Optimizing MOF‐Derived Electromagnetic Wave Absorbers through Gradient Pore Regulation for Pareto Improvement

Abstract Porous materials emerging as potential high‐efficiency electromagnetic (EM) wave absorbers confront a critical trade‐off between impedance matching and attenuation capability. In this study, a versatile strategy is reported to overcome this challenge by constructing gradient pores via solvent‐assisted linker exchange for the fabrication of metal‐organic framework (MOF) derived Fe/Fe 3 Co 7 /Co/C composites with high porosity. The impedance and attenuation characteristics of single‐pored and gradient‐pored derivatives are investigated through combined experimental and simulation approaches. Simulated space EM field, loss density, and Smith charts reveal significantly enhanced EM interactions and optimized impedance within the pores. Compared to individual MOF derivatives, the gradient derivative exhibits improved impedance matching from the large‐pored shell and superior attenuation capability from the small‐pored core, giving rise to a Pareto improvement in EM absorption with strong reflection loss (−64.7 dB) and wide effective adsorption bandwidth (5.8 GHz) at a thickness of 2.5 mm. This work not only advances a novel gradient pore strategy for constructing efficient absorbers with enhanced impedance matching and attenuation capability, but also sheds light on the underlying mechanisms of EM interaction with varied porosity, offering insights for extended designs in magnetic, electric and optic devices.

Authors

Institutions

Publication Details

Journal
Advanced Functional Materials
Published
2024-09-09
DOI
https://doi.org/10.1002/adfm.202413048
Citations
34
Primary Topic
Electromagnetic wave absorption materials
Type
article
Field-Weighted Citation Impact
4.01

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimizing MOF‐Derived Electromagnetic Wave Absorbers through Gradient Pore Regulation for Pareto Improvement

Chen Wu, Xiaoguang Wang, Mi Yan, Guang Liu et al.
34 citations
Advanced Functional Materials
Electromagnetic wave absorption materials
4.01
article

Optimizing MOF‐Derived Electromagnetic Wave Absorbers through Gradient Pore Regulation for Pareto Improvement

Chen Wu, Xiaoguang Wang, Mi Yan, Guang Liu, Runyi Xi, Panbin Zhu, Ji Teng
article en
34 citations

Abstract

Abstract Porous materials emerging as potential high‐efficiency electromagnetic (EM) wave absorbers confront a critical trade‐off between impedance matching and attenuation capability. In this study, a versatile strategy is reported to overcome this challenge by constructing gradient pores via solvent‐assisted linker exchange for the fabrication of metal‐organic framework (MOF) derived Fe/Fe 3 Co 7 /Co/C composites with high porosity. The impedance and attenuation characteristics of single‐pored and gradient‐pored derivatives are investigated through combined experimental and simulation approaches. Simulated space EM field, loss density, and Smith charts reveal significantly enhanced EM interactions and optimized impedance within the pores. Compared to individual MOF derivatives, the gradient derivative exhibits improved impedance matching from the large‐pored shell and superior attenuation capability from the small‐pored core, giving rise to a Pareto improvement in EM absorption with strong reflection loss (−64.7 dB) and wide effective adsorption bandwidth (5.8 GHz) at a thickness of 2.5 mm. This work not only advances a novel gradient pore strategy for constructing efficient absorbers with enhanced impedance matching and attenuation capability, but also sheds light on the underlying mechanisms of EM interaction with varied porosity, offering insights for extended designs in magnetic, electric and optic devices.

Advanced Functional MaterialsVol. 35(18)
Shanxi Electromechanical Design and Research Institute (CN), The Ohio State University (US), Zhejiang University (CN)
Ohio State University, National Natural Science Foundation of China
Openalex Percentile: Top 5%
Electromagnetic wave absorption materials
4.01
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.