Enhancing layered perovskite ferrites with ultra-high-density nanoparticles via cobalt doping for ceramic fuel cell anode

Nanoparticles anchored on the perovskite surface have gained considerable attention for their wide-ranging applications in heterogeneous catalysis and energy conversion due to their robust and integrated structural configuration.Herein,we employ controlled Co doping to effectively enhance the nanoparticle exsolution process in layered perovskite ferrites materials.CoFe alloy nanoparticles with ultra-high-density are exsolved on the(PrBa)_(0.95)(Fe_(0.8)Co_(0.1)Nb_(0.1))2O_(5+δ)(PBFCN_(0.1))surface under reducing atmosphere,providing significant amounts of reaction sites and good durability for hydrocarbon catalysis.Under a reducing atmosphere,cobalt facilitates the reduction of iron cations within PBFCN_(0.1),leading to the formation of CoFe alloy nanoparticles.This formation is accompanied by a cation exchange process,wherein,with the increase in temperature,partial cobalt ions are substituted by iron.Meanwhile,Co doping significantly enhance the electrical conductivity due to the stronger covalency of the Cosingle bondO bond compared with Fesingle bondO bond.A single cell with the configuration of PBFCN_(0.1)-Sm_(0.2)Ce_(0.8)O_(1.9)(SDC)|SDC|Ba_(0.5)Sr_(0.5)Co_(0.8)Fe_(0.2)O_(3−δ)(BSCF)-SDC achieves an extremely low polarization resistance of 0.0163Ωcm^(2)and a high peak power density of 740 mW cm^(−2)at 800℃.The cell also shows stable operation for 120 h in H_(2)with a constant current density of 285 mA cm^(−2).Furthermore,employing wet C_(2)H_(6)as fuel,the cell demonstrates remarkable performance,achieving peak power densities of 455 mW cm^(−2)at 800℃and 320 mW cm^(−2)at 750℃,marking improvements of 36%and 70%over the cell with(PrBa)_(0.95)(Fe_(0.9)Nb_(0.1))_(2)O_(5+δ)(PBFN)-SDC at these respective temperatures.This discovery emphasizes how temperature influences alloy nanoparticles exsolution within doped layered perovskite ferrites materials,paving the way for the development of high-performance ceramic fuel cell anodes.

A panel data model to predict airline passenger volume

Airline passenger volume is an important reference for the implementation of aviation capacity and route adjustment plans.This paper explores the determinants of airline passenger volume and proposes a comprehensive panel data model for predicting volume.First,potential factors influencing airline passenger volume are analyzed from Geo-economic and service-related aspects.Second,the principal component analysis(PCA)is applied to identify key factors that impact the airline passenger volume of city pairs.Then the panel data model is estimated using 120 sets of data,which are a collection of observations for multiple subjects at multiple instances.Finally,the airline data from Chongqing to Shanghai,from 2003 to 2012,was used as a test case to verify the validity of the prediction model.Results show that railway and highway transportation assumed a certain proportion of passenger volumes,and total retail sales of consumer goods in the departure and arrival cities are significantly associated with airline passenger volume.According to the validity test results,the prediction accuracies of the model for 10 sets of data are all greater than 90%.The model performs better than a multivariate regression model,thus assisting airport operators decide which routes to adjust and which new routes to introduce.