic chalcogenide perovskite SrZrS₃. This material has strong optical absorption and has both thermal and chemical stability. Nevertheless, their photovoltaic performance remains constrained by transport-layer selection and interface- and defect-induced recombination losses. Thus, in this work, an ultra-thin all-inorganic SrZrS₃ device with the planar architecture of FTO/ZnO/α-SrZrS₃/NiO_X/Ni has been numerically optimized using SCAPS-1D under AM1.5G illumination (100 mW/cm²) at 300 K. Unlike previous SrZrS₃ simulation studies that primarily focused on transport-layer or contact optimization, the novelty of the present work lies in the development of a comprehensive and defect-sensitive optimization framework that systematically integrates ETL selection, band alignment, interface-defect engineering, resistive-loss analysis, and absorber optimization to provide a more physically realistic assessment of device performance. With NiO_X serving as the hole transport layer, a systematic screening of ETL candidates (ZnO, TiO₂, IGZO, and SnO₂) was conducted. Due to its favorable band alignment and superior device performance, ZnO has been identified as the most suitable ETL. The optimized structure maintains ZnO thickness, α-SrZrS₃ thickness, ZnO donor density, and absorber doping density of 0.08 µm, 0.8 µm, 10¹⁶ cm^-3, and 5 × 10¹⁷ cm^-3 respectively. Keeping the layer defect density at 10¹⁴ cm^-3, interface defect densities at 10¹⁴ cm^-2, series resistance at 3 Ω. cm² and shunt resistance at 5000 Ω. cm², the refined configuration demonstrated an outstanding simulated performance, having V_OC, J_SC, FF<em>,</em> and peak efficiency of 0.997 V, 25.78 mA/cm², 79.16%, and 20.35% respectively. Overall, this study demonstrates that a holistic optimization strategy encompassing systematic ETL selection, defect engineering, and thorough parametric tuning, offers an effective route for the development of high-performance α-SrZrS₃ chalcogenide perovskite solar cells.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"6 ","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149252004","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":"Renewable electricity supply in Amazonas: Energy modeling to decarbonize isolated systems","authors":"Meiriele Alvarenga Cumplido ,&nbsp;André Rodrigues Gonçalves ,&nbsp;Fernando Ramos Martins ,&nbsp;Paula Conde Santos Borba ,&nbsp;Rodrigo Santos Costa ,&nbsp;Felipe Husadel Poyer ,&nbsp;Ricardo Rüther ,&nbsp;Enio Bueno Pereira","doi":"10.1016/j.seja.2026.100130","DOIUrl":"10.1016/j.seja.2026.100130","url":null,"abstract":"<div><div>This study evaluates hybrid systems using local renewable energy resources to decarbonize isolated systems (SISOL) in the Brazilian Amazon. Using the Calliope energy optimization framework, a model for SISOL was built and applied to two case studies: Careiro da Várzea and Rio Preto da Eva. The systems were analyzed under scenarios combining solar photovoltaic (PV), batteries (Bt), and green hydrogen (H<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>), considering two land-use settings for PV deployment: no spatial restrictions (default) and the minimum required area to meet local electricity demand. The installed PV and storage capacities correspond to the cost-optimal solutions obtained through optimization. In Rio Preto da Eva, the PV–Bt scenario under the default condition reduces the levelized cost of electricity (LCOE) by 13% compared to the optimized fossil fuel (FF) baseline, and is characterized by 65 MW of PV capacity and 330 MWh of Bt storage. The PV–greenH<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>–Bt scenario increases the LCOE by 67%, with 117 MW of PV, 715 MWh of H<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> storage, and 2.2 MWh of batteries. Under the strictest land-use constraint, which differs between scenarios due to their distinct conversion and storage needs, the LCOE of the PV–Bt scenario exceeds that of the PV–greenH<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>–Bt scenario. In this setting, the PV–Bt system deploys 53 MW of PV and 1.4 GWh of Bt storage, while the PV–greenH<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>–Bt configuration relies on 94 MW of PV, 3.2 GWh of H<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> storage, and 36.6 MWh of batteries. Compared to FF, GHG emissions decreased by 92% in the PV–Bt scenario and by 85% in the PV–greenH<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>–Bt pathway. In contrast, Careiro da Várzea faces significant land constraints, making scenarios spatially infeasible under the evaluated conditions. These findings highlight the critical role of local context in designing possible strategies to deeply decarbonize isolated systems.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"6 ","pages":"Article 100130"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397377","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}

