Abstract
Guided by ideas from complex systems, a new class of network metamaterials is introduced for light manipulation, which are based on the functional connectivity among heterogeneous subwavelength components arranged in complex networks. The model system is a nanonetwork formed by dealloying a metallic thin film. The connectivity of the network is deterministically controlled, enabling the formation of tunable absorbing states.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1600580 |
| Journal | Advanced Optical Materials |
| Volume | 5 |
| Issue number | 5 |
| DOIs | |
| State | Published - Dec 27 2016 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): CRG-1-2012-FRA-005)
Acknowledgements: H.G. and A.F. contributed equally to this work. H.G. designed and performed the experimental research and fabricated and analyzed the samples used in the article. A.F. designed the theoretical research, developed the network approach based on functional connectivity, and performed FDTD simulations. M.D. performed the Rutherford backscattering experiments. F.C. suggested experiments and contributed to the interpretation. All authors contributed equally to the preparation of the manuscript. H. Galinski gratefully acknowledges financial support from the Size Matters! project, (TDA Capital, UK). Sincere thanks are given to the EMEZ (Electron Microscopy Center, ETH Zurich) and the FIRST clean-room team for their support. A. Fratalocchi and F. Capasso acknowledge funding from KAUST (Award No. CRG-1-2012-FRA-005). F. Capasso and H. Galinski acknowledge the support of Air Force Office of Scientific Research (MURI: FA9550-14-1-0389). The authors declare that they have no competing financial interests. H. Galinski thanks M. Fiebig and M. Lilienblum from the Laboratory for Multifunctional Ferroic Materials (ETH Zurich) for access to the micro-spectrophotometer. A. Fratalocchi thanks P. Magistretti for fruitful discussions about brain functions and neural networks. This work was performed in part at the Center of Nanoscale Systems (CNS), a member of the National Nanotechnology Coordinated Infrastructure Network (NNCI), which is supported by the National Science Foundation under NSF award no. 1541959. CNS is part of Harvard University.