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Discovery of new materials plays a critical role in developing advanced high-temperature thermoelectric (TE) applications. Transition metal oxides (TMOs) are one of the attractive candidates for hightemperature TE applications due to their thermal and chemical stability. However, the trade-off relationship between thermopower (S) and electrical conductivity (s) limits the maximum attainable power factor (PF), thereby hindering improvements in TE conversion efficiency. To overcome this tradeoff relationship, the emerging approach of the redox-driven metal exsolution in TMOs shows promise in improving both S and s. However, the effect of metal exsolution with different particle sizes and densities on S and s is still largely unexplored. This study demonstrates an unusually large enhancement in PF through the exsolution of Ni nanoparticles in epitaxial La0.7Ca0.2Ni0.25Ti0.75O3 (LCNTO) thin films. Metal exsolution leads to a decrease in the carrier concentration while increasing the carrier mobility due to energy filtering effects. In addition, the exsolved metal particles introduce high-mobility electron carriers into the low-mobility LCNTO matrix. Consequently, the exsolution of metal particles results in a significant enhancement in S along with a substantial increase in s, compared to the pristine film. Overall, the TE power factor of LCNTO is dramatically enhanced by up to 8 orders of magnitude owing to the presence of exsolved metal particles. This enhancement is attributed to the selective filtering of carriers caused by energy band bending at the metal–oxide interfaces and the high-mobility carriers from the exsolved Ni particles with a high Ni0 fraction. This study unequivocally demonstrates the impact of metal exsolution on oxide TE properties and provides a novel route to tailor the interconnected physical and chemical properties of oxides, leading to enhanced TE power output. 

Abstract A liquid–gas foam, here called bubble array, is a ubiquitous phenomenon widely observed in daily lives, food, pharmaceutical and cosmetic products, and even bio- and nano-technologies. This intriguing phenomenon has been often studied in a well-controlled environment in laboratories, computations, or analytical models. Still, real-world bubble undergoes complex nonlinear transitions from wet to dry conditions, which are hard to describe by unified rules as a whole. Here, we show that a few early-phase snapshots of bubble array can be learned by a glass-box physics rule learner (GPRL) leading to prediction rules of future bubble array. Unlike the black-box machine learning approach, the glass-box approach seeks to unravel expressive rules of the phenomenon that can evolve. Without known principles, GPRL identifies plausible rules of bubble prediction with an elongated bubble array data that transitions from wet to dry states. Then, the best-so-far GPRL-identified rule is applied to an independent circular bubble array, demonstrating the potential generality of the rule. We explain how GPRL uses the spatio-temporal convolved information of early bubbles to mimic the scientist’s perception of bubble sides, shapes, and inter-bubble influences. This research will help combine foam physics and machine learning to better understand and control bubbles.