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Prediction of impedance responses of protonic ceramic cells using artificial neural network tuned with the distribution of relaxation times

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Journal of Energy Chemistry 2023年第3期78卷 582-588,I0016页

作者： Xuhao Liu Zilin Yan Junwei Wu Jake Huang Yifeng Zheng Neal PSullivan Ryan O'Hayre Zheng Zhong Zehua Pan School of Science Harbin Institute of TechnologyShenzhen 518055GuangdongChina School of Materials Engineering Harbin Institute of TechnologyShenzhen 518055GuangdongChina Department of Metallurgical and Materials Engineering Colorado School of Mines1500 Illinois St.Golden 80401ColoradoUSA College of Materials Science and Engineering Nanjing Tech UniversityNo.30 Puzhu Road(S)Nanjing 211816JiangsuChina Department of Mechanical Engineering Colorado School of Mines1500 Illinois St.Golden 80401ColoradoUSA

A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating *** impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the *** artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS ***,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted *** show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~*** can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem *** adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to ***,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems.

关键词： Protonic ceramic fuel cell/electrolysis cell Electrochemical impedance spectroscopy distribution of relaxation times Artiﬁcial neural network

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自组装FeRu双金属纳米催化剂在可逆固体氧化物电池中实现高效稳定的CO-CO_(2)相互转化

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Science China Materials 2024年第5期67卷 1471-1480页

作者：王晶张栋刘通徐安琪罗瑶胡学雷王瑶 Key Laboratory of Green Chemical Process of Ministry of Education Hubei Key Laboratory of Novel Reactor and Green Chemical TechnologySchool of Chemical Engineering and PharmacyWuhan Institute of TechnologyWuhan430205China School of Power and Mechanical Engineering Wuhan UniversityWuhan430072China

本文报道了一种适应于高效稳定的CO-CO_(2)相互转化的可逆固体氧化物电池(RSOC)相变燃料电极.该燃料电极由FeRu双金属纳米催化剂和Ruddlesden-Popper相Pr_(0.8)Sr_(1.2)Fe_(1-x-y)Ru_(x)Mo_(y)O_(4)氧化物复合而成(FeRu@PSFRM).固体氧化物燃料电池(SOFC)模式时,单电池800℃时的最大输出功率密度可以达到170 W cm^(-2);而在固体氧化物电解池(SOEC)模式下,800℃、1.3 V时电解池的电解电流密度达到-0.256 A cm^(-2).在SOFC-SOEC循环测试过程中,RSOC中CO-CO_(2)相互转化过程经历了“活化-稳定-衰退”三个明显阶段.幸运的是,性能衰退的燃料电极可通过“原位氧化-还原”处理实现性能再生,有效提升该电池的使用寿命.研究结果表明,原位脱溶形成的FeRu@PSFRM材料是一种极具应用潜力的燃料电极候选材料,以期实现高效稳定的CO-CO_(2)相互转化.

关键词： Ruddlesden-Popper oxide CO-CO_(2)conversion cycling test perovskite oxide reversible solid oxide cells distribution of relaxation times electrochemical impedance spectra fuel electrode

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Enhancing cathode performance for CO2 electrolysis with Ce0.9M0.1O2-δ（M=Fe, Co, Ni） catalysts in solid oxide electrolysis cell

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Journal of Energy Chemistry 2020年第1期29卷 46-51,I0003页

作者： Zhidong Huang Zhe Zhao Huiying Qi Xiuling Wang Baofeng Tu Mojie Cheng Division of Fuel Cells Dalian National Laboratory for Clean EnergyDalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023LiaoningChina University of Chinese Academy of Sciences Beijing 100049China

Electrochemical conversion with solid oxide electrolysis cells is a promising technology for CO2 utilization and simultaneously store renewable *** this work,Ce0.9M0.1O2-δ(CeM,M=Fe,Co,Ni)catalysts are infiltrated into La0.6Sr0.4Cr0.5Fe0.5O3-δ-Gd0.2Ce0.8O2-δ(LSCr Fe-GDC)cathode to enhance the electrochemical performance for CO2 ***-LSCrFe-GDC cell obtains the best performance with a current density of 0.652 A cm^-2,followed by CeFe-LSCrFe-GDC and CeNi-LSCrFe-GDC cells with the value of 0.603 and 0.535 A cm^-2,respectively,about 2.44,2.26 and 2.01 times higher than that of the LSCrFe-GDC cell at1.5 V and 800℃.Electrochemical impedance spectra combined with distributions of relaxed times analysis shows that both CO2 adsorption process and the dissociation of CO2 at triple phase boundaries are accelerated by Ce M catalysts,while the latter is the key rate-determining step.

关键词： Solid oxide electrolysis cell Carbon dioxide conversion Doped ceria distribution of relaxation times Electroreduction

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