Borophene-$ Ge_{2} $$ Sb_{2} $$ Te_{5} $ (GST)-Based Refractive Index Sensor: Numerical Study and Behaviour Prediction Using Machine Learning
Abstract We proposed the numerical studies and machine learning prediction for the multilayered Borophene-GST-silica-Ag-based refractive index sensor for the 1.3–1.5 µm wavelength range. The top layers of the proposed sensor incorporate Ag gratings. The proposed structure utilizes a mode based on su...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sorathiya, Vishal [verfasserIn] Soni, Umangbhai [verfasserIn] Vekariya, Vipul [verfasserIn] Golani, Jaysheel [verfasserIn] Almawgani, Abdulkarem H. M. [verfasserIn] Alhawari, Adam R. H. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Plasmonics - Springer US, 2006, 19(2023), 3 vom: 02. Okt., Seite 1211-1226 |
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| Übergeordnetes Werk: |
volume:19 ; year:2023 ; number:3 ; day:02 ; month:10 ; pages:1211-1226 |
| Links: |
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| DOI / URN: |
10.1007/s11468-023-02073-8 |
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| Katalog-ID: |
SPR055943071 |
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| 520 | |a Abstract We proposed the numerical studies and machine learning prediction for the multilayered Borophene-GST-silica-Ag-based refractive index sensor for the 1.3–1.5 µm wavelength range. The top layers of the proposed sensor incorporate Ag gratings. The proposed structure utilizes a mode based on surface plasmonic resonance, which has been created using a finite element model through computational analysis. Resonance is observed across a range of refractive index values from 1 to 2.5, which primarily aligns with the refractive indices of key biomolecules such as haemoglobin, saliva, urine, and cancerous cells. The proposed structure has also been investigated for the different physical parameters such as material height, grating space, the incident wave’s wide incident angle, and the GST material’s phase. We demonstrate the modulation of reflectance values in response to varying phases of the GST material (amorphous and crystalline), which can be controlled through external temperature changes. The distribution of the electric and magnetic fields of the structure is also provided in order to examine the field distribution across the grating and other layers of material. This sensor offers a wide angle of stability (0 to 80°) for the respective resonating points. An artificial neural network is employed to analyze the simulated data and make predictions regarding the behaviour of the structure. We have found the $ R^{2} $ = 0.97 for the proposed artificial neural network (ANN) model. The ANN model of this structure also presented the values of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) for different epoch values. Results shown in this research can help to sense the wide range of biomolecule samples whose refractive index ranges from 1 to 2.5. The wide wavelength range makes this device applicable for developing wideband infrared biosensors. | ||
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10.1007/s11468-023-02073-8 doi (DE-627)SPR055943071 (SPR)s11468-023-02073-8-e DE-627 ger DE-627 rakwb eng 540 VZ 33.68 bkl 44.09 bkl 51.45 bkl Sorathiya, Vishal verfasserin aut Borophene-$ Ge_{2} $$ Sb_{2} $$ Te_{5} $ (GST)-Based Refractive Index Sensor: Numerical Study and Behaviour Prediction Using Machine Learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We proposed the numerical studies and machine learning prediction for the multilayered Borophene-GST-silica-Ag-based refractive index sensor for the 1.3–1.5 µm wavelength range. The top layers of the proposed sensor incorporate Ag gratings. The proposed structure utilizes a mode based on surface plasmonic resonance, which has been created using a finite element model through computational analysis. Resonance is observed across a range of refractive index values from 1 to 2.5, which primarily aligns with the refractive indices of key biomolecules such as haemoglobin, saliva, urine, and cancerous cells. The proposed structure has also been investigated for the different physical parameters such as material height, grating space, the incident wave’s wide incident angle, and the GST material’s phase. We demonstrate the modulation of reflectance values in response to varying phases of the GST material (amorphous and crystalline), which can be controlled through external temperature changes. The distribution of the electric and magnetic fields of the structure is also provided in order to examine the field distribution across the grating and other layers of material. This sensor offers a wide angle of stability (0 to 80°) for the respective resonating points. An artificial neural network is employed to analyze the simulated data and make predictions regarding