材料科学
电解质
电化学
陶瓷
电解
电极
质子
电导率
纳米技术
化学工程
复合材料
物理化学
化学
物理
量子力学
工程类
作者
Ning Wang,Baoyin Yuan,Chunmei Tang,Lei Du,Ruijie Zhu,Yoshitaka Aoki,Weibo Wang,Lixin Xing,Siyu Ye
标识
DOI:10.1002/adma.202203446
摘要
Currently, the development of high-performance protonic ceramic cells (PCCs) is limited by the scarcity of efficient mixed protonic-electronic conducting oxides that can act as air electrodes to satisfy the high protonic conductivity of electrolytes. Despite the extensive research efforts in the past decades, the development of mixed protonic-electronic conducting oxides still remains in a trial-and-error process, which is extremely time consuming and high cost. Herein, based on the data acquired from the published literature, the machine-learning (ML) method is introduced to accelerate the discovery of efficient mixed protonic-electronic conducting oxides. Accordingly, the hydrated proton concentration (HPC) of 3200 oxides is predicted to evaluate the proton conduction that is essential for enhancing the electrochemical performances of PCCs. Subsequently, feature importance for HPC is evaluated to establish a guideline for rapid and accurate design and development of high-efficiency mixed protonic-electronic conducting oxides. Thereafter, screened (La0.7 Ca0.3 )(Co0.8 Ni0.2 )O3 (LCCN7382) is prepared, and the experimental HPC adequately corresponds with the predicted results. Moreover, the PCC with LCCN7382 exhibits satisfactory electrochemical performances in electrolysis and fuel cell modes. In addition to the development of a promising air electrode for PCC, this study establishes a new avenue for ML-based development of mixed protonic-electronic conducting oxides.
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