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Juan Shi

Publications and source records attributed to Juan Shi.

2 recordsLinked to original sources

A brief review of high-entropy oxides in solid oxide fuel cell applications

Solid oxide fuel cells are efficient energy conversion devices essential to clean energy development, yet their broad application is limited by material challenges, including sluggish oxygen reduction kinetics at intermediate temperatures, electrode instability and vulnerability to contaminants. High-entropy oxides, a novel class of materials characterized by multiple principal elements and high configurational entropy, present a promising approach to overcome these issues via their distinctive "four core effects". This review begins with the fundamentals of high-entropy materials, covering their definition, phase stabilization mechanisms, and relevant descriptors, then systematically reviews their progress as SOFC cathodes, electrolytes, and anodes. Key advances are summarized, and current challenges are analyzed, offering guidance for the design of high-performance and stable high-entropy oxides for solid oxide fuel cells.

cond-mat.mtrl-sci

Basis Choices for Frequency Domain Statistical Independence Tests and Algorithms for Algebraic Relation Extraction

In this paper, we explore how different selections of basis functions impact the efficacy of frequency domain techniques in statistical independence tests, and study different algorithms for extracting low-dimensional algebraic relations from dependent data. We examine a range of complete orthonormal bases functions including the Legendre polynomials, Fourier series, Walsh functions, and standard and nonstandard Haar wavelet bases. We utilize fast transformation algorithms to efficiently transform physical domain data to frequency domain coefficients. The main focuses of this paper are the effectiveness of different basis selections in detecting data dependency using frequency domain data, e.g., whether varying basis choices significantly influence statistical power loss for small data with large noise; and on the stability of different optimization formulations for finding proper algebraic relations when data are dependent. We present numerical results to demonstrate the effectiveness of frequency domain-based statistical analysis methods and provide guidance for selecting the proper basis and algorithm to detect a particular type of relations.

math.NA