Strategy for predicting catalytic activity of catalysts with hierarchical nanostructures

IF 2.9 3区 化学 Q3 CHEMISTRY, PHYSICAL
Zidi Zhu, Daoming Huan, Jingchao Yuan, Dan Zhang, Aijun Li, Jiujun Zhang
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引用次数: 0

Abstract

Three-dimensional hierarchical nanostructures have been employed as electrodes of solid oxide fuel cells (SOFCs) to notably improve the catalytic performance. Hierarchical nanoscale porous electrodes face a trade-off: macroscale pores enhance mass transfer but reduce the number of active sites, while microscale pores increase the number of active sites at the cost of higher transport resistance. Careful design of these structures is crucial for balancing mass transfer and reaction dynamics. A three-dimensional multiphysics model is developed in this paper to examine the influence of different hierarchical geometrical nanostructures on catalytic performance. Additionally, the effects of different diffusion coefficients are also investigated in this study to present the changes in catalytic activity in diffusion, mixed, and reaction-controlled regimes. The model shows good alignment with the experimentally obtained data. An improved Thiele modulus is formulated to quantitatively evaluate the efficiencies of complex hierarchical nanostructures by considering the detailed characteristics of the main and secondary structures.

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来源期刊
Physical Chemistry Chemical Physics
Physical Chemistry Chemical Physics 化学-物理:原子、分子和化学物理
CiteScore
5.50
自引率
9.10%
发文量
2675
审稿时长
2.0 months
期刊介绍: Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.
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