Energy Conversion and Management-X最新文献

{"title":"Enhancing the mixotrophic production of PUFA lipid-rich microalga Chlorella biomass using hydroponic effluent and its application in a zero-waste multiproduct biorefinery to promote a circular bioeconomy","authors":"Nanthakrit Sriket ,&nbsp;Antira Wichaphian ,&nbsp;Apiwit Kamngoen ,&nbsp;Jeeraporn Pekkoh ,&nbsp;Tawanchai Khuendee ,&nbsp;Yupa Chromkaew ,&nbsp;Benjamas Cheirsilp ,&nbsp;Wageeporn Maneechote ,&nbsp;Supakit Chaipoot ,&nbsp;Gochakorn Kanthakat ,&nbsp;Kuan Shiong Khoo ,&nbsp;Yoong Kit Leong ,&nbsp;Sirasit Srinuanpan","doi":"10.1016/j.ecmx.2025.101082","DOIUrl":"10.1016/j.ecmx.2025.101082","url":null,"abstract":"<div><div>This study optimized the mixotrophic cultivation of <em>Chlorella</em> sp. G049 using hydroponic effluent to maximize biomass, lipid, and polyunsaturated fatty acid accumulation. Optimal conditions, identified through response surface methodology, included 20-hour light exposure, 60.03 µmol/m²/s light intensity, and supplementation with 7.21 g/L glucose and 14.22 mg/L indole-3-acetic acid. Under these conditions, <em>Chlorella</em> sp. G049 achieved 3.77 g/L dry cell weight, 23.78 % lipid content, and 27.78 % polyunsaturated fatty acid (PUFA). Nutrient removal efficiencies were 100 % for ammonium, 63.10 % for nitrate, 98.85 % for phosphate, and 71.30 % for chemical oxygen demand, with carbon dioxide fixation reaching 1.00 g/L/day. A sequential biorefinery approach enabled sustainable biomass valorization, with high polyunsaturated fatty acid content for biodiesel and protein extraction yielding an amino acid profile with notable antioxidant activity. The residual biomass served as a biofertilizer, enhancing soil quality and promoting plant growth in lettuce. These results highlight the potential of <em>Chlorella</em> sp. G049 for sustainable wastewater treatment, carbon sequestration, and multiproduct biorefinery applications.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"27 ","pages":"Article 101082"},"PeriodicalIF":7.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204208","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":"A review on machine learning driven next generation thermoelectric generators","authors":"Uzair Sajjad ,&nbsp;Ahsan Ali ,&nbsp;Hafiz Muhammad Ali ,&nbsp;Khalid Hamid","doi":"10.1016/j.ecmx.2025.101092","DOIUrl":"10.1016/j.ecmx.2025.101092","url":null,"abstract":"<div><div>Thermoelectric (TE) materials and devices have emerged as promising technologies for energy conversion and waste heat recovery. Yet, challenges remain in improving TE efficiency due to the complicated relationship between TE material properties, geometrical designs, and performance optimization. Machine learning (ML) on the other hand has played a great role in discovery and optimization of TEMs, offering new insights and accelerating the development of high-performance TE generators (TEG). This review provides a comprehensive overview of the applications of ML in TEG research, highlighting the methodologies and techniques employed for performance prediction and optimization of various TEGs including TEGs, segmented (STEGs), annular (ATEGs), photovoltaic (PVTEGs), and other hybrid (HTEGs) along with their maximum power point tracking (MPPT) and integration of TEGs with other systems. Key advancements include the use of regression and optimization as well as physics informed methods. Also, the potential benefits of integrating ML methods with other computational schemes such as COMSOL and CFD are highlighted. This review also addresses the challenges of limited datasets, model interpretability, and experimental validation. Future research directions are proposed, focusing on integrating ML with experimental and computational approaches to unlock new pathways for thermoelectric generators’ design. In general, this review provides valuable insights and potential of AI/ML into the advancement of TEG technology.