Applications in Energy and Combustion Science最新文献

{"title":"Comprehensive characterisation of PM emissions from old and modern boilers using wood and fossil fuels","authors":"Kamil Krpec ,&nbsp;Oleksandr Molchanov ,&nbsp;Lenka Kuboňová ,&nbsp;Jenn-Kun Kuo","doi":"10.1016/j.jaecs.2025.100441","DOIUrl":"10.1016/j.jaecs.2025.100441","url":null,"abstract":"<div><div>In light of the increasing number of decommissioned old small-scale combustion appliances, there is great anticipation for a significant improvement in local air quality. However, even modern appliances emit harmful pollutants, necessitating monitoring and reduction to mitigate their impact on the environment. The study compares particulate matter number and mass concentrations in the flue gas from commonly used boiler types. Low-Pressure Cascade Impactor (DLPI) to determine the mass concentration of individual dust fractions was used, as well as an Electrical Low-Pressure Cascade Impactor (ELPI) and a particle mobility sizer SMPS 3936 (TSI). Beyond appliance design, the type and quality of fuel emerged as significant factors influencing flue gas composition. In addition to particulate matter characteristics, measurements of CO, organic gaseous compounds (OGC), and PMx mass concentrations allowed for the determination of correlations among these pollutants. The highest correlation was observed between CO and OGC, with a coefficient of determination (R²) value of 0.98 for coal combustion. The feasibility of replacing older stationary combustion appliances with new ones was demonstrated across several parameters, including specific emissions for the PM2.5 and PM0.1. The reduction between overfire and automatic boilers reached 99.96 % and 93 %, respectively.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100441"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791042","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":"Monitoring thermoacoustic instability: A comparative analysis of feature-based and end-to-end deep learning approaches","authors":"Jikang Wang ,&nbsp;Yichen Wang,&nbsp;Yupeng Qin,&nbsp;Xuan Lv","doi":"10.1016/j.jaecs.2026.100459","DOIUrl":"10.1016/j.jaecs.2026.100459","url":null,"abstract":"<div><div>This paper compares two data-driven frameworks for monitoring thermoacoustic instability (TAI) in a gas turbine combustor. A conventional machine learning approach using handcrafted features is contrasted with an end-to-end deep learning method employing a convolutional autoencoder (CNN-AE). Both frameworks generate a continuous stability index to quantify the transition from stable to unstable states. Using experimental data, both indices successfully track the entire dynamic evolution, including intermittent precursors. Critically, the CNN-AE autonomously learns a physically meaningful latent space. Visualizing this space reveals the system’s trajectory, showing a clear transition from a disordered attractor (combustion noise) to well-defined limit cycles (instability) through distinct topological shifts. The study demonstrates that deep representation learning not only automates monitoring but also provides a powerful tool for uncovering the underlying nonlinear dynamics of TAI.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100459"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977777","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":"DNS of turbulence backscatter in a laboratory-scale swirl stabilized aero-combustor","authors":"Bruno S. Soriano,&nbsp;Jacqueline H. Chen","doi":"10.1016/j.jaecs.2026.100468","DOIUrl":"10.1016/j.jaecs.2026.100468","url":null,"abstract":"<div><div>Classical theories of turbulence suggest that the kinetic energy cascade in nonreacting flows has a net transfer of energy from large to small scales. However, previous studies demonstrated that the thermal expansion of premixed flames can reverse the flux of kinetic energy through the injection of kinetic energy at flame scales into larger scales, resulting in backscatter. These observations were confirmed experimentally for a premixed laboratory-scale aero-combustor presented in Kazbekov and Steinberg (2021). However, it is unknown whether backscatter also occurs in partially-premixed combustion in a swirl-stabilized combustor undergoing localized extinction/reignition near the lean blowoff limit. Near this limit, edge flame propagation was found to exist, and its flame structure is comprised of a fuel rich and fuel lean premixed leading edge that generates an order of magnitude larger thermal expansion due to heat release rate compared to the trailing non-premixed flames, acting as a source of flame-generated backscatter. Direct numerical simulation (DNS) of a laboratory-scale swirl stabilized aero-combustor is performed for a reacting Jet-A liquid spray flame and backscatter is quantified from the filtered and subgrid kinetic energy transport equations evaluated in physical coordinates. It is found that edge flames lead to an inversion of the turbulent kinetic energy cascade at scales close to the flame thermal thickness. The backscatter occurs within the flame brush and correlates with dilatation generated by thermal expansion. The total pressure gradient–velocity correlation term is also observed to produce kinetic energy at subgrid scales due to subgrid fluctuations in the pressure field caused by turbulent combustion. Hence, aero-combustors undergoing blow-off are more prone to backscatter compared to stable conditions due to the development of edge flames that propagate to reignite previously extinguished regions.