Energy Storage最新文献

{"title":"Porous Carbon Derived From Food Waste for Asymmetric Supercapacitor","authors":"Khang Huynh,&nbsp;Isamu Umeda,&nbsp;Bharath Maddipudi,&nbsp;Anuradha Shende,&nbsp;Sandeep Kumar,&nbsp;Rajesh Shende","doi":"10.1002/est2.70230","DOIUrl":"https://doi.org/10.1002/est2.70230","url":null,"abstract":"<div>\u0000 \u0000 <p>Globally, by 2030, it is estimated that about 2 billion tons of food waste will be generated. This will not only cause economic losses but will also lead to serious environmental issues such as the emission of greenhouse gases (GHGs), bad odor, and land pollution due to the decomposition of food waste in an open environment and landfills. It is imperative to develop novel solutions to reduce food waste and perhaps valorize it into a valuable product, thereby reducing its environmental and economic impacts. Food waste can be considered a renewable and sustainable feedstock that can be used for chemical and biological processing for its valorization. In this investigation, hydrochar is derived from the hydrothermal carbonization (HTC) of food waste and subjected to chemical activation with potassium hydroxide (KOH), followed by thermal treatment at 800°C to produce porous carbon (POC). As-prepared POC is thoroughly characterized by Brunauer–Emmett–Teller (BET) surface area analyzer, Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/EDX), and transmission electron microscopy (TEM). A specific capacitance of 112 F/g at 0.5 A/g current density is observed for POC in the three-cell standard electrochemical setup while asymmetric supercapacitor (ASC) fabricated with POC and Cu-ferrite electrodes exhibited energy and power densities of 29 Wh/kg and 1.36 kW/kg, respectively. Preliminary cost analysis shows a significantly lower cost for the POC derived from food waste than for a few other biomass feedstocks.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144647104","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":"Ion Conduction Mechanism and Super Capacitor Performance of Polymer Electrolyte Incorporated With Ionic Liquid","authors":"Ibrahim Zakariya'u,&nbsp;Sehrish Nasir,&nbsp;Neelam Rawat,&nbsp;Shubham Kathuria,&nbsp;Markus Diantor,&nbsp;I. M. Noor,&nbsp;Pramod Kumar Singh","doi":"10.1002/est2.70223","DOIUrl":"https://doi.org/10.1002/est2.70223","url":null,"abstract":"<div>\u0000 \u0000 <p>In the present work, highly conducting polymer electrolyte films are prepared by integrating Polyvinyl-pyrrolidone (PVP) with sodium iodide (NaI) salt. To further improve performance, different concentrations of an ionic liquid, 1-ethyl-3-methylimidazolium thiocyanate, were added to the optimized polymer matrix containing salt through the solution casting method. Experiments with complex impedance spectroscopy identified conductivity, and the electrochemical stability window was measured using linear sweep voltammetry. The number of charge carriers (<i>T</i>_ion) is studied using Wagner's DC polarization method. A notable increase in conductivity was recorded after the addition of the ionic liquid to the maximum conductive polymer-salt system. Fourier transform infrared (FTIR) spectroscopy validated the composite structure and the complexation within the matrix. Additionally, polarized optical microscopy indicated a decrease in crystallinity and an increase in amorphous content because of interaction with both the salt and the ionic liquid. The resulting highly conductive polymer electrolyte, achieved by combining the salt and ionic liquid, and previously reported activated carbon-based electrodes are utilized to fabricate an electrical double-layer capacitor (EDLC). The EDLC cell is further studied using various electrochemical tools such as EIS, CV, and GCD.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144647408","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":"Development of NiS and Biomass-Derived Carbon Spheres Composite for High-Performance Supercapacitor Electrodes","authors":"Mahima Sheoran,&nbsp;Rohit Sharma,&nbsp;Sunil Ojha,&nbsp;Anit Dawar,&nbsp;Om Prakash Sinha","doi":"10.1002/est2.70224","DOIUrl":"https://doi.org/10.1002/est2.70224","url":null,"abstract":"<div>\u0000 \u0000 <p>The development of innovative and efficient energy storage technologies has become a critical concern in modern society due to the ongoing depletion of conventional power reserves and increasing environmental pollution. To address the rising current and future energy demands, it is imperative to implement a “Green” strategy that leverages the numerous accessible energy sources, minimizing environmental impact while deriving value from waste. Consequently, this study reports the synthesis of a nickel sulfide and onion peel-derived carbon sphere composite (NiS/OPCS) through an economical hydrothermal process. The synthesized composite has been optimized using various characterization techniques. Electrochemical performance was optimized through cyclic voltammetry (CV), galvanostatic charge–discharge (GCD), and electrochemical impedance spectroscopy (EIS) analyses. The NiS/OPCS composite demonstrated exceptional retention of 95.66% after 6000 cycles, with a significant specific capacitance value of 707 F/g at 1 A/g current density. Both capacitive-controlled and diffusion-controlled mechanisms were found to contribute significantly to charge storage. Therefore, NiS/OPCS is a promising candidate as an electrode material for supercapacitor applications.