Mathematical modeling and experiment verification for the Solid oxide Fuel Cell Mn1.5Co1.5O4 interconnect coating

This study presents a new mathematical modeling study for interconnect oxidation analysis. A protective coating Mn1.5Co1.5O4 with different thicknessessare applied on ferritic stainless steel interconnect to address the chromium poisoning issue. Different Mn1.5Co1.5O4 coating thicknesses are applied...

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Bibliographic Details
Published in:Materials Letters
Main Author: Kang Huai T.; Lowrance Y.; Farhana Abd Rahman N.; Asyikin Yusop U.; Abd Rahman H.; Jaidi Z.; Faizal Tukimon M.; Saifulddin Mohd Azami M.
Format: Article
Language:English
Published: Elsevier B.V. 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181752772&doi=10.1016%2fj.matlet.2023.135825&partnerID=40&md5=92181538403eecf86bf4f170b80d1ab2
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Summary:This study presents a new mathematical modeling study for interconnect oxidation analysis. A protective coating Mn1.5Co1.5O4 with different thicknessessare applied on ferritic stainless steel interconnect to address the chromium poisoning issue. Different Mn1.5Co1.5O4 coating thicknesses are applied. The coatings are assessed by surface morphology and phase structure. Developing coatings with various thicknesses is successful while maintaining consistent crystalline phases and coating morphology. The average coating thickness achieved at 35v for deposition times of 20, 30, and 40 s are 53.38 μm, 68.13 μm, and 85.13 μm, respectively. The coating thickness, 68.13 μm yields the lowest area specific resistance of 0.0469 Ω cm2 after 400 h of oxidation at 800 °C, compared to 0.0532 Ω cm2 and 0.0477 Ω cm2 for 53.38 μm and 85.13 μm The weight gains of the coated samples are recorded mathematically.This modeling approach results in a low weight gain and low oxidation kinetic rate of 0.139 mg cm−2 and 21.55 × 10−15 g2 cm−4 s−1, respectively for 68.13 μm coating thickness It predicts an area-specific resistance of 0.0451 Ωcm2 for the 35v 30 s coating, which is close to 0.0469 Ωcm2 experimental data. © 2023 Elsevier B.V.
ISSN:0167577X
DOI:10.1016/j.matlet.2023.135825