{"title":"Advancing solar energy financing in Sub-Saharan Africa: A systematic review of trends, progress, and pathways to sustainable development","authors":"John Bain ,&nbsp;Emmanuella Akosua Appiah ,&nbsp;Flavio Odoi-Yorke","doi":"10.1016/j.seja.2026.100136","DOIUrl":"10.1016/j.seja.2026.100136","url":null,"abstract":"<div><div>Access to affordable and sustainable energy remains a significant development challenge in Sub-Saharan Africa (SSA), where over 600 million people lack electricity despite the region's abundant solar resources. Financing constraints, weak institutional frameworks, and high capital costs continue to hinder the deployment of solar energy technologies. This study assessed the evolution, trends, and key drivers of solar energy financing in SSA. Using the Scopus database, research publications from 1995 to 2025 were systematically screened in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. Bibliometric analyses were conducted using the Bibliometrix package in R-Studio to map publication trends, country contributions, collaboration networks, and thematic evolution. The findings revealed growth in research output since 2019, demonstrating global interest in clean energy access. The United Kingdom and South Africa lead contributions, while Ghana and Nigeria are emerging hubs. Key research themes include energy access, renewable energy policy, financing models, and climate change mitigation. Persistent barriers, including high upfront costs, inadequate financial mechanisms, and institutional weaknesses, hinder investment flows. The study identified potential areas for future research on solar energy financing in SSA. The study's findings highlight the importance of innovative financing instruments, local capacity-building, and policy consistency in attracting private investment. These insights are crucial for policymakers, investors, and researchers aiming to develop inclusive, sustainable, and scalable solar energy financing frameworks in SSA.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"6 ","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147600284","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}

{"title":"Compact temporal CNNs for PV tracker telemetry fault detection using a temporal squeeze-fire architecture","authors":"Katleho Masita,&nbsp;Thokozani Shongwe,&nbsp;Ali N Hasan","doi":"10.1016/j.seja.2026.100142","DOIUrl":"10.1016/j.seja.2026.100142","url":null,"abstract":"<div><div>This study proposes PV-TSFNet, a compact deep learning model for unsupervised fault detection in tracker-based photovoltaic (PV) systems using multivariate telemetry. The method is motivated by the limited availability of labeled fault data in operational PV plants and the need for lightweight models suitable for practical deployment. After preprocessing, resampling, daytime filtering, normalization, and feature engineering, sliding windows of telemetry data are used to train the model for next-step prediction. PV-TSFNet is inspired by SqueezeNet but redesigned for PV time-series analysis through Temporal Squeeze-Fire modules that combine multi-scale temporal convolutions, cross-feature convolutions, and residual connections. The model outputs a next-step telemetry estimate, and the anomaly score is computed from the mean squared prediction error. Fault detection is performed by thresholding the anomaly score using a percentile-based threshold derived from training data, followed by threshold sweeping to analyze Precision-Recall and F1 behavior. Because the dataset does not provide explicit fault annotations, evaluation is conducted using a proxy rule-based reference labeling protocol that captures tracker stall behavior, low battery conditions, and sensor jumps. Results are reported through confusion matrices, Precision, Recall, F1, Precision-Recall curves, and F1-versus-threshold analysis, and compared against baseline models including LSTM, 1D CNN, autoencoder, and a SqueezeNet-style predictor. The proposed PV-TSFNet provides a compact and PV-aware anomaly detection framework that is well suited for telemetry-based PV monitoring and forms a strong foundation for future validation using plant alarm logs and expert-labeled fault events.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"6 ","pages":"Article 100142"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148539747","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":"Electroluminescence image-based multi-class defect classification of solar photovoltaic cells using a lightweight CNN: An analysis of input image resolution","authors":"Abdulbasit Hassan,&nbsp;Md Shafiullah,&nbsp;Selma K.E. Awadallah","doi":"10.1016/j.seja.2026.100141","DOIUrl":"10.1016/j.seja.2026.100141","url":null,"abstract":"<div><div>Due to the rapid growth in large-scale solar farm photovoltaic (PV) installations, existing asset management activities, such as maintenance mechanisms, have become increasingly demanding and inefficient. Developing an automated system to monitor PV cell conditions is now essential for significantly reducing this maintenance burden. Inspired by this, we proposed a lightweight (simple and computationally efficient) convolutional neural network (CNN) to extract complex features and classify defects from electroluminescence (EL) images of solar PV cells. Based on the prediction, this multi-classification model categorizes EL cell images into three main classes: good, cracked, and corroded. To address dataset scarcity and class imbalance, a hybrid augmentation strategy combining geometric transformations and Gaussian noise injection was employed to enhance data diversity and improve model robustness. However, to investigate the influence of input image resolution on defect classification performance, a sensitivity analysis was conducted to determine the optimal image resolution. The EL images were