Genetic Algorithm-Driven Surface-Enhanced Raman Spectroscopy Substrate Optimization

Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive and molecule-specific detection technique that uses surface plasmon resonances to enhance Raman scattering from analytes. In SERS system design, the substrates must have minimal or no background at the incident laser wavelength and lar...

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Autores principales:	Buse Bilgin, Cenk Yanik, Hulya Torun, Mehmet Cengiz Onbasli
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	surface-enhanced Raman spectroscopy genetic algorithm metasurface Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/a45e5a4aa1544ccc9f94cc4a2ea3d1a5
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spelling	oai:doaj.org-article:a45e5a4aa1544ccc9f94cc4a2ea3d1a52021-11-25T18:30:45ZGenetic Algorithm-Driven Surface-Enhanced Raman Spectroscopy Substrate Optimization10.3390/nano111129052079-4991https://doaj.org/article/a45e5a4aa1544ccc9f94cc4a2ea3d1a52021-10-01T00:00:00Zhttps://www.mdpi.com/2079-4991/11/11/2905https://doaj.org/toc/2079-4991Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive and molecule-specific detection technique that uses surface plasmon resonances to enhance Raman scattering from analytes. In SERS system design, the substrates must have minimal or no background at the incident laser wavelength and large Raman signal enhancement via plasmonic confinement and grating modes over large areas (i.e., squared millimeters). These requirements impose many competing design constraints that make exhaustive parametric computational optimization of SERS substrates prohibitively time consuming. Here, we demonstrate a genetic-algorithm (GA)-based optimization method for SERS substrates to achieve strong electric field localization over wide areas for reconfigurable and programmable photonic SERS sensors. We analyzed the GA parameters and tuned them for SERS substrate optimization in detail. We experimentally validated the model results by fabricating the predicted nanostructures using electron beam lithography. The experimental Raman spectrum signal enhancements of the optimized SERS substrates validated the model predictions and enabled the generation of a detailed Raman profile of methylene blue fluorescence dye. The GA and its optimization shown here could pave the way for photonic chips and components with arbitrary design constraints, wavelength bands, and performance targets.Buse BilginCenk YanikHulya TorunMehmet Cengiz OnbasliMDPI AGarticlesurface-enhanced Raman spectroscopygenetic algorithmmetasurfaceChemistryQD1-999ENNanomaterials, Vol 11, Iss 2905, p 2905 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	surface-enhanced Raman spectroscopy genetic algorithm metasurface Chemistry QD1-999
spellingShingle	surface-enhanced Raman spectroscopy genetic algorithm metasurface Chemistry QD1-999 Buse Bilgin Cenk Yanik Hulya Torun Mehmet Cengiz Onbasli Genetic Algorithm-Driven Surface-Enhanced Raman Spectroscopy Substrate Optimization
description	Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive and molecule-specific detection technique that uses surface plasmon resonances to enhance Raman scattering from analytes. In SERS system design, the substrates must have minimal or no background at the incident laser wavelength and large Raman signal enhancement via plasmonic confinement and grating modes over large areas (i.e., squared millimeters). These requirements impose many competing design constraints that make exhaustive parametric computational optimization of SERS substrates prohibitively time consuming. Here, we demonstrate a genetic-algorithm (GA)-based optimization method for SERS substrates to achieve strong electric field localization over wide areas for reconfigurable and programmable photonic SERS sensors. We analyzed the GA parameters and tuned them for SERS substrate optimization in detail. We experimentally validated the model results by fabricating the predicted nanostructures using electron beam lithography. The experimental Raman spectrum signal enhancements of the optimized SERS substrates validated the model predictions and enabled the generation of a detailed Raman profile of methylene blue fluorescence dye. The GA and its optimization shown here could pave the way for photonic chips and components with arbitrary design constraints, wavelength bands, and performance targets.
format	article
author	Buse Bilgin Cenk Yanik Hulya Torun Mehmet Cengiz Onbasli
author_facet	Buse Bilgin Cenk Yanik Hulya Torun Mehmet Cengiz Onbasli
author_sort	Buse Bilgin
title	Genetic Algorithm-Driven Surface-Enhanced Raman Spectroscopy Substrate Optimization
title_short	Genetic Algorithm-Driven Surface-Enhanced Raman Spectroscopy Substrate Optimization
title_full	Genetic Algorithm-Driven Surface-Enhanced Raman Spectroscopy Substrate Optimization
title_fullStr	Genetic Algorithm-Driven Surface-Enhanced Raman Spectroscopy Substrate Optimization
title_full_unstemmed	Genetic Algorithm-Driven Surface-Enhanced Raman Spectroscopy Substrate Optimization
title_sort	genetic algorithm-driven surface-enhanced raman spectroscopy substrate optimization
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a45e5a4aa1544ccc9f94cc4a2ea3d1a5
work_keys_str_mv	AT busebilgin geneticalgorithmdrivensurfaceenhancedramanspectroscopysubstrateoptimization AT cenkyanik geneticalgorithmdrivensurfaceenhancedramanspectroscopysubstrateoptimization AT hulyatorun geneticalgorithmdrivensurfaceenhancedramanspectroscopysubstrateoptimization AT mehmetcengizonbasli geneticalgorithmdrivensurfaceenhancedramanspectroscopysubstrateoptimization
_version_	1718411036515631104

Genetic Algorithm-Driven Surface-Enhanced Raman Spectroscopy Substrate Optimization

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