Applied Energy最新文献

{"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}

{"title":"A new application study of liquefied natural gas in assisting in the start-up of the liquid air energy storage system without sufficient cold storage energy","authors":"Yufei Zhou ,&nbsp;Hanfei Zhang ,&nbsp;Shuo Liu ,&nbsp;Jin Huang ,&nbsp;Xingqi Ding ,&nbsp;Liqiang Duan ,&nbsp;Umberto Desideri","doi":"10.1016/j.apenergy.2025.126794","DOIUrl":"10.1016/j.apenergy.2025.126794","url":null,"abstract":"<div><div>Liquid air energy storage (LAES) is a highly promising large-scale energy storage technology, with the cold energy cycle being a key part. However, during the initial start-up of an LAES system or its restart after a prolonged maintenance, the system may face a cold storage energy deficiency, and how to establish the cold energy cycle under such conditions has not been adequately addressed in existing studies. Based on a dynamic model of an LAES system, this study first investigates the process and performance of air liquefaction without cold storage energy, relying solely on the throttling cooling effect of air. Subsequently, a novel scheme and evaluation metrics are proposed for using the additional cold energy from liquefied natural gas (LNG) to assist in the start-up of the LAES system without cold storage energy, and the performance of this process is analyzed. The results show that when cold storage energy is sufficient, the liquid air begins to form approximately 30 s after start-up. However, when cold storage energy is deficient, it takes about 844 s to generate liquid air, and the maximum liquid yield is only 2.71 %, leading to around a 30-fold increase in the time required to fill up the liquid air tank (LAT) compared to the rated charging duration. By introducing the LNG, the liquid air can be produced 92 s after start-up under optimal conditions, with a maximum liquid yield of 41.7 %. The time required to fill up the LAT is reduced to 1/15.2 of the required time of the LAES system not using external cold energy. Additionally, the lower the LNG operating pressure, the faster the air liquefaction process, and the less the total LNG consumption. The findings of this study provide a viable contingency strategy for cold storage energy deficiency in LAES systems caused by any possible factors, contributing to the development of robust start-up procedures and enhancing system reliability.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126794"},"PeriodicalIF":11.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118318","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":"Toward a representative accelerated stress test for PEMFC stacks in automotive applications","authors":"Taha Al Rafei ,&nbsp;Nadia Yousfi Steiner ,&nbsp;Elodie Pahon ,&nbsp;Daniel Hissel","doi":"10.1016/j.apenergy.2025.126790","DOIUrl":"10.1016/j.apenergy.2025.126790","url":null,"abstract":"<div><div>An accelerated stress test at stack level was developed and validated to replicate the main aging mechanisms observed in automotive applications using a commercial Proton Exchange Membrane stack. The test achieved approximately 10 % voltage loss at 1.0 A.cm⁻² after 130 h of operation. Electrochemical impedance spectroscopy and distribution of relaxation times analyses revealed a 30 % decrease in the proton transfer time constant at a current density of 0.2 A.cm⁻², alongside an average increase in charge transfer resistance of 47 % at 0.2 A.cm⁻² and 53 % at 0.5 A.cm⁻², indicating degradation of key components—the membrane and catalyst layer. A reference test based on actual bus data was also used to validate the AST and to calculate the degradation acceleration factor. The accelerated stress test effectively accelerated degradation at the stack level by simulating operational conditions that replicate in-field aging mechanisms, providing a benchmark toward a viable stack durability assessment.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126790"},"PeriodicalIF":11.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118322","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":"Progress in underground thermal energy storage: research contents, hotspots, and development trends","authors":"Renfeng Wei ,&nbsp;Yong Li ,&nbsp;Yanfeng Liu","doi":"10.1016/j.apenergy.2025.126725","DOIUrl":"10.1016/j.apenergy.2025.126725","url":null,"abstract":"<div><div>Existing reviews on underground thermal energy storage (UTES) are often fragmented and lack analysis of the spatial-temporal evolution of research hotspots. This study aims to provide an objective and comprehensive analysis of the developmental trajectory and research trends in the global UTSES field. This study utilizes 7705 documents published over the past 30 years as its data source to conduct bibliometric and content analysis using knowledge graph techniques. It focuses on three core issues: research frontiers and technology maturity, core bottlenecks, and future trends and cost characteristics. The aim is to overcome the limitations of traditional qualitative reviews and establish a data-driven, multi-dimensional analytical framework. The results indicate that the UTES field has undergone three stages of development: embryonic, stable growth, and rapid expansion, with large-scale commercialization expected by 2065. Aquifer thermal energy storage (ATES) focuses on heat-fluid-solid coupling optimization. Borehole thermal energy storage (BTES) emphasizes the integration of phase-change materials (PCMs) with renewable energy. Energy piles (EPs) serve as a critical key link between underground structures and energy systems. Rock thermal energy storage (RTES) is shifting toward high-temperature material innovations, with EP demonstrating significantly higher research intensity. The cost ranking is as follows: EP &lt; (BTES, abandoned mine type; ATES, with existing well reuse) &lt; (BTES, without existing wells) &lt; (ATES, without existing wells) &lt; RTES. Through quantitative analysis and prediction models, this study provides a scientific basis for UTES technological innovation, collaboration establishment, and policy formulation. Moreover, it significantly enhances the scientific rigor of thermal storage system design and engineering translation efficiency.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126725"},"PeriodicalIF":11.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118325","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 multi-level isobaric hydrogen-electric coupled energy storage system with a wide-range operational strategy: enhancing efficiency and flexibility in renewable-dominated power grid","authors":"Ning Ma,&nbsp;Pan Zhao,&nbsp;Wenpan Xu,&nbsp;Aijie Liu,&nbsp;Huichao Zhu,&nbsp;Zhaochun Shi,&nbsp;Jiangfeng Wang","doi":"10.1016/j.apenergy.2025.126783","DOIUrl":"10.1016/j.apenergy.2025.126783","url":null,"abstract":"<div><div>Fluctuations in electricity caused by high penetration of renewable energy sources impose greater demands on energy storage systems for flexible absorption and efficient power supply. This paper proposes a novel integration strategy that combines the efficiency of isobaric energy storage technology with the flexibility of hydrogen-electric hybrid energy storage technology. Based on this, a multi-level net-zero emissions isobaric hydrogen-electric coupled energy storage system is developed. The system offers a wide range of power consumption capabilities, and its effectiveness has been validated in real-world scenarios with high renewable energy penetration rates when combined with an adaptive power allocation scheme. The results indicate that the system accommodates five charge modes and four discharge modes, with peak efficiencies achieved in charge mode I (85.17%) and discharge mode II (46.66%). The wide-range coordinated control framework maintains higher compressor isentropic efficiencies while expanding the adjustable power range by 45.28% compared to series configurations using only VS control, operating in a coordination control mode with variable speed and inlet guide vanes below rated power, and switching to variable speed mode above rated power. The adaptive power allocation scheme categorizes the imbalanced power into eight scenarios, keeping the CO₂ compressor operates under design conditions during the charging process, while the discharge side consistently utilizes the low-pressure turbine as a stable power source. Compared with the conventional strategy, the proposed approach demonstrates improvements of 3.49% and 5.12% in average charge and discharge efficiencies, respectively, while reducing wind curtailment and mismatch power by 22.85% and 30.69%, respectively. In addition, parameter analysis indicates that the mutual constraints among efficiency and reliability index parameter adjustments must be comprehensively considered to enhance overall performance and effectively suppress unbalanced power in practical applications.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126783"},"PeriodicalIF":11.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118321","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}