resized to five spatial resolutions ranging from 50 × 50 to 250 × 250 pixels, and the CNN configuration was systematically evaluated using Adam and RMSprop optimization algorithms. The results demonstrate that image resolution plays a critical role in defect classification, with a small number of misclassifications observed at resolutions of 50 × 50 and 100 × 100 pixels. In contrast, all evaluation metrics and confusion matrices achieved perfect classification performance at resolutions of 150 × 150 pixels and above, indicating that this resolution is sufficient to preserve the defect-related features required for reliable EL image classification. The proposed lightweight CNN architecture was quantitatively compared with ResNet18 and VGG16 on the same dataset and training conditions, demonstrating an effective balance between predictive accuracy and computational efficiency. This balance enables efficient learning and robust generalization without the challenges associated with higher resolutions, making the proposed model a practical and scalable solution for real-world photovoltaic defect classification.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"6 ","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148539745","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":"Advancing the solar cooking revolution: Insights into the evolving landscape of solar PV-based electric cooking","authors":"Flavio Odoi-Yorke ,&nbsp;Richard Opoku ,&nbsp;Francis Davis ,&nbsp;George Yaw Obeng","doi":"10.1016/j.seja.2025.100091","DOIUrl":"10.1016/j.seja.2025.100091","url":null,"abstract":"<div><div>In recent years, there has been a significant emphasis on transitioning to sustainable energy technologies. Among these, electric cooking (e-cooking) has emerged as a promising alternative to traditional cooking methods. This study employed a systematic review and bibliometric analysis to investigate emerging trends and advancements in solar PV-based e-cooking technology. The systematic review followed the PRISMA guidelines, and the bibliometric analysis utilised the R Studio Bibliometrix software package in conjunction with the Biblioshiny interface. The findings revealed a growing research interest in solar PV-based e-cooking, driven by concerns about climate change, energy security, and environmental sustainability, such as deforestation, indoor air pollution, and greenhouse gas emissions. The thematic analysis identified core research areas, including solar PV systems, renewable energy integration, developing clean and efficient cooking solutions, and addressing socio-economic factors for widespread adoption. Advancements were observed in areas such as solar PV-powered cooking devices, integrating PV e-cooking into microgrids and mini-grids, and exploring solar PV-based hydrogen production for cooking. Socio-economic and cultural factors were recognised as pivotal for successful technology adoption. The study highlights the potential of solar PV-based e-cooking as a sustainable and clean cooking solution and proposes future research directions, including optimisation of cooking devices, integration with advanced technologies, and comprehensive assessments of techno-economic viability and life-cycle impacts. The study findings are vital to researchers, policymakers, and industry stakeholders advancing the e-cooking sector for sustainable development.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"5 ","pages":"Article 100091"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168227","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":"Exploring the evolution of solar-driven interfacial evaporation: Bibliometric and trends analyses","authors":"Maryam Nooman AlMallahi ,&nbsp;Mohamed Y.E. Selim ,&nbsp;Mahmoud Elgendi","doi":"10.1016/j.seja.2025.100116","DOIUrl":"10.1016/j.seja.2025.100116","url":null,"abstract":"<div><div>The interfacial solar evaporation (ISE) field has gained significant attention since its emergence due to its potential for harnessing solar radiation and developing novel photothermal materials. The present study conducts a bibliometric analysis to explore ISE research over the past decade from 2014 up to November 2024, using VOSviewer software and Bibliometrix package. Key metrics such as publication trends, affiliation, countries, keyword analysis, citations, and dynamic mapping were examined. The results showed a remarkable growth in the research output, with an impressive cumulative annual growth rate of 43.35 % and publications peaking in 2024. Desalination journal emerged as a leading source with 112 publications. Countries like China, the United States, and Australia have demonstrated a strong interest in this field. Keyword analysis revealed that terms like evaporation, desalination, steam generators, light absorption, photothermal, and water absorption are the most frequently used words. Materials such as graphene, carbon, cellulose, hydrogels, and biomass are often featured in ISE research. The conclusions and remarks presented can offer scholars a quantitative understanding of the interdisciplinary and multi-perspective approaches, highlighting the trends and advancements in the field.</div></div>","PeriodicalId":101174,"journal":{"name":"Solar Energy Advances","volume":"5 ","pages":"Article 100116"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887266","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}