the behaviour of the structure. We have found the $ R^{2} $ = 0.97 for the proposed artificial neural network (ANN) model. The ANN model of this structure also presented the values of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) for different epoch values. Results shown in this research can help to sense the wide range of biomolecule samples whose refractive index ranges from 1 to 2.5. The wide wavelength range makes this device applicable for developing wideband infrared biosensors. Surface plasmon resonance (dpeaa)DE-He213 Biosensor (dpeaa)DE-He213 Refractive index sensor (dpeaa)DE-He213 Reflectance (dpeaa)DE-He213 GST (dpeaa)DE-He213 Borophene (dpeaa)DE-He213 Soni, Umangbhai verfasserin aut Vekariya, Vipul verfasserin aut Golani, Jaysheel verfasserin aut Almawgani, Abdulkarem H. M. verfasserin aut Alhawari, Adam R. H. verfasserin aut Enthalten in Plasmonics Springer US, 2006 19(2023), 3 vom: 02. Okt., Seite 1211-1226 (DE-627)512879648 (DE-600)2237548-X 1557-1963 nnns volume:19 year:2023 number:3 day:02 month:10 pages:1211-1226 https://dx.doi.org/10.1007/s11468-023-02073-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.68 VZ 44.09 VZ 51.45 VZ AR 19 2023 3 02 10 1211-1226 |
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10.1007/s11468-023-02073-8 doi (DE-627)SPR055943071 (SPR)s11468-023-02073-8-e DE-627 ger DE-627 rakwb eng 540 VZ 33.68 bkl 44.09 bkl 51.45 bkl Sorathiya, Vishal verfasserin aut Borophene-$ Ge_{2} $$ Sb_{2} $$ Te_{5} $ (GST)-Based Refractive Index Sensor: Numerical Study and Behaviour Prediction Using Machine Learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We proposed the numerical studies and machine learning prediction for the multilayered Borophene-GST-silica-Ag-based refractive index sensor for the 1.3–1.5 µm wavelength range. The top layers of the proposed sensor incorporate Ag gratings. The proposed structure utilizes a mode based on surface plasmonic resonance, which has been created using a finite element model through computational analysis. Resonance is observed across a range of refractive index values from 1 to 2.5, which primarily aligns with the refractive indices of key biomolecules such as haemoglobin, saliva, urine, and cancerous cells. The proposed structure has also been investigated for the different physical parameters such as material height, grating space, the incident wave’s wide incident angle, and the GST material’s phase. We demonstrate the modulation of reflectance values in response to varying phases of the GST material (amorphous and crystalline), which can be controlled through external temperature changes. The distribution of the electric and magnetic fields of the structure is also provided in order to examine the field distribution across the grating and other layers of material. This sensor offers a wide angle of stability (0 to 80°) for the respective resonating points. An artificial neural network is employed to analyze the simulated data and make predictions regarding the behaviour of the structure. We have found the $ R^{2} $ = 0.97 for the proposed artificial neural network (ANN) model. The ANN model of this structure also presented the values of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) for different epoch values. Results shown in this research can help to sense the wide range of biomolecule samples whose refractive index ranges from 1 to 2.5. The wide wavelength range makes this device applicable for developing wideband infrared biosensors. Surface plasmon resonance (dpeaa)DE-He213 Biosensor (dpeaa)DE-He213 Refractive index sensor (dpeaa)DE-He213 Reflectance (dpeaa)DE-He213 GST (dpeaa)DE-He213 Borophene (dpeaa)DE-He213 Soni, Umangbhai verfasserin aut Vekariya, Vipul verfasserin aut Golani, Jaysheel verfasserin aut Almawgani, Abdulkarem H. M. verfasserin aut Alhawari, Adam R. H. verfasserin aut Enthalten in Plasmonics Springer US, 2006 19(2023), 3 vom: 02. Okt., Seite 1211-1226 (DE-627)512879648 (DE-600)2237548-X 1557-1963 nnns volume:19 year:2023 number:3 day:02 month:10 pages:1211-1226 https://dx.doi.org/10.1007/s11468-023-02073-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.68 VZ 44.09 VZ 51.45 VZ AR 19 2023 3 02 10 1211-1226 |