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100468"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173227","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":"Ester-based immersion coolant demonstrates superior safety for energy storage systems: a comprehensive comparative analysis","authors":"Yihua Qian ,&nbsp;Yaohong Zhao ,&nbsp;Zhi Li ,&nbsp;Qing Wang ,&nbsp;Nian Tang ,&nbsp;Chao Wang","doi":"10.1016/j.jaecs.2025.100436","DOIUrl":"10.1016/j.jaecs.2025.100436","url":null,"abstract":"<div><div>With the increasing demand for high battery energy density and thermal management, immersion liquid cooling has emerged as a promising strategy, while the safety of coolants remains an area requiring further investigation. The study systematically compares the physicochemical properties, thermal stability, combustion behavior, and char residuals of mineral oil-based coolant (MIC), silicone oil-based coolant (SIC), and ester-based coolant (EIC). The results indicated that MIC exhibited a low flash point (199.2 °C), poor thermal stability, and underwent dramatic combustion after ignition. Although SIC formed a protective SiO₂ layer during combustion that inhibited further combustion, it released tremendous smoke particles. In contrast, EIC demonstrated the highest initial decomposition temperature and flash point (261.3 °C), combining low flammability with excellent thermal stability. A comprehensive safety evaluation revealed that EIC offered the best overall safety. This study provides valuable insights for coolant selection in safety-critical energy storage systems.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100436"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737617","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":"Influence of ethanol-blended B7-diesel on in-cylinder combustion characteristic, engine thermal efficiency and emission of a 3L-compression ignition engine","authors":"Teerapat Suteerapongpun ,&nbsp;Poonnut Thaeviriyakul ,&nbsp;Watanyoo Phairote ,&nbsp;Peerawat Saisirirat ,&nbsp;Watcharin Po-ngaen ,&nbsp;Hidenori Kosaka ,&nbsp;Preechar Karin","doi":"10.1016/j.jaecs.2026.100461","DOIUrl":"10.1016/j.jaecs.2026.100461","url":null,"abstract":"<div><div>This study investigated the effects of blending weight ratio of 5% and 10% ethanol (B7E5 and B7E10) with standard B7-diesel on the performance, combustion, and emission characteristics of a light-duty common-rail diesel engine. The engine was tested on a dynamometer across various speeds (1600–2000 rpm) and loads (84 and 112 Nm) to analyze in-cylinder pressure, thermal efficiencies, and exhaust emissions. Results indicated significant emission benefits, especially at high loads. The B7E10 blend reduced smoke intensity by approximately 75% and carbon dioxide emissions by 34% compared to the baseline B7. The performance analysis revealed a critical trade-off associated with the ethanol blends: while the inherent oxygen content in ethanol significantly improved the indicated thermal efficiency (ITE) through enhanced combustion, its lower viscosity simultaneously led to increased frictional losses. Consequently, these competing effects resulted in only a modest improvement in brake thermal efficiency (BTE) and comparable brake-specific energy consumption (BSEC) compared to the baseline B7. The primary objective is to identify the benefits and trade-offs associated with ethanol blending in biodiesel-based diesel fuels that are compatible with existing diesel vehicles.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100461"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977859","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":"Data-driven combustion kinetic model generation with optimized pathway sampling: A case study for fuel ammonia","authors":"Márton Kovács,&nbsp;Hisashi Nakamura","doi":"10.1016/j.jaecs.2025.100438","DOIUrl":"10.1016/j.jaecs.2025.100438","url":null,"abstract":"<div><div>A novel data-driven algorithm, MA-DOPS (Model Assembly with Data-driven Optimized Pathway Sampling), is introduced for the automated generation of combustion kinetic models. The method systematically assembles models by sampling and evolving reaction pathways from published models to minimize simulation error against experimental data. MA-DOPS builds models in a bottom-up manner, incorporating reactions and parameterizations from validated sources, ensuring physically meaningful model generation. The performance of the method was evaluated in three test cases with increasing complexity of the test conditions. First, algorithm control parameters were systematically analysed, identifying configurations that balance computational cost and model accuracy. Second, the constraining potential of different experimental data types was assessed, including micro flow, jet-stirred and tubular flow reactor speciation data, shock tube ignition delay times, and laminar burning velocities. This analysis showed that micro flow reactor measurements provide valuable complementary information for experimental validation conditions, demonstrating their efficiency as target for model development. Finally, a full-scale model generation task for ammonia combustion was carried out. The resulting model was compiled based on rate parameters from 72 source models and it outperformed all individual sources, showing good agreement with a comprehensive reference dataset, particularly excelling in micro flow reactor simulations. These results highlight the utility of the MA-DOPS approach in generating accurate combustion models by combining literature data with systematic algorithmic refinement.