High-throughput oxygen chemical potential engineering of perovskite oxides for chemical looping applications. Issue 4 (24th February 2022)

• Record Type: Journal Article
• Authors: Wang, Xijun; Gao, Yunfei; Krzystowczyk, Emily; Iftikhar, Sherafghan; Dou, Jian; Cai, Runxia; Wang, Haiying; Ruan, Chongyan; Ye, Sheng; Li, Fanxing
• Is Part Of: Energy & environmental science. Volume 15:Issue 4(2022)
• Page Start: 1512
• Page End: 1528
• Publication Date: 2022-02-24
• Subjects: Energy conversion -- Periodicals; Fuel switching -- Periodicals; Environmental sciences -- Periodicals; Environmental chemistry -- Periodicals; 333.79
• DOI: 10.1039/d1ee02889h
• Languages: English
• ISSNs: 1754-5692

Abstract

Abstract : Integrating DFT, machine learning and experimental verifications, a high-throughput screening scheme is performed to rationally engineer the redox properties of SrFeO3− δ based perovskites for chemical looping applications. Abstract : Chemical looping (CL) represents a versatile, emerging strategy for sustainable chemical and energy conversion. Designing metal oxide oxygen carriers with suitable redox properties remains one of the most critical challenges to CL due to the considerably different thermodynamic property requirements for different applications. Taking SrFeO3− δ as a base-structure, this study seeks to rationally substitute its A- and/or B-site cations to tailor the equilibrium oxygen partial pressure over 20 orders of magnitude. 2401 Sr x A1− x Fe y B1− y O3− δ perovskite-phase structures were investigated using high-throughput density functional theory (DFT) and 227, 273 high-entropy perovskites were screened via machine learning (ML). This significantly expands the materials design space. While most of the compositions predicted are new and nonobvious, 19 previously reported oxygen carriers, with excellent redox properties, were correctly identified by the algorithm. Moreover, we experimentally demonstrated 15 new oxygen carriers with superior redox performance. These results support the effectiveness of the high-throughput approaches for accelerated materials discovery.