Tunable Broadband Terahertz Metamaterial Absorber Based on Graphene

Tongzhe Zhang, Jun Zhu, Zao Yi, Shubo Cheng, Boxun Li · Wiley · 2026

A novel tunable broadband terahertz metamaterial absorber using graphene achieves over 90% absorptance across a wide frequency range.

High AI ConfidenceStrong SourceConceptEarly Research

Plain English summary

This research presents a new design for a terahertz metamaterial absorber that utilizes graphene. The structure allows for high absorptance, exceeding 90% across a frequency range of 2.89 to 6.43 THz, with perfect absorption at 4.25 THz. The design also accommodates adjustments for various applications and shows resilience to changes in the angle of incoming electromagnetic waves.

Why this matters

The development of a highly efficient terahertz absorber has significant implications for various technologies, including optoelectronic devices and stealth applications. By enhancing absorption capabilities, this research could lead to advancements in detection technologies and improved performance in electronic systems.

Key findings

  • Achieves over 90% absorptance in the 2.89–6.43 THz frequency range.
  • Perfect absorption at 4.25 THz.
  • Strong electric field coupling effect enhances broadband response.
  • Design allows for parameter adjustments based on application needs.
  • Exhibits tolerance to incident angle of electromagnetic waves.

What's new

Introduces a proportional structure for a tunable broadband absorber that leverages graphene, achieving high absorptance and design flexibility.

Limitations

The abstract does not provide details on practical implementation or real-world testing of the absorber.

Commercial context

The research is still in the conceptual stage and has not been demonstrated in practical applications.

Publication

Publisher
Wiley
Publication date
March 1, 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