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100438"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791040","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":"Streamlined assessment and optimization of sustainable aviation fuels via few-shot learning and tailored genetic algorithm","authors":"Xinrui Ren ,&nbsp;Huixin Yang ,&nbsp;Molly Meng-Jung Li ,&nbsp;Shao-Yuan Leu ,&nbsp;Carol Sze Ki Lin ,&nbsp;Xiaoge Zhang ,&nbsp;Chih-yung Wen ,&nbsp;Christopher Yu Hang Chao ,&nbsp;Song Cheng","doi":"10.1016/j.jaecs.2025.100442","DOIUrl":"10.1016/j.jaecs.2025.100442","url":null,"abstract":"<div><div>Sustainable aviation fuel (SAF) is considered the primary technological pathway to decarbonize the global civil aviation in the short to medium term. However, the scale-up of SAFs has been hampered by the high risk and high cost associated with assessing SAFs. Therefore, this study aims to develop an innovative framework to enable streamlined assessment and optimization of SAFs. A multi-scheme semi-supervised few-shot learning (MSSFSL) framework is first proposed and demonstrated to reconstruct a limited jet fuel database with respect to the information on ASTM-specified jet fuel properties. The database is further expanded via a novel synthetic data generation framework, where the database is enriched from only 48 jet fuel samples to over 2000 jet fuel samples. With the reconstructed and enriched database in conjunction with stacking ensemble learning, an accurate and reliable reciprocating mapping between jet fuel compositions and 14 ASTM-specified jet fuel properties (including density, derived cetane number, six distillation temperatures, flash point, freezing point, heat of combustion, surface tension, and viscosities) is established, based on which excellent prediction accuracy is achieved for practical petroleum-derived jet fuels, SAFs and their blends, with R² values above 0.8 achieved for all properties and relative prediction error reduced by approximately 2 orders of magnitude. Correlation analysis between jet fuel properties and compositions is subsequently conducted using a case study of ATJ, which further reveals the commendable contribution of iso-paraffins and di-cycloparaffins to the performance of SAFs (e.g., heat of combustion and freezing point). A genetic algorithm-based optimization framework is further developed and demonstrated through a case study where the 14 ASTM-specified jet fuel properties of three SAF candidates (i.e., one 100 % SAF and two 50 %/50 % SAF/Jet A blends) are optimized to fall within the ASTM tolerance and the respective property ranges of Jet A and Jet A-1. The proposed framework enables real-time, risk-lean and cost-effective SAF assessment and fine-tuning at lab-scale based on jet fuel standards and feedback from end-users before platform scale-up, which is expected to facilitate SAF development considerably. The developed model is released as a module of the SAFRA_Master_1.0 software package, hosted free of charge at the UHPC laboratory website (<span><span>https://uhpc-lab.org/downloads/</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100442"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737618","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":"Machine learning enhanced time-resolved multi-particle tracking velocimetry in solid fuel particle group combustion","authors":"Haowen Chen,&nbsp;Benjamin Böhm,&nbsp;Tao Li","doi":"10.1016/j.jaecs.2025.100455","DOIUrl":"10.1016/j.jaecs.2025.100455","url":null,"abstract":"<div><div>Particle velocity is an essential parameter in solid fuel combustion studies, however, the accurate detection and tracking of particles in high particle number density (PND) combustion scenario remain challenging. The current study advances the machine-learning particle detection approaches for precise velocity measurements of solid particles. For visualizing particle locations, time-resolved laser Mie scattering experiments were performed for high-volatile bituminous (hvb) coal particles of different size burning in a high-temperature oxidizing laminar flow. The machine learning (ML) based object detection models you only look once (YOLO) and realtime detection transformer (RT-DETR) were trained on the conventional blob detection annotations (weak-label) from low-PND cases and evaluated against the manually labeled images from high-PND cases, which served as ground truth. Particle tracking was then performed using the simple online realtime tracking (SORT) algorithm. The results demonstrate that models trained on a limited set of weak-label data can achieve satisfactory prediction performance in complex environments that are difficult for traditional object detection methods. Slicing aided hyper inference (SAHI) algorithm is implemented for improving the performance of the used ML models. By evaluating the velocity statistics, it is found that the mean particle velocity decreases with increasing PND and particle size, primarily due to stronger particle–gas and particle–particle interactions. The particle dynamics are closely related to the position of volatile combustion zone.