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10.1007/s11468-023-02073-8 doi (DE-627)SPR055943071 (SPR)s11468-023-02073-8-e DE-627 ger DE-627 rakwb eng 540 VZ 33.68 bkl 44.09 bkl 51.45 bkl Sorathiya, Vishal verfasserin aut Borophene-$ Ge_{2} $$ Sb_{2} $$ Te_{5} $ (GST)-Based Refractive Index Sensor: Numerical Study and Behaviour Prediction Using Machine Learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We proposed the numerical studies and machine learning prediction for the multilayered Borophene-GST-silica-Ag-based refractive index sensor for the 1.3–1.5 µm wavelength range. The top layers of the proposed sensor incorporate Ag gratings. The proposed structure utilizes a mode based on surface plasmonic resonance, which has been created using a finite element model through computational analysis. Resonance is observed across a range of refractive index values from 1 to 2.5, which primarily aligns with the refractive indices of key biomolecules such as haemoglobin, saliva, urine, and cancerous cells. The proposed structure has also been investigated for the different physical parameters such as material height, grating space, the incident wave’s wide incident angle, and the GST material’s phase. We demonstrate the modulation of reflectance values in response to varying phases of the GST material (amorphous and crystalline), which can be controlled through external temperature changes. The distribution of the electric and magnetic fields of the structure is also provided in order to examine the field distribution across the grating and other layers of material. This sensor offers a wide angle of stability (0 to 80°) for the respective resonating points. An artificial neural network is employed to analyze the simulated data and make predictions regarding the behaviour of the structure. We have found the $ R^{2} $ = 0.97 for the proposed artificial neural network (ANN) model. The ANN model of this structure also presented the values of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) for different epoch values. Results shown in this research can help to sense the wide range of biomolecule samples whose refractive index ranges from 1 to 2.5. The wide wavelength range makes this device applicable for developing wideband infrared biosensors. Surface plasmon resonance (dpeaa)DE-He213 Biosensor (dpeaa)DE-He213 Refractive index sensor (dpeaa)DE-He213 Reflectance (dpeaa)DE-He213 GST (dpeaa)DE-He213 Borophene (dpeaa)DE-He213 Soni, Umangbhai verfasserin aut Vekariya, Vipul verfasserin aut Golani, Jaysheel verfasserin aut Almawgani, Abdulkarem H. M. verfasserin aut Alhawari, Adam R. H. verfasserin aut Enthalten in Plasmonics Springer US, 2006 19(2023), 3 vom: 02. Okt., Seite 1211-1226 (DE-627)512879648 (DE-600)2237548-X 1557-1963 nnns volume:19 year:2023 number:3 day:02 month:10 pages:1211-1226 https://dx.doi.org/10.1007/s11468-023-02073-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.68 VZ 44.09 VZ 51.45 VZ AR 19 2023 3 02 10 1211-1226 |
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10.1007/s11468-023-02073-8 doi (DE-627)SPR055943071 (SPR)s11468-023-02073-8-e DE-627 ger DE-627 rakwb eng 540 VZ 33.68 bkl 44.09 bkl 51.45 bkl Sorathiya, Vishal verfasserin aut Borophene-$ Ge_{2} $$ Sb_{2} $$ Te_{5} $ (GST)-Based Refractive Index Sensor: Numerical Study and Behaviour Prediction Using Machine Learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We proposed the numerical studies and machine learning prediction for the multilayered Borophene-GST-silica-Ag-based refractive index sensor for the 1.3–1.5 µm wavelength range. The top layers of the proposed sensor incorporate Ag gratings. The proposed structure utilizes a mode based on surface plasmonic resonance, which has been created using a finite element model through computational analysis. Resonance is observed across a range of refractive index values from 1 to 2.5, which primarily aligns with the refractive indices of key biomolecules such as haemoglobin, saliva, urine, and cancerous cells. The proposed structure has also been investigated for the different physical parameters such as material height, grating space, the incident wave’s wide incident angle, and the GST material’s phase. We demonstrate the modulation of reflectance values in response to varying phases of the GST material (amorphous and crystalline), which can be controlled through external temperature changes. The distribution of the electric and magnetic fields of the structure is also provided in order to examine the field distribution across the grating and other layers of material. This sensor offers a wide angle of stability (0 to 80°) for the respective resonating points. An artificial neural network is employed to analyze the simulated data and make predictions regarding the behaviour of the structure. We have found the $ R^{2} $ = 0.97 for the proposed artificial neural network (ANN) model. The ANN model of this structure also presented the values of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) for different epoch values. Results shown in this research can help to sense the wide range of biomolecule samples whose refractive index ranges from 1 to 2.5. The wide wavelength range makes this device applicable for developing wideband infrared biosensors. Surface plasmon resonance (dpeaa)DE-He213 Biosensor (dpeaa)DE-He213 Refractive index sensor (dpeaa)DE-He213 Reflectance (dpeaa)DE-He213 GST (dpeaa)DE-He213 Borophene (dpeaa)DE-He213 Soni, Umangbhai verfasserin aut Vekariya, Vipul verfasserin aut Golani, Jaysheel verfasserin aut Almawgani, Abdulkarem H. M. verfasserin aut Alhawari, Adam R. H. verfasserin aut Enthalten in Plasmonics Springer US, 2006 19(2023), 3 vom: 02. Okt., Seite 1211-1226 (DE-627)512879648 (DE-600)2237548-X 1557-1963 nnns volume:19 year:2023 number:3 day:02 month:10 pages:1211-1226 https://dx.doi.org/10.1007/s11468-023-02073-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.68 VZ 44.09 VZ 51.45 VZ AR 19 2023 3 02 10 1211-1226 |
