Applied Energy最新文献

{"title":"A hydrogen supply system utilizing PEMFC exhaust heat and modular metal hydride tanks for hydrogen-powered bicycles","authors":"Shan Miao ,&nbsp;Tomoya Ezawa ,&nbsp;Masami Sumita ,&nbsp;Koki Harano ,&nbsp;Ayane Imai ,&nbsp;Noboru Katayama ,&nbsp;Kiyoshi Dowaki","doi":"10.1016/j.apenergy.2025.126760","DOIUrl":"10.1016/j.apenergy.2025.126760","url":null,"abstract":"<div><div>A compact hydrogen supply system for thermally integrating metal hydride (MH) tanks with a proton exchange membrane fuel cell (PEMFC) for a hydrogen-powered electric-assist bicycle (H-bike) is proposed. The system recovers the exhaust heat generated by the PEMFC to sustain hydrogen desorption and improve the system's energy efficiency. The results demonstrate that the split-tank strategy decreases thermal and pressure gradients and enhances heat transfer and hydrogen release. The honeycomb tank configuration further improves hydrogen desorption by promoting uniform airflow distribution around each tank, thereby improving exhaust heat utilization from the PEMFC. It employs a layer-adjustable configuration, facilitating the flexible adaptation of MH cartridge quantities to meet hydrogen demand and prevailing road conditions in urban areas. Under a PEMFC power output of 215 W, the system maintains a stable hydrogen flow rate for over 30 min, with a heat recovery efficiency of 22.62 %. Furthermore, increasing the number of MH cartridge layers significantly improves the thermal utilization of the system, achieving a utilization efficiency of 39.90 % with two layers. These findings confirm the feasibility and scalability of the proposed system for H-bike, highlighting its potential as a decentralized hydrogen supply solution for lightweight mobility and urban transportation applications.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126760"},"PeriodicalIF":11.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term impacts and design considerations of dual-purpose wave farms for energy generation and coastal protection","authors":"Avinash Boodoo ,&nbsp;Jeffrey S. Cross ,&nbsp;Christopher Ridgewell ,&nbsp;Ville Kortelainen ,&nbsp;Matti Vuorinen ,&nbsp;Amina Harouna-Mayer","doi":"10.1016/j.apenergy.2025.126805","DOIUrl":"10.1016/j.apenergy.2025.126805","url":null,"abstract":"<div><div>The dual use of wave farms for renewable energy generation and coastal protection presents a promising strategy to reduce the Levelized Cost of Electricity (LCoE) and improve the economic feasibility of wave energy. However, no prior study has quantified the long-term morphodynamic impacts of wave farms or evaluated how seasonal wave conditions influence energy output and coastal protection effectiveness. This study presents the first integrated assessment of a nearshore WaveRoller Wave Energy Converter (WEC) array over 1-, 10-, and 20-year periods, using a field-validated, coupled depth-averaged (2DH) hydrodynamic, spectral wave, and sediment transport model in Delft3D. Nine deployment configurations were simulated to explore how array layout (spacing and distance from shore) affects wave attenuation, sediment retention, and energy output. Results show that the WaveRoller array produced 562.3 MWh annually per device, with a capacity factor of 18.34 % and a capture efficiency of 49.9 %. The system also retained up to 278,427 m³ of sediment after 20 years, with a sediment retention per unit area of 1.941 m³/m². Wave attenuation was greatest during low-to-moderate energy conditions, suggesting year-round protection benefits. Sensitivity analyses revealed a trade-off between energy yield and erosion mitigation, with tighter spacing enhancing sediment retention and moderate distances offshore improving energy yield. By quantifying energy production and erosion mitigation under different design scenarios, this study demonstrates the dual functionality of wave farms and supports their use as multi-functional coastal infrastructure. These results offer a foundation for future techno-economic models that incorporate both energy and coastal protection outcomes.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126805"},"PeriodicalIF":11.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive mathematical model for chemical membrane degradation of proton exchange membrane fuel cell with considering precipitated Pt formation","authors":"Yueqiang Zhu,&nbsp;Zhiguo Qu","doi":"10.1016/j.apenergy.2025.126795","DOIUrl":"10.1016/j.apenergy.2025.126795","url":null,"abstract":"<div><div>Chemical membrane degradation under open-circuit/idling condition results in membrane thinning and gas separation deterioration, which subsequently reduces the durability and lifetime of proton exchange membrane fuel cells (PEMFCs). Traditional membrane degradation models do not consider the existence of precipitated Pt in the membrane. This is the reason why the simulated membrane degradation adjacent to the anode catalyst layer (CL) is more severe than that adjacent to the cathode CL, which is not consistent with the experiment results. To address such an inconsistency, a comprehensive membrane degradation model was developed with consideration of precipitated Pt. In this model, the processes of precipitated Pt formation, H₂O₂ formation and decomposition, and attack of free radicals on membrane are included. This makes