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100455"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925453","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":"Measurement of heat release rate using regression of chemiluminescence signals in premixed ammonia-methane/hydrogen-air flames","authors":"Xuren Zhu ,&nbsp;Daniel Vigarinho de Campos ,&nbsp;Yujing Wei ,&nbsp;Thibault F. Guiberti","doi":"10.1016/j.jaecs.2025.100434","DOIUrl":"10.1016/j.jaecs.2025.100434","url":null,"abstract":"<div><div>This paper proposes a non-intrusive method for quantitatively measuring heat release rate (HRR) through regression of chemiluminescence signals. The method is demonstrated using premixed ammonia-methane-air and ammonia-hydrogen-air counterflow twin laminar flames, with chemiluminescence data previously collected across a wide range of ammonia fractions, equivalence ratios, and strain rates. Opposed-flow 1-D simulations are employed to determine the true HRR of these twin flames, providing the complete dataset necessary to train an algorithm that correlates HRR with chemiluminescence intensities via Gaussian Process Regression (GPR). The accuracy of HRR predictions from the trained algorithms is evaluated using various input combinations, from including all recorded excited species (such as NO*, OH*, NH*, CN*, CO₂*, and CH* for ammonia-methane-air flames, and NO*, OH*, NH* along with <em>violet, blue, green, yellow, orange</em>, and <em>red</em> colors for ammonia-hydrogen-air flames) to only one pair of species. Results indicate that UV–visible chemiluminescence alone can reliably infer HRR with high accuracy, better than 6.7 % for ammonia-methane and 7.5 % for ammonia-hydrogen flames, when all species are considered in the GPR, even if ammonia fraction, equivalence ratio, and strain rate are unknown. Further data show that chemiluminescence intensities from just two excited species, specifically OH*-CN* for ammonia-methane-air and OH*-NH* for ammonia-hydrogen-air, can provide average accuracies of 2.8 % and 4.7 %, respectively. The method is also applied to ammonia-hydrogen-nitrogen-air swirling turbulent flames, which better represent practical scenarios, using a new dataset of NH*, OH*, and NO* chemiluminescence intensities. It is demonstrated that, with proper data processing, this approach can accurately predict HRR for these more complex swirling turbulent flames.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100434"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737619","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":"Hydrogen production from pyrolysis of biomass components","authors":"Guanyi Chen ,&nbsp;Wanhua Du ,&nbsp;Jingliang Cai ,&nbsp;Jian Li ,&nbsp;Junyu Tao ,&nbsp;Muhammad Irfan Rajput ,&nbsp;Beibei Yan ,&nbsp;Zhi Wang","doi":"10.1016/j.jaecs.2025.100381","DOIUrl":"10.1016/j.jaecs.2025.100381","url":null,"abstract":"<div><div>Hydrogen energy is key for the global green energy transition, and biomass thermochemical has become an important option for green hydrogen production due to its carbon neutrality advantage. Pyrolysis is the initial step of thermochemical technologies. A systematic analysis of the mechanism of H₂ production from biomass pyrolysis is significant for the subsequent optimal design of efficient biomass thermochemical H₂ production technologies. Biomass is mainly composed of cellulose, hemicellulose, and lignin, and differences in their physicochemical properties and structures directly affect the pyrolysis hydrogen production process. In this study, thermogravimetry-mass spectrometry-Fourier transform infrared spectroscopy (TG-MS-FTIR) was employed and fixed-bed pyrolysis experiments were conducted to systematically investigate the pyrolysis of biomass component with focusing on hydrogen production. According to the results of TG-MS-FTIR experiments, hemicellulose produced hydrogen through the breaking of C<img>H bonds in short chains and acetyl groups, as well as secondary cracking of volatiles and condensation of aromatic rings at high temperatures. Cellulose produced hydrogen through the breaking of C<img>H bonds in volatiles generated from sugar ring cleavage, along with char gasification and condensation of aromatic rings at high temperatures. Lignin produced hydrogen through ether bond cleavage, breaking of methoxy groups, as well as cleavage of phenylpropane side chains and condensation of aromatic rings at high temperatures. Results from fixed-bed pyrolysis experiments further showed that hemicellulose exhibited the strongest hydrogen production capacity, with the maximum H₂ production efficiency of 6.09 mmol/g, the maximum H₂ selectivity of 17.79 %, and the maximum H₂ effectiveness of 59 % at 800 °C.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"24 ","pages":"Article 100381"},"PeriodicalIF":5.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010761","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}