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624755","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":"EIS Mimicking DC Measurement Technique: A Novel Path for Battery Aging Analysis","authors":"Sabri Hakan Sakallıoğlu,&nbsp;Koray Bahadır Dönmez,&nbsp;Burak Onur","doi":"10.1002/est2.70229","DOIUrl":"https://doi.org/10.1002/est2.70229","url":null,"abstract":"<div>\u0000 \u0000 <p>Electrochemical impedance spectroscopy (EIS), an alternating current (AC) technique, is commonly employed to monitor the aging process of lithium-ion batteries (LIBs). However, its use requires sophisticated electrochemical equipment, which not only complicates battery management systems (BMS) but also raises overall costs. Moreover, analyzing EIS data often requires expert-level interpretation. In this study, we investigated the applicability of various direct current (DC) methods for tracking the total internal resistance (T-IR) during the aging process. We evaluated the accuracy of their potential use in estimating the State of Health (SoH). The performance of these DC methods was compared with classical EIS techniques to identify the most reliable conditions for accurate SoH estimation. Among the techniques explored, one method involved applying a low current to the battery and determining T-IR based on the real-time voltage response, thereby mimicking the EIS approach. This method demonstrated the highest accuracy compared to classical EIS results. Additionally, we evaluated the impact of high-DC pulses on T-IR and analyzed its variation with the state of charge (SoC), comparing these findings with EIS-derived data. Our results indicate that low-DC techniques not only provide reliable T-IR measurements but also offer a cost-effective and simpler alternative for SoH monitoring in BMS and laboratory applications. The EIS-mimicking low-DC approach, in particular, shows promise as a versatile tool for determining the T-IR of electrochemical cells under various operational scenarios.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624754","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":"Waterwheel Plant Algorithm and Capsule Attention Convolutional Neural Networks for Optimal Sizing Framework for Photovoltaic-Battery EV Charging Microgrids","authors":"R. Raja,&nbsp;R. Geetha,&nbsp;Vemana U. P. Lavanya,&nbsp;G. Indumathi","doi":"10.1002/est2.70222","DOIUrl":"https://doi.org/10.1002/est2.70222","url":null,"abstract":"<div>\u0000 \u0000 <p>The increasing use of electric vehicles (EVs) highlights the importance of energy management (EM) and particularly photovoltaic (PV)-battery microgrids (MGs). However, the conventional optimization methodologies are not always capable of striking an optimal balance between cost, energy, and size of the system, considering uncertainties such as the variability of solar resource and the fluctuating demand of charging of EVs. This paper proposes a hybrid method for the optimal sizing framework for PV-battery EV charging MGs. The proposed method is the combined execution of the waterwheel plant algorithm (WWPA) and capsule attention convolutional neural networks (CACNN). Thus, the proposed method is referred to as the WWPA-CACNN approach. The goal of this work is to achieve optimal sizing of PV-battery systems, enhancing energy utilization, cost-efficiency, and grid independence. The WWPA is used to optimize the sizing of PV panels and battery storage to minimize costs and maximize energy utilization in EV charging MGs. The CACNN is used to predict energy generation, storage, and demand, ensuring accurate forecasting and system adaptability. By then, the proposed method is simulated on the MATLAB platform and compared with various existing methods like particle swarm optimization (PSO), artificial neural network (ANN), non-dominated sorting genetic algorithm-II (NSGA-II), modified snake optimization (MSO), and dung beetle optimizer (DBO). The proposed WWPA-CACNN method also has the lowest total lifetime cost of $12 730 and high efficiency of 96%, which underlines its better overall performance to effectively manage PV-battery EV charging MGs at optimal cost.