Novel LaFe_(2)O_(4)spinel structure with a large oxygen reduction response towards protonic ceramic fuel cell cathode

Highly active and stable electrocatalysts are mandatory for developing high-performance and longlasting fuel cells.The current study demonstrates a high oxygen reduction reaction(ORR)electrocatalytic activity of a novel spinel-structured LaFe_(2)O_(4)via a self-doping strategy.The LaFe_(2)O_(4)demonstrates excellent ORR activity in a protonic ceramic fuel cell(PCFC)at temperature range of 350-500℃.The high ORR activity of LaFe_(2)O_(4)is mainly attributed to the facile release of oxide and proton ions,and improved synergistic incorporation abilities associated with interplay of multivalent Fe^(3+)/Fe^(2+)and La^(3+)ions.Using LaFe_(2)O_(4)as cathode over proton conducting BaZr_(0.4)Ce_(0.4)Y_(0.2)O_(3)(BZCY)electrolyte,the fuel cell has delivered a high-power density of 806 mW/cm^(2)operating at 500℃.Different spectroscopic and calculations methods such as UV-visible,Raman,X-ray photoelectron spectroscopy and density functional theory(DFT)calculations were performed to screen the potential application of LaFe_(2)O_(4)as cathode.This study would help in developing functional cobalt-free ORR electrocatalysts for low temperature-PCFCs(LT-PCFCs)and solid oxide fuel cells(SOFCs)applications.

Comparative analysis of ceramic-carbonate nanocomposite fuel cells using composite GDC/NLC electrolyte with different perovskite structured cathode materials

A comparative analysis of perovskite structured cathode materials, La0.65Sr0.35Mn03 （LSM）, La0.8Sr0.2CoO3 （LSC）, La0.6Sr0.4FeO3 （LSF） and La0.6Sr0.4Co0.2Fe0.803 （LSCF）, was performed for a ceramic-carbonate nanocomposite fuel cell using composite electrolyte consisting of Gd0.1Ce0.9O1.95 （GDC） and a eutectic mixture of Na2CO3 and Li2CO3. The compatibility of these nanocomposite electrode powder materials was investigated under air, CO2 and air/CO2 atmospheres at 550 ℃. Microscopy measurements together with energy dispersive X-ray spectroscopy （EDS） elementary analysis revealed few spots with higher counts of manganese relative to lanthanum and strontium under pure CO2 atmosphere. Furthermore, electrochemical impedance （EIS） analysis showed that LSC had the lowest resistance to oxygen reduction reaction （ORR） （14.12 Ω·cm^2） followed by LSF （15.23 Ω·cm^2）, LSCF （19.38Ω·cm^2） and LSM （ 〉 300 Ω·cm^2）. In addition, low frequency EIS measurements （down to 50 gHz） revealed two additional semi-circles at frequencies around 1 Hz. These semicircles can yield additional information about electrochemical reactions in the device. Finally, a fuel cell was fabricated using GDC/NLC nanocomposite electrolyte and its composite with NiO and LSCF as anode and cathode, respectively. The cell produced an excellent power density of 1.06 W/cm^2 at 550 ℃ under fuel cell conditions.

TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

In this study,we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea.A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis.Backscatter intensity,interferometric coherence magnitude,and interferometric phase have been used as informative features in several classification experiments.Various combinations of classification features were evaluated using Maximum likelihood(ML),Random Forests(RF)and Support Vector Machine(SVM)classifiers to achieve the best possible discrimination between open water and several sea ice types(undeformed ice,ridged ice,moderately deformed ice,brash ice,thick level ice,and new ice).Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification per-formance compared to using only backscatter-intensity.The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies,however,at the expense of somewhat longer processing time.The best overall accuracy(OA)for three methodologies were achieved using combination of all tested features were 71.56,72.93,and 72.91%for ML,RF and SVM classifiers,respectively.Compared to OAs of 62.28,66.51,and 63.05%using only backscatter intensity,this indicates strong benefit of SAR interferometry in discriminating different types of sea ice.In contrast to several earlier studies,we were particularly able to successfully discriminate open water and new ice classes.