Optimization of Magnetoplasmonic <i>ε</i>-Near-Zero Nanostructures Using a Genetic Algorithm
Magnetoplasmonic permittivity-near-zero (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>-near-zero) nanostructures hold promise for novel hi...
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MDPI AG
2022-08-01
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| Online Access: | https://www.mdpi.com/1424-8220/22/15/5789 |
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| author | Felipe A. P. de Figueiredo Edwin Moncada-Villa Jorge Ricardo Mejía-Salazar |
| author_facet | Felipe A. P. de Figueiredo Edwin Moncada-Villa Jorge Ricardo Mejía-Salazar |
| author_sort | Felipe A. P. de Figueiredo |
| collection | DOAJ |
| description | Magnetoplasmonic permittivity-near-zero (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>-near-zero) nanostructures hold promise for novel highly integrated (bio)sensing devices. These platforms merge the high-resolution sensing from the magnetoplasmonic approach with the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>-near-zero-based light-to-plasmon coupling (instead of conventional gratings or bulky prism couplers), providing a way for sensing devices with higher miniaturization levels. However, the applications are mostly hindered by tedious and time-consuming numerical analyses, due to the lack of an analytical relation for the phase-matching condition. There is, therefore, a need to develop mechanisms that enable the exploitation of magnetoplasmonic <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>-near-zero nanostructures’ capabilities. In this work, we developed a genetic algorithm (GA) for the rapid design (in a few minutes) of magnetoplasmonic nanostructures with optimized TMOKE (transverse magneto-optical Kerr effect) signals and magnetoplasmonic sensing. Importantly, to illustrate the power and simplicity of our approach, we designed a magnetoplasmonic <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>-near-zero sensing platform with a sensitivity higher than <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mn>56</mn><mo>∘</mo></msup><mo>/</mo><mi>RIU</mi></mrow></semantics></math></inline-formula> and a figure of merit in the order of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mn>2</mn></msup></semantics></math></inline-formula>. These last results, higher than any previous magnetoplasmonic <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>-near-zero sensing approach, were obtained by the GA intelligent program in times ranging from 2 to 5 min (using a simple inexpensive dual-core CPU computer). |
| first_indexed | 2024-03-09T10:05:52Z |
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| language | English |
| last_indexed | 2024-03-09T10:05:52Z |
| publishDate | 2022-08-01 |
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| spelling | doaj.art-d7969dce4a7f46d7873290a71c3645862023-12-01T23:10:24ZengMDPI AGSensors1424-82202022-08-012215578910.3390/s22155789Optimization of Magnetoplasmonic <i>ε</i>-Near-Zero Nanostructures Using a Genetic AlgorithmFelipe A. P. de Figueiredo0Edwin Moncada-Villa1Jorge Ricardo Mejía-Salazar2Instituto Nacional de Telecomunicações (Inatel), Santa Rita do Sapucaí 37540-000, BrazilEscuela de Física, Universidad Pedagógica y Tecnológica de Colombia, Avenida Central del Norte 39-115, Tunja 150003, ColombiaInstituto Nacional de Telecomunicações (Inatel), Santa Rita do Sapucaí 37540-000, BrazilMagnetoplasmonic permittivity-near-zero (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>-near-zero) nanostructures hold promise for novel highly integrated (bio)sensing devices. These platforms merge the high-resolution sensing from the magnetoplasmonic approach with the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>-near-zero-based light-to-plasmon coupling (instead of conventional gratings or bulky prism couplers), providing a way for sensing devices with higher miniaturization levels. However, the applications are mostly hindered by tedious and time-consuming numerical analyses, due to the lack of an analytical relation for the phase-matching condition. There is, therefore, a need to develop mechanisms that enable the exploitation of magnetoplasmonic <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>-near-zero nanostructures’ capabilities. In this work, we developed a genetic algorithm (GA) for the rapid design (in a few minutes) of magnetoplasmonic nanostructures with optimized TMOKE (transverse magneto-optical Kerr effect) signals and magnetoplasmonic sensing. Importantly, to illustrate the power and simplicity of our approach, we designed a magnetoplasmonic <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>-near-zero sensing platform with a sensitivity higher than <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mn>56</mn><mo>∘</mo></msup><mo>/</mo><mi>RIU</mi></mrow></semantics></math></inline-formula> and a figure of merit in the order of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mn>2</mn></msup></semantics></math></inline-formula>. These last results, higher than any previous magnetoplasmonic <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>-near-zero sensing approach, were obtained by the GA intelligent program in times ranging from 2 to 5 min (using a simple inexpensive dual-core CPU computer).https://www.mdpi.com/1424-8220/22/15/5789genetic algorithm optimizationmagnetoplasmonicsmagneto-opticssensingTMOKE |
| spellingShingle | Felipe A. P. de Figueiredo Edwin Moncada-Villa Jorge Ricardo Mejía-Salazar Optimization of Magnetoplasmonic <i>ε</i>-Near-Zero Nanostructures Using a Genetic Algorithm Sensors genetic algorithm optimization magnetoplasmonics magneto-optics sensing TMOKE |
| title | Optimization of Magnetoplasmonic <i>ε</i>-Near-Zero Nanostructures Using a Genetic Algorithm |
| title_full | Optimization of Magnetoplasmonic <i>ε</i>-Near-Zero Nanostructures Using a Genetic Algorithm |
| title_fullStr | Optimization of Magnetoplasmonic <i>ε</i>-Near-Zero Nanostructures Using a Genetic Algorithm |
| title_full_unstemmed | Optimization of Magnetoplasmonic <i>ε</i>-Near-Zero Nanostructures Using a Genetic Algorithm |
| title_short | Optimization of Magnetoplasmonic <i>ε</i>-Near-Zero Nanostructures Using a Genetic Algorithm |
| title_sort | optimization of magnetoplasmonic i ε i near zero nanostructures using a genetic algorithm |
| topic | genetic algorithm optimization magnetoplasmonics magneto-optics sensing TMOKE |
| url | https://www.mdpi.com/1424-8220/22/15/5789 |
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