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"27 ","pages":"Article 101092"},"PeriodicalIF":7.1,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204209","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":"The effect of carbon dioxide emissions, environmental innovation, and market regulation on renewable energy in France","authors":"Vu Ngoc Xuan","doi":"10.1016/j.ecmx.2025.101081","DOIUrl":"10.1016/j.ecmx.2025.101081","url":null,"abstract":"<div><div>The manuscript examines the impact of carbon dioxide (CO₂) emissions, environmental innovation, and market regulation on renewable energy development in France. Using time-series data from 1990 to 2023, this research applies the Autoregressive Distributed Lag (ARDL) model as the core analytical framework to investigate these variables’ short- and long-term relationships. Additional analyses using Ordinary Least Squares (OLS) and Vector Autoregression (VAR) are conducted for robustness checks. The findings reveal that environmental innovation positively influences renewable energy adoption, while CO₂ emissions and market regulation have mixed effects. CO₂ emissions, while catalyzing policy-driven renewable energy adoption, may also create barriers to sustainable investment if not mitigated effectively. Market regulation can foster or constrain renewable energy expansion depending on its design and implementation. The manuscript underscores the importance of balanced policy frameworks that promote innovation and address environmental concerns while ensuring economic feasibility. Policy implications suggest that fostering innovation and strategic regulatory frameworks can enhance France’s transition to a sustainable energy (SE) system. The manuscript examines the impact of CO₂ emissions, environmental innovation, and market regulation on renewable energy development in France. The findings provide actionable insights for policymakers seeking to accelerate France’s transition toward a SE system.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"27 ","pages":"Article 101081"},"PeriodicalIF":7.1,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144189708","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":"Dynamics and interrelations in hybrid electricity storage systems due to storage sizing and management","authors":"Jasmin Mensik,&nbsp;David Wöss,&nbsp;Tobias Pröll","doi":"10.1016/j.ecmx.2025.101086","DOIUrl":"10.1016/j.ecmx.2025.101086","url":null,"abstract":"<div><div>This analysis explores the dynamic interactions within a hybrid electricity storage system, consisting of short-term lithium-ion battery storage, mid-term pumped hydro storage and long-term power-to-gas storage. Thereby, this analysis addresses a gap in the literature by analysing interactions within complex hybrid electricity storage systems involving more than two technologies. Using a reference scenario for the Austrian control area as a baseline, eight comparative scenarios are developed, all featuring 100 % renewable electricity generation. The scenarios differ in available storage capacity and charging/discharging power constraints per 15-minute interval. A key focus is the comparison between an efficiency-optimized and a peak-load reserve management. A multi-dimensional assessment framework is applied to quantify detailed subsystem behaviours and overall systemic effects. Consequently, synergies and trade-offs within the hybrid storage system, overall system losses as well as cross-border exchanges are quantified.</div><div>The results reveal that changing the battery capacity (+/-30 %), primarily leads to compensation and displacement effects of the pumped hydro storage up to 17 % compared to the reference scenario. Varying the size of power-to-gas (+/-30 %) affects overall system losses, leading to an increase up to 4.8 % compared to the most efficient scenario, where the battery is enlarged while power-to-gas is reduced simultaneously. The implementation of a peak-load reserve management strategy decreases the number of 15-minute intervals with imports by up to 11.5 % per year. Nevertheless, compared to the efficiency-optimized strategy, the peak-load reserve management results in an imbalance, with a 1:7 ratio between import reduction and the corresponding increase in generation requirement.