Dorsiventrally Bicolored Leaf‐Inspired Metamaterial Absorbers for Tailorable Electromagnetic Absorption

Xiaohan Liu, Wenjun Cai, Huanrong Tian, Zidong Zhang, Fushan Li, Zixuan Liu +6 · Wiley · 2026

This research proposes a bioinspired metamaterial that achieves high-performance, tailorable electromagnetic absorption across a broad frequency range.

High AI ConfidenceStrong SourceLaboratory ResearchReadiness Unknown

Plain English summary

This study presents a new type of metamaterial absorber inspired by the structure of dorsiventrally bicolored leaves. The design allows for broadband electromagnetic absorption and the ability to reinforce specific frequencies, achieving over 80% absorption from 18 to 40 GHz. The research also includes a model for optimizing the design of the feedback layer to enhance performance further.

Why this matters

The ability to tailor electromagnetic absorption is crucial for various applications in electronics, such as improving the efficiency of devices and reducing interference. This research could lead to advancements in the design of high-performance absorbers that are adaptable to specific needs.

Key findings

  • Proposed a bioinspired hybrid metastructure for electromagnetic absorption.
  • Achieved over 80% absorptance from 18 to 40 GHz.
  • Enabled targeted reinforcement of weak-absorption frequencies.
  • Utilized a genetic-algorithm-optimized model for design.
  • Demonstrated good agreement between simulations and experiments.

What's new

Introduces a bioinspired framework that combines broadband response with targeted reinforcement and inverse design for electromagnetic absorbers.

Limitations

The abstract does not specify the practical applications or potential limitations in real-world scenarios.

Commercial context

The abstract does not provide information on commercial availability or readiness.

Publication

Publisher
Wiley
Publication date
July 10, 2026
Research type
Paper
License
http://onlinelibrary.wiley.com/termsAndConditions#vor

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Method note: Summaries and ratings on this page are generated by AI from the abstract only. Read the original paper for full context. · Model: gpt-4o-mini-2024-07-18