the simulated spatial nonuniformity of membrane degradation notably consistent with the experimental results, which subverts the traditional membrane degradation models. The concentration distributions of O₂, H₂, H₂O₂, Fe²⁺/Fe³⁺, as well as local potential and ionomer species in the membrane were obtained. Moreover, membrane degradation under various temperatures and relative humidity values was explored. It was found that an increasing temperature weakens the nonuniformity of membrane degradation and that a lowering humidity can inhibit membrane degradation. Finally, the membrane degradation process can be separated into the finite dissociation and fragmented stages, which are dominated by the scission and unzipping of ionomer chains and falling off of short-chain fragments, respectively. This model enables comprehensive understanding of the membrane degradation process and facilitates the development of corresponding mitigation strategies.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126795"},"PeriodicalIF":11.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large-scale analysis of photovoltaic, photovoltaic-thermal, and solar thermal systems in high-density urban environments","authors":"Arash Kazemian,&nbsp;Changying Xiang","doi":"10.1016/j.apenergy.2025.126765","DOIUrl":"10.1016/j.apenergy.2025.126765","url":null,"abstract":"<div><div>Urban solar energy deployment in high-density environments is often limited by rooftop availability, building height, and shading. This study presents a robust, data-driven framework integrating high-resolution Geographic Information System data, 3D building models, and detailed urban morphology to evaluate the potential of various solar technologies, including standard photovoltaic systems, photovoltaic-thermal (PVT) systems (e.g., using water, air, or refrigerant as heat transfer media), and solar thermal systems (e.g., flat-plate or evacuated tube collectors with water or air). Using Hong Kong as a case study, the analysis highlights the impact of urban geometry, showing that incorporating shading reduces rooftop solar radiation by 31 %. Among the technologies assessed, photovoltaic-thermal systems demonstrate the highest combined energy yield, generating approximately 15.99 TWh per year (electricity and heat) from 40 % rooftop utilization. Of this, electricity accounts for 4.0 TWh/year—about 8.9 % of Hong Kong's total electricity consumption (44.8 TWh in 2022), which comprises 33 % of its final energy use. In the residential sector, cooling and hot water each account for 25–26 % of energy demand, emphasizing the value of combined thermal and electrical outputs. Thermal results represent theoretical maximums, as building-specific thermal demands were not modelled. This deployment could offset up to 30.8% of current energy imports, lower NOₓ emissions by 44.3%, and decrease smog-forming pollutants by 8.6%. The proposed framework offers a scalable, transferable approach to urban energy planning, enabling cities worldwide to harness rooftop solar energy more effectively for sustainability and climate resilience.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126765"},"PeriodicalIF":11.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CO2-compensated natural gas economically beats synthetic methane","authors":"Wolfgang Männer ,&nbsp;Joshua Fragoso García ,&nbsp;Benjamin Lux ,&nbsp;Giovanni Sansavini ,&nbsp;Frank Sensfuß","doi":"10.1016/j.apenergy.2025.126327","DOIUrl":"10.1016/j.apenergy.2025.126327","url":null,"abstract":"<div><div>CO₂-neutral carbon-based gases, such as synthetic methane, offer high volumetric energy density and serve as viable greenhouse gas (GHG) mitigation measures for various end uses, including industrial processes and heating. Synthetic methane can utilize existing natural gas infrastructure, minimizing the need for demand-side transformation. Synthetic methane production requires sustainable carbon sources, such as direct air carbon capture (DACC) and electricity-based hydrogen from energy-intensive electrolysis (renewable hydrogen path). Alternatively, sustainable carbon can be used to compensate for CO₂ emissions from fossil natural gas (natural gas path). In this study, we design a comparative framework to show that the economic competition between synthetic methane and CO₂-compensated fossil natural gas is independent of CO₂ supply costs. We revise and consolidate literature supply costs of synthetic methane from potential exporting countries and compare them to costs of CO₂-compensated fossil natural gas. In addition, we compare the supply chain emissions of both pathways. The results indicate that synthetic methane is only cost-competitive when fossil natural gas prices exceed 74 EUR/MWh in 2030 and 52 EUR/MWh in 2050 in the Tech_progressive scenario with progressive technology cost assumptions. The study highlights that a cost-based regulatory approach may favor the natural gas path over the renewable hydrogen path due to the higher cost of synthetic methane. Applying a CO₂ penalty for compensation for supply chain emissions can improve the competitiveness of synthetic methane only for high methane leakage rates and CO₂ costs. This research contributes to the debate on cost-effective methane supply and the role of synthetic methane in promoting energy efficiency and sustainable energy supply. In addition, the developed comparative framework is generally transferable to other carbon-based energy carriers.