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606460","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":"Remaining Useful Life Prediction of Li-NMC Batteries Using Algorithmic Fusion and Cascading Approach","authors":"Sreejaun Thothaathiri Janaki,&nbsp;Naresh Gnanasekaran,&nbsp;Dinesh Kumar Madheswaran,&nbsp;Praveenkumar Thangavelu,&nbsp;Sivanesan Murugesan","doi":"10.1002/est2.70219","DOIUrl":"https://doi.org/10.1002/est2.70219","url":null,"abstract":"<div>\u0000 \u0000 <p>Lithium-NMC batteries in electric vehicles exhibit complex degradation mechanisms, where capacity fade, internal resistance growth, and discharge behavior evolve nonlinearly under varying operating conditions. Accurate remaining useful life prediction necessitates capturing these intricate interdependencies, which traditional models fail to generalize effectively. This study develops a robust machine-learning framework leveraging experimental cycling data under nominal and over-discharge conditions. Key parameters like voltage, discharge time, internal resistance, and state of health were chosen due to their direct correlation with electrochemical aging, resistive losses, and failure progression, ensuring high sensitivity to degradation dynamics. Support Vector Regression and Bayesian-optimized Lasso Regression were employed to model these dependencies, providing precise predictions of key battery health indicators. A hybrid framework integrating these models for remaining useful life estimation achieved <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>R</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {R}^2 $$</annotation>\u0000 </semantics></math>, MAE, RMSE of 0.9998, 0.093 and 0.138 respectively, significantly outperforming conventional approaches. Rigorous evaluation through K-fold cross-validation and subset stability analysis ensured generalizability across diverse operating conditions. Benchmark comparisons with state-of-the-art methods demonstrated superior predictive accuracy. By addressing critical limitations in traditional degradation modeling, this work provides a scalable, data-driven solution for real-time battery health management, enhancing the reliability and sustainability of electric vehicle applications.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606461","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":"Performance Study of Gel Polymer Electrolyte-Based EDLC With Silver-Coated Textile Substrate for Integrated PV-EDLC System","authors":"W. L. Leong,&nbsp;Grishika Arora,&nbsp;Nuur Syahidah Sabran,&nbsp;H. K. Jun","doi":"10.1002/est2.70221","DOIUrl":"https://doi.org/10.1002/est2.70221","url":null,"abstract":"<div>\u0000 \u0000 <p>A novel integrated energy storage system combining a flexible silver-coated textile-based electric double-layer capacitor (EDLC) with commercial solar photovoltaics (PV) has been successfully developed. This system, capable of bending up to 180°, demonstrates excellent mechanical flexibility, wearability, and energy performance, addressing key limitations in conventional EDLCs such as rigidity, bulkiness, and low energy density. The textile-based EDLC, fabricated using a gel polymer electrolyte (GPE), achieved a high specific capacitance of 2.89 mF cm⁻² (71.32 F kg⁻¹), along with energy and power densities of 12.5 and 13.2 mW cm⁻², respectively. Notably, the device retained 100% capacitance over short cycling bursts, confirming its stability and reliability. What distinguishes this work is the use of silver-coated textile as both a current collector and flexible substrate, offering biocompatibility, high conductivity, and excellent integration with garments. This material choice allows higher mass loading and enhanced charge storage without compromising comfort or flexibility. When integrated with a flexible solar PV, the system achieved an overall power conversion efficiency of 2.2% and demonstrated rapid charging capability, powering a simple electronic device for 30 min after just 5 s of sunlight exposure. This study presents a low-cost, lightweight, and durable solution for next-generation wearable electronics, pushing forward the development of flexible, textile-based PV-EDLC systems by overcoming the limitations of existing rigid or semi-flexible designs.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581851","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":"An Optimized Bi-LSTM Machine Learning Model for Predicting Congestion at Electric Vehicle Charging Stations","authors":"Sourav Sarkar,&nbsp;Jenson Narzary,&nbsp;Debasis Chaterjee,&nbsp;Amarjit Roy,&nbsp;Chiranjit Sain,&nbsp;Anubav Agarwal,&nbsp;F. Ahmad","doi":"10.1002/est2.70216","DOIUrl":"https://doi.org/10.1002/est2.70216","url":null,"abstract":"<div>\u0000 \u0000 <p>The proliferation of electric vehicles (EVs) has intensified the need for efficient and reliable charging infrastructure. This study introduces a bidirectional long short-term memory (Bi LSTM)-based model designed to predict and manage congestion at EV charging stations. Leveraging the advanced capabilities of Bi LSTM networks in handling sequential data, our model analyzes historical and real-time data to forecast congestion levels. The bidirectional nature of the LSTM allows for a comprehensive analysis of the data, capturing dependencies from both past and future contexts. The proposed model aims to provide real-time intimation to both users and operators, enhancing decision-making processes and optimizing the utilization of charging resources. By offering accurate predictions of congestion, the Bi LSTM-based model facilitates strategic planning for station deployment and user navigation, ultimately improving the overall efficiency of the charging infrastructure. Experimental results demonstrate the model's efficacy in accurately predicting congestion, significantly reducing wait times, and improving user satisfaction. This research underscores the potential of advanced machine learning techniques, particularly Bi LSTM networks, in addressing the dynamic challenges of EV charging station management. The implementation of such predictive models is a crucial step toward the development of a smart, efficient, and user-centric EV charging ecosystem.