Optical modeling, solver, and design of macroscopic single-enantiomer carbon nanotube film and reconfigurable chiral photonic device

The interaction of circularly polarized light with chiral matter and functional devices enables novel phenomena and applications. Recently, macroscopic solid-state single-enantiomer carbon nanotube (CNT) films have become feasible and are emerging as a chiral photonic material platform thanks to the...

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Veröffentlicht in:Carbon (New York) Jg. 235; S. 120016
Hauptverfasser: Fan, Jichao, Hillam, Benjamin, Guo, Cheng, Fujinami, Hiroyuki, Shiba, Koki, Xie, Haoyu, Chen, Ruiyang, Yanagi, Kazuhiro, Gao, Weilu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 10.03.2025
Schlagworte:
Backpropagation algorithm
carbon
carbon nanotubes
GPU acceleration
Phase change material
phase transition
photons
Reconfigurable chiral photonic devices
Single-enantiomer carbon nanotube
Transfer matrix
Reconfigurable chiral photonic devices
Transfer matrix
Backpropagation algorithm
Phase change material
Single-enantiomer carbon nanotube
GPU acceleration
ISSN:0008-6223
Online-Zugang:Volltext
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Zusammenfassung:The interaction of circularly polarized light with chiral matter and functional devices enables novel phenomena and applications. Recently, macroscopic solid-state single-enantiomer carbon nanotube (CNT) films have become feasible and are emerging as a chiral photonic material platform thanks to their quantum-confinement-induced optical properties and facile scalable assembly. However, optical modeling, solver, and device design tools for such materials are non-existent. Here, we prepare macroscopic single-enantiomer (6,5) and (11,-5) randomly oriented CNT films and create an optical material model based on measured experimental optical spectra. We also implement a highly-parallel graphic-processing-unit accelerated transfer matrix solver for general bi-anisotropic materials and layered structures. Further, we demonstrate reconfigurable chiral photonic devices in a heterostructure with phase change materials through machine learning-enabled efficient gradient-based inverse design and optimization. Our developed full stack of a chiral photonic material and device hardware platform and a corresponding high-performance differential-programming-enabled solver opens the door for future chiral photonic devices and applications based on single-enantiomer CNT films. [Display omitted]
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ISSN:0008-6223
DOI:10.1016/j.carbon.2025.120016