Effect of processing route on the properties of LSCF-based composite cathode for IT-SOFC
Source abstract
Abstract
A novel processing technique was developed to produce an in-situ nano-composite powder based on $$\hbox {La}0.6\hbox {Sr}0.4\hbox {Co}0.2 \hbox {Fe}0.8\hbox {O}_{3\text {-}{\updelta }}$$ (LSCF6428) and $$\hbox {Gd}0.1\hbox {Ce}0.9\hbox {O}1.95$$ (GDC10) for application as cathode material in intermediate temperature solid oxide fuel cells (IT-SOFC). The nano-composite powder was produced using glycine-nitrate solution combustion technique starting from nitrates of six metal ions. The synthesized powder was characterized using X-ray diffraction (XRD), transmission electron microscopy (TEM), particle size and BET surface area analyses. XRD analysis of as-produced nano-composite powder confirmed the formation of desired phases right after combustion synthesis. The structural parameters of different phases present in the powders were estimated through Rietveld refinement of XRD data. To compare the electrical properties of nano-composite cathode powder produced through the present method, nano-powders of GDC10 and LSCF6428 were individually produced through glycine nitrate process and subsequently mixed through solid-state technique and characterized for functional properties. Using this in-situ nano-composite material, lower polarization resistance was achieved as compared to the LSCF–GDC composite produced from mechanical mixtures of nano-powders of GDC10 and LSCF6428 when used as cathode in GDC10 electrolyte-based symmetrical cell. The effects of cathode layer thickness and electrode firing temperature on the cathodic polarization resistance were studied using in-situ nano-composite cathode powder.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advancements in Solid Oxide Fuel Cells · Electronic and Structural Properties of Oxides · Magnetic and transport properties of perovskites and related materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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