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126327"},"PeriodicalIF":11.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A stable, reliable and interpretable diffusion model for HVAC FDD with data unavailability","authors":"Ke Yan ,&nbsp;Jian Bi ,&nbsp;Hua Wang ,&nbsp;Yuan Gao ,&nbsp;Afshin Afshari","doi":"10.1016/j.apenergy.2025.126774","DOIUrl":"10.1016/j.apenergy.2025.126774","url":null,"abstract":"<div><div>Data-driven fault detection and diagnosis (FDD) methods are emerging and attractive techniques for smart energy management in buildings, including the energy management in heating, ventilation, and air conditioning (HVAC) sub-systems. However, the real-world deployment of FDD in HVAC is hindered by data unavailability scenarios. In the past few years, various data augmentation methods, such as the generative adversarial network (GAN), have been proposed to address the abovementioned problem. However, these data augmentation methods suffer from stability, reliability, and interpretability issues. This paper proposes an interpretable ensemble learning-based diffusion model (IELDM) for HVAC systems, generating stable, reliable synthetic datasets to address the data unavailability issue. A split-gain-based method is introduced in IELDM to enhance the interpretability of the overall machine learning framework. Experimental results show that IELDM stably boosts FDD accuracy under extremely limited fault data, with improvements of up to 11.2 %, 13.2 %, and 12.08 % across three HVAC systems, clearly outperforming current state-of-the-art methods. By systematically overcoming the challenges of instability, unreliability, and lack of interpretability in current generative models, this work offers a robust solution to close the application gap of HVAC FDD in practical building energy systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126774"},"PeriodicalIF":11.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking minute-level battery incremental capacity analysis construction using deep learning and multi-sequence alignment","authors":"Haichuan Zhao ,&nbsp;Qiao Peng ,&nbsp;Xizhe Zheng ,&nbsp;Jinhao Meng","doi":"10.1016/j.apenergy.2025.126763","DOIUrl":"10.1016/j.apenergy.2025.126763","url":null,"abstract":"<div><div>Incremental capacity analysis (ICA) is crucial for accurate, non-destructive lithium-ion battery degradation diagnosis, particularly for loss-sensitive electric vehicle (EV) applications. However, conventional ICA requires low-current charging over several hours, making it impractical under the EVs' multi-stage fast-charging conditions. Thus, this work unlocks a minute-level ICA construction framework for non-destructive mechanism diagnosis using stochastic charging segments. The multi-sequence alignment technique establishes the equivalent match between partial voltage segments and the ICA curve to eliminate conventional ICA data collection constraints. A residual-based convolutional neural network (R-CNN) is developed to achieve rapid and accurate ICA curve construction through feature fusion. Results demonstrate that 30 points collected within 5 min (starting from an arbitrary initial capacity) are sufficient for reliable ICA curve construction with the average mean absolute error (MAE) less than 0.061 Ah/V, and the average absolute percentage error (APE) less than 7.734 % for ICA peak estimation. The robustness of the proposed method under different working conditions has been verified. Through transfer learning, it is possible to adapt the pre-trained model to multiple fast-charging policies. Furthermore, the quantitative degradation mechanism from the rapidly constructed ICA curves facilitates practical electrode-level non-destructive battery diagnostics. This work can provide new perspectives for the characterization of battery degradation under fast-charging conditions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126763"},"PeriodicalIF":11.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A synergistic EV charging framework for smart cities with commitment-driven penalty mechanism and preference-based optimal charging source selection","authors":"Rajapandiyan Arumugam,&nbsp;Thangavel Subbaiyan","doi":"10.1016/j.apenergy.2025.126780","DOIUrl":"10.1016/j.apenergy.2025.126780","url":null,"abstract":"<div><div>The rapid growth of electric vehicle (EV) adoption poses significant challenges to the existing grid infrastructure and demands various advanced energy management strategies. Among the emerging solutions, coordinated charging frameworks like Grid-to-Vehicle (G2V) and Vehicle-to-Vehicle (V2V) paradigms have proven considerable potential in optimizing energy distribution, reducing peak demand, and enhancing the flexibility and resilience of smart energy systems. However, relying solely on G2V could lead to congestion during peak hours, and V2V risks unreliable participation. Despite progress in both domains, integrating their trading mechanisms for optimal pricing remains a challenge. This study presents a novel synergistic energy management framework that combines the cooperative G2V and V2V energy trading with penalty enforcement and a user preference-based charging source selection