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581850","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":"Thermal Performance Assessment of Nano-Enhanced Phase Change Material-Based Building Envelopes for Tropical Climatic Cities in India","authors":"Jagadeesan Dhayanithi,&nbsp;Tapano Kumar Hotta","doi":"10.1002/est2.70220","DOIUrl":"https://doi.org/10.1002/est2.70220","url":null,"abstract":"<div>\u0000 \u0000 <p>The manuscript aims to assess numerically the thermal performance of building envelopes (integrated with nano-enhanced phase change material) for the tropical climatic cities in India during the hottest months of May/June. A building envelope of size 5 m (Length) × 4 m (Width) × 3.5 m (Height) integrated with a 25 mm thick Rubitherm phase change material layer on its walls and roof is considered for the analysis. The numerical model is developed using the Design-Builder software by considering the dissimilar Indian climatic zones (composite, hot-dry, and warm-humid) and buildings of the different Indian cities (Delhi, Ahmedabad, and Chennai). The goal is to reduce the peak temperature in the buildings of these cities. The maximum solar intensity for these cities is found between 11 AM to 1 PM for the hottest month, May/June of the year, which is very high (22 kW) in Delhi, followed by Chennai (20 kW), and then Ahmedabad (18 kW). The phase change material stores heat during the peak hours (daytime) and releases the same during the off-peak hours (nighttime) when integrated into the building envelopes. The results show that the building temperature was reduced by 4°C, 5.8°C, and 1.2°C for Delhi, Ahmedabad, and Chennai, respectively. The heat gain reduction from conventional buildings compared to the phase change material-integrated buildings of Delhi, Ahmedabad, and Chennai is 33.47, 35.59, and 26.14 kWh/m², respectively. The thermal enhancement ratio, which captures the building performance with and without using phase change material, is calculated as 0.988, 0.982, and 0.996 for Delhi, Ahmedabad, and Chennai, respectively. This confirms the significant role of phase change material in lowering the peak loads in Indian buildings.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519715","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":"Enhancing Stability and Performance of Grid-Connected Residential PV Systems With Battery-Super Capacitor Storage Using Advanced Control Techniques","authors":"V. Pushpabala,&nbsp;C. Christober Asir Rajan","doi":"10.1002/est2.70202","DOIUrl":"https://doi.org/10.1002/est2.70202","url":null,"abstract":"<div>\u0000 \u0000 <p>The increasing integration of renewable energy technologies poses significant challenges to the power grid due to generation unpredictability. Variations in output, driven by weather uncertainties, highlight the need for effective storage solutions to maintain grid stability and reliability. This research proposes a novel approach for a grid-connected residential photovoltaic (PV) system incorporated with a hybrid energy storage system (HESS) comprising a battery bank and a super capacitor (SC) pack. The novelty of this paper lies in the innovation of the Red Panda Optimization (RPO) and Efficient Predefined Time Adaptive Neural Network (EPTANN). Hence, the method is named RPO-EPTANN. The objective of the proposed method is to enhance stability, reduce voltage overshoot, improve efficiency, and reduce the system's entire cost. The converter's control signal is optimized using the proposed RPO, and the EPTANN predicts the converter's ideal control signal. By then, the proposed approach is put into practice from the MATLAB working platform, and the findings are calculated using the existing process. The proposed strategy outperforms all current approaches in terms of Particle Swarm Optimization (PSO), Artificial Neural Networks (ANN), and Artificial Rabbits Optimization (ARO). The existing methods exhibit total system costs of 27 660$, 29 665$, and 30 025$, whereas the proposed method achieves a significantly lower cost of 24 540$. Efficiency of 85%, 75%, and 62% in the existing approaches are improved to 98% with the proposed method. These findings indicate that the proposed RPO-EPTANN method significantly reduces operational costs while enhancing overall system efficiency. This reflects a substantial advancement in performance, ensuring improved stability, reliability, and energy optimization in grid-connected residential PV systems.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"7 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482294","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}