PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 19, 2026Micromachines0 citationsOpen Access

Intrinsic Relations Between Transmission and Reflection in Metamaterials

View Full Paper
BXBoli XuUniversity of Electronic Science and Technology of ChinaRZRenbin ZhongUniversity of Electronic Science and Technology of China

Key Points

  • This research aims to explore the intrinsic constraints governing the modulation of electromagnetic waves in metamaterials.
  • Analyzed statistical amplitudes and phases of transmission and reflection waves
  • Utilized scattering theory for analysis
  • Developed a general description of the electromagnetic modulation process
  • Derived geometry-independent corollaries for wave coupling
  • Identified intrinsic constraints affecting wave modulation in metamaterials
  • Established a general framework for understanding electromagnetic wave interaction
  • Verified two geometry-independent relationships between transmission and reflection waves

Abstract

Metamaterials possess high freedom on structural design, yet their ability to modulate electromagnetic waves is subject to intrinsic constraints that are independent of specific meta-atom geometries. The constraints are revealed by analyzing the statistical amplitudes and phases of transmission and reflection wave in some representative metamaterials. Based on scattering theory, a reconstructed and more general description of the electromagnetic modulation process in metamaterials is established. Two explicit and geometry-independent corollaries concerning the coupling between transmission and reflection waves are further obtained and verified. The results provide a new perspective on the fundamental modulation mechanism of metamaterials on electromagnetic waves.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/69e47250010ef96374d8e595https://doi.org/10.3390/mi17040493
Ask AI
Helpful
Bookmark
Share
View Full Paper