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"27 ","pages":"Article 101086"},"PeriodicalIF":7.1,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213471","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":"Multi-objective optimization of unbalanced power distribution systems: a comprehensive approach to address uncertainties and enhance performance","authors":"Mohammad Reza Maghami ,&nbsp;Elaheh Yaghoubi ,&nbsp;Mazlan Mohamed ,&nbsp;Elnaz Yaghoubi ,&nbsp;Mehdi Zareian Jahromi ,&nbsp;Thang Ka Fei","doi":"10.1016/j.ecmx.2025.101087","DOIUrl":"10.1016/j.ecmx.2025.101087","url":null,"abstract":"<div><div>This study presents an advanced multi-objective optimization framework to enhance the performance of unbalanced power distribution systems. It highlights the significance of three-phase load flow analysis and addresses key problems such as voltage fluctuations, power losses, cost inefficiency, and uncertainty related to renewable energy integration. A holistic strategy integrating demand response (DR), energy storage, and distributed generation is employed. Using a customized Genetic Algorithm (GA), the model optimizes the capacity, location, and phase placement of energy sources, including solar, wind, biomass, diesel, and utility power. It also incorporates probabilistic scenario generation to manage uncertainties in load and renewable resources. Simulation results on the IEEE 13-bus system demonstrate substantial improvements: 91 % reduction in power losses (from 9924 kW to 780 kW), 11 % reduction in load profiles, 60 % decrease in utility usage, and 84 % reduction in overall network cost. These findings confirm the effectiveness of the proposed framework in enhancing energy efficiency, reliability, and sustainability in modern unbalanced distribution networks.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"27 ","pages":"Article 101087"},"PeriodicalIF":7.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213470","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":"Sustainable solar dish based CSP-fossil operated hybrid power plant with thermal energy storage utilizing triple extraction steam and organic Rankine cycles","authors":"Shakhawat Hossain Sarker,&nbsp;Sameeul Amin,&nbsp;Nurul Abrar,&nbsp;M. Monjurul Ehsan,&nbsp;Yasin Khan","doi":"10.1016/j.ecmx.2025.101084","DOIUrl":"10.1016/j.ecmx.2025.101084","url":null,"abstract":"<div><div>CSP is gaining popularity as a means to collect solar energy. This study examines its role in promoting sustainable energy. The paper presents the design and analysis of a solar dish-based CSP system operating with a triple extraction Rankine cycle — a thermodynamic cycle that utilizes energy at multiple points in the process to maximize overall work output by extracting steam during expansion at varied pressures. The system features thermal energy storage (TES) using molten salt (MSES), coupled with an organic Rankine cycle (ORC) to boost efficiency. It also includes a natural gas-based oxy-fuel combustion chamber for consistent energy supply when solar power is unavailable, forming a hybrid setup. The system aims to generate substantial electrical energy, thereby repositioning the role of CSP from a supplementary component in fossil fuel-dependent power cycles to a fully sustainable alternative capable of meeting the escalating global energy demand. CSP technology is strongly supported by the study as a key power source. It considers Curzon-Ahlborn efficiency, which accounts for irreversibility, representing the theoretical maximum efficiency of a heat engine operating between two thermal reservoirs. Dynamic interactions occur among its components (CSP system, ORC, oxy chamber, and conventional units) in response to changing energy demand, solar availability, and fuel supply. The study reveals that the triple extraction Rankine cycle has an efficiency of 47.15 % under optimal operating conditions, compared to other extraction processes. Furthermore, a comprehensive analysis has been conducted on the system’s dynamic behavior under various weather conditions, as the CSP/Rankine system can supply stable energy for 82 % of the year while only using solar power. This study also includes a comparison between the standard paradigm of electricity generation and the utilization of renewable energy sources. The implementation of a hybrid system results in a fuel consumption reduction of 74 % compared to a power plant that solely relies on fossil fuels.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"27 ","pages":"Article 101084"},"PeriodicalIF":7.