Orthogonal three-dimensional manipulation of a chiro-photonic hybrid-architecture enabling high-order information encryption

Multidimensional microstructure manipulation for tailored optical and physical properties remains a fundamental challenge in photonic materials engineering, which is primarily hindered by multi-field response correlations in self-assembling systems and the inherent static properties of fabricated microstructures. Here we present a groundbreaking approach that enables the orthogonal three-dimensional manipulation of a chiro-photonic hybrid-architecture <i>via</i> self-assembly of soft helices on surface relief nanostructures. This advanced hybrid-architecture allows for independent control of three critical structural parameters, including relief period, relief vector orientation and helical pitch. The period governs spectral information within individual channels, while the vector orientation switching facilitates channel integration and photoprogramming of the helical pitch further introduces dynamic spectral variations, collectively establishing a deterministic structure-information mapping paradigm. A straightforward encoding prototype has been implemented by using a three-channel multiplexed framework based on optical wavevector and spectral information, substantially achieving a tenfold enhancement in information capacity compared with conventional microstructures. Our work extends the capabilities of current technologies in responsive soft materials and opens new avenues for prospective application in dynamic multidimensional optical information modulation and integration systems.

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Publication Details

Journal
Materials Horizons
Published
2025-01-01
DOI
https://doi.org/10.1039/d5mh00637f
Citations
4
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
9.92

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article

Orthogonal three-dimensional manipulation of a chiro-photonic hybrid-architecture enabling high-order information encryption

Honglong Hu, Peizhi Sun, Zhigang Zheng, Conglong Yuan et al.
4 citations
Materials Horizons
Neural Networks and Reservoir Computing
9.92
article

Orthogonal three-dimensional manipulation of a chiro-photonic hybrid-architecture enabling high-order information encryption

Honglong Hu, Peizhi Sun, Zhigang Zheng, Conglong Yuan, Xuan Liu, Yifei Wang
article en
4 citations

Abstract

Multidimensional microstructure manipulation for tailored optical and physical properties remains a fundamental challenge in photonic materials engineering, which is primarily hindered by multi-field response correlations in self-assembling systems and the inherent static properties of fabricated microstructures. Here we present a groundbreaking approach that enables the orthogonal three-dimensional manipulation of a chiro-photonic hybrid-architecture <i>via</i> self-assembly of soft helices on surface relief nanostructures. This advanced hybrid-architecture allows for independent control of three critical structural parameters, including relief period, relief vector orientation and helical pitch. The period governs spectral information within individual channels, while the vector orientation switching facilitates channel integration and photoprogramming of the helical pitch further introduces dynamic spectral variations, collectively establishing a deterministic structure-information mapping paradigm. A straightforward encoding prototype has been implemented by using a three-channel multiplexed framework based on optical wavevector and spectral information, substantially achieving a tenfold enhancement in information capacity compared with conventional microstructures. Our work extends the capabilities of current technologies in responsive soft materials and opens new avenues for prospective application in dynamic multidimensional optical information modulation and integration systems.

Materials HorizonsVol. 12(15)
East China University of Science and Technology (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, National Key Research and Development Program of China
Openalex Percentile: Top 2%
Neural Networks and Reservoir Computing
9.92
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