Metal-organic framework-derive d carbon-base d composites for electromagnetic wave absorption:Dimension design and morphology regulation

Developing highly efficient microwave absorbing materials(MAMs)to ameliorate the electromagnetic(EM)response and facilitate energy absorption is crucial in both the civil and military industries.Metal-organic framework(MOF)derived nanoporous carbon composites have emerged as advanced MAMs ow-ing to their rich porosity,tunable compositions,facile functionalization,and morphology diversity.To-gether with the flourishing development of composition-tuning strategy,the rational dimension design and elaborate control over the architectures have also evolved into an effective approach to regulating their EM properties.Herein,we provide a comprehensive review of the recent advances in using di-mension and morphology modulation to adjust the microwave attenuation capacities for MOF-derived carbon composites.The underlying design rules and unique advantages for the MAMs of various dimen-sions were discussed with the selection of representative work,providing general concepts and insight on how to efficiently tune the morphologies.Accordingly,the fundamental dimension-morphology-function relationship was also elucidated.Finally,the challenges and perspectives of dimension design and mor-phology control over MOF-derived MAMs were also presented.

Unveiling the surface-interface properties of perovskite crystals and pivotal regulation strategies

Metal-halide perovskite solar cells have garnered significant research attention in the last decade due to their exceptional photovoltaic performance and potential for commercialization.Despite achieving remarkable power conversion efficiency of up to 26.1%,a substantial discrepancy persists when compared to the theoretical Shockley-Queisser(SQ)limit.One of the most serious challenges facing perovskite solar cells is the energy loss incurred during photovoltaic conversion,which affects the SQ limits and stability of the device.More significant than the energy loss occurring in the bulk phase of the perovskite is the energy loss occurring at the surface-interface.Here,we provide a systematic overview of the physical and chemical properties of the surface-interface.Firstly,we delve into the underlying mechanism causing the energy deficit and structural degradation at the surface-interface,aiming to enhance the understanding of carrier transport processes and structural chemical reactivity.Furthermore,we systematically summarized the primary modulating pathways,including surface reconstruction,dimensional construction,and electric-field regulation.Finally,we propose directions for future research to advance the efficiency of perovskite solar cells towards the radiative limit and their widespread commercial application.

Variable-fidelity optimization with design space reduction

Advanced engineering systems, like aircraft, are defined by tens or even hundreds of design variables. Building an accurate surrogate model for use in such high-dimensional optimization problems is a difficult task owing to the curse of dimensionality. This paper presents a new algorithm to reduce the size of a design space to a smaller region of interest allowing a more accurate surrogate model to be generated. The framework requires a set of models of different physical or numerical fidelities. The low-fidelity （LF） model provides physics-based approximation of the high-fidelity （HF） model at a fraction of the computational cost. It is also instrumental in identifying the small region of interest in the design space that encloses the high-fidelity optimum. A surrogate model is then constructed to match the low-fidelity model to the high-fidelity model in the identified region of interest. The optimization process is managed by an update strategy to prevent convergence to false optima. The algorithm is applied on mathematical problems and a two-dimen-sional aerodynamic shape optimization problem in a variable-fidelity context. Results obtained are in excellent agreement with high-fidelity results, even with lower-fidelity flow solvers, while showing up to 39% time savings.