Protonic ceramic fuel cells(PCFCs)are more suitable for operation at low temperatures due to their smaller activation energy(Ea).Unfortunately,the utilization of PCFC technology at reduced temperatures is limited by t...Protonic ceramic fuel cells(PCFCs)are more suitable for operation at low temperatures due to their smaller activation energy(Ea).Unfortunately,the utilization of PCFC technology at reduced temperatures is limited by the lack of durable and high-activity air electrodes.A lot number of cobalt-based oxides have been developed as air electrodes for PCFCs,due to their high oxygen reduction reaction(ORR)activity.However,cobalt-based oxides usually have more significant thermal expansion coefficients(TECs)and poor thermomechanical compatibility with electrolytes.These characteristics can lead to cell delamination and degradation.Herein,we rationally design a novel cobalt-containing composite cathode material with the nominal composition of Sr_(4)Fe_(4)Co_(2)O_(13)+δ(SFC).SFC is composed of tetragonal perovskite phase(Sr_(8)Fe_(8)O_(23)+δ,I4/mmm,81 wt.%)and spinel phase(Co_(3)O_(4),Fd3m,19 wt.%).The SFC composite cathode displays an ultra-high oxygen ionic conductivity(0.053 S·cm^(-1)at 550℃),superior CO_(2)tolerance,and suitable TEC value(17.01×10^(-6)K^(-1)).SFC has both the O_(2)^(-)/e^(-)conduction function,and the triple conducting(H^(+)/O_(2)^(-)/e^(-))capability was achieved by introducing the protonic conduction phase(BaZr_(0.2)Ce_(0.7)Y_(0.1)O_(3-δ),BZCY)to form SFC+BZCY(70 wt.%:30 wt.%).The SFC+BZCY composite electrode exhibits superior ORR activity at a reduced temperature with extremely low area-specific resistance(ASR,0.677Ω·cm^(2)at 550℃),profound peak power density(PPD,535 mW·cm^(-2)and 1.065 V at 550℃),extraordinarily long-term durability(>500 h for symmetrical cell and 350 h for single cell).Moreover,the composite has an ultra-low TEC value(15.96×10^(-6)K^(-1)).This study proves that SFC+BZCY with triple conducting capacity is an excellent cathode for low-temperature PCFCs.展开更多
This paper describes our approach for the Chinese clinical named entity recognition(CNER) task organized by the 2020 China Conference on Knowledge Graph and Semantic Computing(CCKS) competition. In this task, we need ...This paper describes our approach for the Chinese clinical named entity recognition(CNER) task organized by the 2020 China Conference on Knowledge Graph and Semantic Computing(CCKS) competition. In this task, we need to identify the entity boundary and category labels of six entities from Chinese electronic medical record(EMR). We constructed a hybrid system composed of a semi-supervised noisy label learning model based on adversarial training and a rule post-processing module. The core idea of the hybrid system is to reduce the impact of data noise by optimizing the model results. Besides, we used post-processing rules to correct three cases of redundant labeling, missing labeling, and wrong labeling in the model prediction results. Our method proposed in this paper achieved strict criteria of 0.9156 and relax criteria of 0.9660 on the final test set, ranking first.展开更多
基金This research was financially supported by the National Natural Science Foundation of China(No.22179054)the National Natural Science Foundation of China(No.22101150)+1 种基金Natural Science Foundation of Jiangsu Province,China(No.BK20190965)Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.18KJB470011).
文摘Protonic ceramic fuel cells(PCFCs)are more suitable for operation at low temperatures due to their smaller activation energy(Ea).Unfortunately,the utilization of PCFC technology at reduced temperatures is limited by the lack of durable and high-activity air electrodes.A lot number of cobalt-based oxides have been developed as air electrodes for PCFCs,due to their high oxygen reduction reaction(ORR)activity.However,cobalt-based oxides usually have more significant thermal expansion coefficients(TECs)and poor thermomechanical compatibility with electrolytes.These characteristics can lead to cell delamination and degradation.Herein,we rationally design a novel cobalt-containing composite cathode material with the nominal composition of Sr_(4)Fe_(4)Co_(2)O_(13)+δ(SFC).SFC is composed of tetragonal perovskite phase(Sr_(8)Fe_(8)O_(23)+δ,I4/mmm,81 wt.%)and spinel phase(Co_(3)O_(4),Fd3m,19 wt.%).The SFC composite cathode displays an ultra-high oxygen ionic conductivity(0.053 S·cm^(-1)at 550℃),superior CO_(2)tolerance,and suitable TEC value(17.01×10^(-6)K^(-1)).SFC has both the O_(2)^(-)/e^(-)conduction function,and the triple conducting(H^(+)/O_(2)^(-)/e^(-))capability was achieved by introducing the protonic conduction phase(BaZr_(0.2)Ce_(0.7)Y_(0.1)O_(3-δ),BZCY)to form SFC+BZCY(70 wt.%:30 wt.%).The SFC+BZCY composite electrode exhibits superior ORR activity at a reduced temperature with extremely low area-specific resistance(ASR,0.677Ω·cm^(2)at 550℃),profound peak power density(PPD,535 mW·cm^(-2)and 1.065 V at 550℃),extraordinarily long-term durability(>500 h for symmetrical cell and 350 h for single cell).Moreover,the composite has an ultra-low TEC value(15.96×10^(-6)K^(-1)).This study proves that SFC+BZCY with triple conducting capacity is an excellent cathode for low-temperature PCFCs.
基金This work is supported by the National Key R&D Program of China(2020AAA0106400)the National Natural Science Foundation of China(No.61831022,No.61806201)+1 种基金the Key Research Program of the Chinese Academy of Sciences(Grant No.ZDBS-SSW-JSC006)This work is also supported by Beijing Academy of Artificial Intelligence(BAAI).
文摘This paper describes our approach for the Chinese clinical named entity recognition(CNER) task organized by the 2020 China Conference on Knowledge Graph and Semantic Computing(CCKS) competition. In this task, we need to identify the entity boundary and category labels of six entities from Chinese electronic medical record(EMR). We constructed a hybrid system composed of a semi-supervised noisy label learning model based on adversarial training and a rule post-processing module. The core idea of the hybrid system is to reduce the impact of data noise by optimizing the model results. Besides, we used post-processing rules to correct three cases of redundant labeling, missing labeling, and wrong labeling in the model prediction results. Our method proposed in this paper achieved strict criteria of 0.9156 and relax criteria of 0.9660 on the final test set, ranking first.