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144189709","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":"Artificial intelligence for microgrids design, control, and maintenance: A comprehensive review and prospects","authors":"Mojtaba Hadi ,&nbsp;Elhoussin Elbouchikhi ,&nbsp;Zhibin Zhou ,&nbsp;Abdelhakim Saim ,&nbsp;Miadreza Shafie-khah ,&nbsp;Pierluigi Siano ,&nbsp;Hesam Rahbarimagham ,&nbsp;Pau Marti Colom","doi":"10.1016/j.ecmx.2025.101056","DOIUrl":"10.1016/j.ecmx.2025.101056","url":null,"abstract":"<div><div>The rising use of renewable energy sources in today’s landscape underscores the growing significance of microgrids. As the deployment of microgrids continues to grow, there is a pressing need to develop advanced control and management strategies to overcome the challenges associated with their integration and operation. Notably, Artificial Intelligence (AI) is a rapidly developing field that is well-positioned to effectively address these challenges. This paper begins by exploring the fundamentals of microgrids, emphasizing their structure, components, and control aspects. After introducing essential AI techniques, it analyzes their role in various microgrid domains, such as design, control, and maintenance. (1) In the design realm, the focus is on optimal sizing. (2) In the realm of control, a hierarchical framework comprising primary, secondary, and tertiary levels is introduced. Within the tertiary control level, the focus is directed toward energy management systems. (3) Maintenance involves activities such as condition monitoring, diagnosis, and prognosis. In this context, focusing on power quality (PQ) issues underscores the role of AI in fault detection and classification as an integral part of the diagnostic process. Finally, the paper explores cutting-edge technologies and future advancements in microgrids. It highlights the integration of IoT for improved connectivity and data exchange, emphasizes the role of federated learning and blockchain in bolstering cybersecurity, and explores the transformative potential of digital twins in optimizing microgrid operations.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"27 ","pages":"Article 101056"},"PeriodicalIF":7.1,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178328","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":"Insights into the addition of exogenous materials to enhance anaerobic digestion","authors":"Adhirashree Vannarath ,&nbsp;Yahia H. Ahmad ,&nbsp;Toka Mahmoud Farhat ,&nbsp;Kyu-Jung Chae ,&nbsp;Amina S. Aljaber ,&nbsp;Siham Y. Al-Qaradawi","doi":"10.1016/j.ecmx.2025.101078","DOIUrl":"10.1016/j.ecmx.2025.101078","url":null,"abstract":"<div><div>Anaerobic digestion (AD) is a globally recognized technology for treating organic waste, providing key environmental benefits, including renewable energy generation. Over the years, significant research efforts have focused on optimizing AD under diverse conditions to enhance its feasibility and broaden its application to various substrates. Among the promising approaches for improving AD performance, using exogenous materials (EMs) has garnered considerable attention. This review presents a comprehensive overview of the recent advances in using EMs as promoters to enhance the efficiency of the AD process. The EMs were classified based on their chemical composition into seven categories, namely, carbon-based, metal-based, zeolites, natural, biological, hybrid, and miscellaneous materials. This review aims to deepen the understanding of how various promoters influence AD, focusing on their specific functions, dosage effects, underlying mechanisms, and their role in mitigating both conventional inhibitors and emerging contaminants (e.g., antibiotic resistant genes), thereby enhancing the AD process. Notably, this review explores natural and waste-derived materials as exogenous promoters in AD, an overlooked topic, highlighting their role in improving AD while promoting circular economy principles and sustainable development goals. This review highlights the economic and recovery potential of EMs in AD, offering novel insights not covered in existing literature. Finally, the review provides insights into the possibility of using exogenous materials in AD systems, highlighting the challenges and opportunities for future research.