mechanism to ensure reliable participation. A dynamic pricing mechanism is formulated using a multi-armed bandit reinforcement learning model to optimize economic outcomes for both energy demanding EVs and energy-supplying entities, such as supplying electric vehicles and charging stations. The proposed framework employs a Gale-Shapley based cooperative matching protocol enhanced with preference-based charging source selection, and a novel penalty model based on EV default behavior to ensure efficient and stable pairings while incorporating individual rationality. Simulation results across multiple case scenarios demonstrate that the proposed framework significantly improves schedule adherence, participant's welfare, matching optimality, and energy trading reliability. The findings underscore the potential of the framework for real-world implementation in achieving cost-effective, practical, and reliable energy trading across dynamic mobility scenarios.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126780"},"PeriodicalIF":11.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immersion cooling enabled thermal runaway prevention in overcharged batteries: Mechanisms and metrics","authors":"Jiaxing Li ,&nbsp;Jingrong Ou ,&nbsp;Shaohong Zeng ,&nbsp;Long Chen ,&nbsp;Yajun Qiao ,&nbsp;Zijian Tan ,&nbsp;Yubai Li ,&nbsp;Weixiong Wu","doi":"10.1016/j.apenergy.2025.126798","DOIUrl":"10.1016/j.apenergy.2025.126798","url":null,"abstract":"<div><div>Immersion cooling (IC) is an effective thermal management strategy for batteries. However, experimentally validation of its chemical compatibility and quantitative impacts on suppressing thermal runaway (TR) in large-capacity lithium‑iron-phosphate (LFP) batteries under overcharge conditions remains limited. In this study, we designed three cooling modes: fully immersed (FI), non-immersed safety valve (NISV), and non-immersed (NI). The experimental results indicate that the FI mode significantly suppresses TR by limiting both the maximum battery temperature and temperature rise rate. In particular, TR can be completely prevented at a 1/3 charging rate (C), with the maximum temperature limited to 110.45 °C and a minimal temperature rise rate of 0.15 °C/s. Furthermore, the FI mode enhances the battery release capacity by 35 %–40 % before the occurrence of internal short circuits (ISC) under the 1/3C overcharge condition, while limiting the voltage increase. However, this capacity-enhancement effect diminishes with an increase in the overcharge rates. Thermal profiling indicates that the FI mode exhibits superior heat dissipation, with significantly lower immersion liquid temperature and temperature rise rate when compared with the NISV mode. Safety evaluation with adoptable metrics further presents hazard scores of 0.206 for FI, 0.342 for NISV, and 0.955 for NI, indicating that IC technology significantly mitigates battery hazards, albeit with a more modest impact on TR risk. Additionally, the proposed hydrocarbon-based IC demonstrated promising chemical compatibility with the battery components (e.g., electrodes, electrolytes). This study highlights the safety benefits of IC technology for LFP batteries during overcharge and presents valuable guidelines for applications in electric vehicles and grid-scale energy storage systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126798"},"PeriodicalIF":11.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time proactive control of cascading failures in integrated electricity–gas systems based on a privacy-preserving physics informed deep operator surrogate model","authors":"Jiachen Zhang,&nbsp;Qinglai Guo,&nbsp;Yanzhen Zhou,&nbsp;Hongbin Sun","doi":"10.1016/j.apenergy.2025.126791","DOIUrl":"10.1016/j.apenergy.2025.126791","url":null,"abstract":"<div><div>As the coupling between the power system and the gas network increases, the risk of fault propagation between the two systems also escalates, jeopardizing the safe operation of integrated energy systems. However, the computational inefficiency of dynamic energy flow analysis using traditional numerical methods makes it challenging to meet the requirements of real-time emergency control. Additionally, direct model and data sharing between these systems remain impractical. To address these challenges, this paper presents fast proactive control for cascading failures in integrated electricity and gas systems (IEGS), leveraging physics informed gas network surrogate model to significantly expedite the security analysis process. The proposed framework integrates physics informed Deep Operator Neural Network (PI-DeepONet) for fast energy flow computation under fault conditions, coupled with an autoencoder for data compression and encryption. The proposed method is further combined with a real-time application algorithm for proactive control. Numerical case studies demonstrate that the method effectively predicts the dynamics of the gas network, while ensuring the privacy of operational data and models. Besides, the proactive control signals calculated by the proposed method provide the power system with available escape time to respond to the faults in the gas network, thereby reducing potential losses.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126791"},"PeriodicalIF":11.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}