Insights on proton‐conducting ceramic electrochemical cell fabrication

制作 陶瓷 材料科学 电化学 质子 纳米技术 电化学电池 复合材料 化学 电极 物理化学 物理 量子力学 医学 病理 替代医学
作者
Charlie Meisel,Jake Huang,You‐Dong Kim,Sophia Stockburger,Ryan O’Hayre,Neal P. Sullivan
出处
期刊:Journal of the American Ceramic Society [Wiley]
卷期号:108 (4) 被引量:6
标识
DOI:10.1111/jace.20321
摘要

Abstract This study investigates the key factors influencing sintering behavior and grain growth in (BCZYYb4411)–NiO negatrodes and BCZYYb electrolytes for protonic ceramic electrochemical cells (PCECs). Elastic net machine learning models are applied to a dataset of nearly 200 individual PCEC button cells fabricated over the course of more than 3 years to identify the key processing parameters that significantly affect negatrode shrinkage and electrolyte grain growth. The shrinkage rate of the BCZYYb4411–NiO negatrode is primarily governed by the solid‐state sintering behavior. Higher sintering temperatures, longer dwell times, and smaller NiO particle size are the primary determinants that lead to greater shrinkage. New or lightly‐used setters and more compact negatrodes are also found to increase shrinkage. Electrolyte grain growth is chiefly controlled by the liquid‐phase sintering of the BCZYYb phase. Increased cerium content on the B‐site leads to the largest enhancement in grain size, followed by increasing maximum sintering temperature. We find that the parameters used to tune the spray deposition of the electrolyte layer are also critical, with wetter and more uniform sprays promoting grain enlargement. Finally, we find that the sintering environment (e.g. presence/absence of sintering neighbors or sacrificial powders and the ambient humidity level) also substantially impacts both shrinkage and grain growth. This work comprehensively analyzes data from nearly 200 PCECs without “success bias,” meaning that poor performers and fabrication failures were included in the analysis. By doing so, the study provides valuable insight into the critical factors controlling shrinkage and grain growth in BCZYYb‐based PCECs. The findings offer foundational guidance for processing optimization that could lead to better repeatability, increased yields, and higher performance.
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