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"27 ","pages":"Article 101078"},"PeriodicalIF":7.1,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184956","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":"Innovative dust detection and efficient cleaning of PV Panels: A CNN‑RF approach using I–V curve data transformed into RGB mosaics","authors":"Safia Babikir Bashir ,&nbsp;Mena Maurice Farag ,&nbsp;Abdul-Kadir Hamid ,&nbsp;Ali A. Adam ,&nbsp;Ramesh C. Bansal ,&nbsp;Nsilulu T Mbungu ,&nbsp;A. Elnady ,&nbsp;Ahmed G. Abo-Khalil ,&nbsp;Mousa Hussein","doi":"10.1016/j.ecmx.2025.101079","DOIUrl":"10.1016/j.ecmx.2025.101079","url":null,"abstract":"<div><div>Photovoltaic (PV) panels are vital for renewable energy generation, yet their efficiency is critically hindered by environmental challenges such as dust accumulation, especially in arid regions like the UAE. Dust buildup can reduce efficiency by up to 30% within a month, threatening the sustainability of solar power, which is projected to supply 10% of global energy by 2030. Existing cleaning methods are unsustainable, consuming an estimated 10 billion gallons of water annually, enough to meet the drinking needs of 2 million people, necessitating the development of a cost-effective, resource-efficient alternative. This research presents a novel machine learning-based system to automate dust detection and optimize cleaning, significantly reducing water consumption while improving power generation efficiency. The methodology transforms I-V curve electrical parameters into RGB mosaic images, enabling precise classification of operational states such as normal operation, dust accumulation, shading, and faults without relying on external imaging devices. The system is built on a hybrid model combining Convolutional Neural Networks (CNN) and Random Forest (RF) classifiers (CNN-RF), where the CNN extracts high-level features from RGB mosaic images, and the RF classifier accurately categorizes operational states. Upon detecting dust accumulation, a secondary CNN-RF model classifies the severity into low, moderate, or heavy, guiding an optimized cleaning process that minimizes water usage while maintaining cleaning effectiveness. The primary CNN-RF model achieved 100% accuracy in classifying operational states using RGB mosaic images, surpassing the 97% accuracy achieved by I–V curve-based methods. Furthermore, the secondary CNN-RF model for dust severity classification attained an accuracy of 98% using RGB mosaic images, compared to only 68% when using traditional I–V curves, highlighting the superior performance of RGB mosaic images in detecting fine-grained dust levels. This optimized classification approach guides an automated cleaning system that minimizes water usage while maintaining PV panel efficiency.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"27 ","pages":"Article 101079"},"PeriodicalIF":7.1,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204207","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":"Transformation of waste vegetable oil to green fuel in the presence of novel heterogeneous catalysts","authors":"Farrukh Jamil ,&nbsp;Ala’a H. Al-Muhtaseb ,&nbsp;Ahmad M. Abu-Jrai ,&nbsp;Myo Tay Zar Myint ,&nbsp;MAA Ghani ,&nbsp;Mohammed Al-Abri ,&nbsp;Rashid Al-Hajri","doi":"10.1016/j.ecmx.2025.101067","DOIUrl":"10.1016/j.ecmx.2025.101067","url":null,"abstract":"<div><div>Waste vegetable oil, chosen for its abundant availability, is utilised for fuel production via a novel catalyst using a hydrodeoxygenation (HDO) process. The catalyst is novel (based on composition), synthesized by modifying ZrO₂ with Fe₂O₃ and Pt as an active metal, resulting in the final composition of Pt-Fe₂O₃-ZrO₂ by incorporating the incipient impregnation technique. Comprehensive characterisation of the catalyst using XRD, SEM, BET, NH₃-TPD, and XPS confirms its suitability for HDO. The resulting liquid oil, obtained through HDO of waste vegetable oil in the presence of Pt-Fe₂O₃-ZrO₂, consists of hydrocarbon fractions within the aviation fuel and diesel range. Additionally, while waste vegetable oil initially contains even-numbered oxygenated hydrocarbons, the product oil results after HDO contains both odd- and even-numbered hydrocarbons, indicating the occurrence of decarbonylation, decarboxylation, and dehydration reactions. Elemental analysis reveals a 91.3% deoxygenation efficiency, demonstrating the high catalytic activity of the synthesised material and confirming its reusability, which enhances its commercial viability.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"27 ","pages":"Article 101067"},"PeriodicalIF":7.1,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147917","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}