Evolutionary Photonics for Renewable Energy, Nanomedicine, and Advanced Material Engineering
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Applied Mathematics and Computational Science Program
PRIMALIGHT Research Group
KAUST Grant Number
OSR-2016-CRG5-463 2995Date
2018-09-17Online Publication Date
2018-09-17Print Publication Date
2018-11Permanent link to this record
http://hdl.handle.net/10754/630548
Metadata
Show full item recordAbstract
Taking inspiration from complex natural phenomena such as chaos and unpredictability, as well as highly optimized organisms constituted by multitudes of interacting cells, evolutionary photonics aims at developing new sustainable technologies for renewable energy production, bioimaging, and scalable materials with advanced optical functionalities. These photonics systems integrate complex dielectric and/or metallic units into large networks of heterogeneous elements, which are entirely controlled by acting on the interactions among the different units that compose the network. This Review presents a selection of recent results in this research field, discussing advantages, challenges and perspectives of this technology in addressing global challenges in several scientific areas, ranging from broadband light energy harvesting, to scalable clean water production, early-stage cancer detection, sensing, artificial intelligence, invisible structures, and to large-scale optical materials with programmable functionality and robust structural coloration.Citation
Favraud G, Gongora JST, Fratalocchi A (2018) Evolutionary Photonics for Renewable Energy, Nanomedicine, and Advanced Material Engineering. Laser & Photonics Reviews 12: 1700028. Available: http://dx.doi.org/10.1002/lpor.201700028.Sponsors
A.F. acknowledges funding from KAUST (Award No. OSR-2016-CRG5-463 2995).Publisher
WileyJournal
Laser & Photonics ReviewsAdditional Links
https://onlinelibrary.wiley.com/doi/full/10.1002/lpor.201700028ae974a485f413a2113503eed53cd6c53
10.1002/lpor.201700028