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10.1007/s11468-023-02073-8 doi (DE-627)SPR055943071 (SPR)s11468-023-02073-8-e DE-627 ger DE-627 rakwb eng 540 VZ 33.68 bkl 44.09 bkl 51.45 bkl Sorathiya, Vishal verfasserin aut Borophene-$ Ge_{2} $$ Sb_{2} $$ Te_{5} $ (GST)-Based Refractive Index Sensor: Numerical Study and Behaviour Prediction Using Machine Learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We proposed the numerical studies and machine learning prediction for the multilayered Borophene-GST-silica-Ag-based refractive index sensor for the 1.3–1.5 µm wavelength range. The top layers of the proposed sensor incorporate Ag gratings. The proposed structure utilizes a mode based on surface plasmonic resonance, which has been created using a finite element model through computational analysis. Resonance is observed across a range of refractive index values from 1 to 2.5, which primarily aligns with the refractive indices of key biomolecules such as haemoglobin, saliva, urine, and cancerous cells. The proposed structure has also been investigated for the different physical parameters such as material height, grating space, the incident wave’s wide incident angle, and the GST material’s phase. We demonstrate the modulation of reflectance values in response to varying phases of the GST material (amorphous and crystalline), which can be controlled through external temperature changes. The distribution of the electric and magnetic fields of the structure is also provided in order to examine the field distribution across the grating and other layers of material. This sensor offers a wide angle of stability (0 to 80°) for the respective resonating points. An artificial neural network is employed to analyze the simulated data and make predictions regarding the behaviour of the structure. We have found the $ R^{2} $ = 0.97 for the proposed artificial neural network (ANN) model. The ANN model of this structure also presented the values of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) for different epoch values. Results shown in this research can help to sense the wide range of biomolecule samples whose refractive index ranges from 1 to 2.5. The wide wavelength range makes this device applicable for developing wideband infrared biosensors. Surface plasmon resonance (dpeaa)DE-He213 Biosensor (dpeaa)DE-He213 Refractive index sensor (dpeaa)DE-He213 Reflectance (dpeaa)DE-He213 GST (dpeaa)DE-He213 Borophene (dpeaa)DE-He213 Soni, Umangbhai verfasserin aut Vekariya, Vipul verfasserin aut Golani, Jaysheel verfasserin aut Almawgani, Abdulkarem H. M. verfasserin aut Alhawari, Adam R. H. verfasserin aut Enthalten in Plasmonics Springer US, 2006 19(2023), 3 vom: 02. Okt., Seite 1211-1226 (DE-627)512879648 (DE-600)2237548-X 1557-1963 nnns volume:19 year:2023 number:3 day:02 month:10 pages:1211-1226 https://dx.doi.org/10.1007/s11468-023-02073-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.68 VZ 44.09 VZ 51.45 VZ AR 19 2023 3 02 10 1211-1226 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract We proposed the numerical studies and machine learning prediction for the multilayered Borophene-GST-silica-Ag-based refractive index sensor for the 1.3–1.5 µm wavelength range. The top layers of the proposed sensor incorporate Ag gratings. The proposed structure utilizes a mode based on surface plasmonic resonance, which has been created using a finite element model through computational analysis. Resonance is observed across a range of refractive index values from 1 to 2.5, which primarily aligns with the refractive indices of key biomolecules such as haemoglobin, saliva, urine, and cancerous cells. The proposed structure has also been investigated for the different physical parameters such as material height, grating space, the incident wave’s wide incident angle, and the GST material’s phase. We demonstrate the modulation of reflectance values in response to varying phases of the GST material (amorphous and crystalline), which can be controlled through external temperature changes. The distribution of the electric and magnetic fields of the structure is also provided in order to examine the field distribution across the grating and other layers of material. This sensor offers a wide angle of stability (0 to 80°) for the respective resonating points. An artificial neural network is employed to analyze the simulated data and make predictions regarding the behaviour of the structure. We have found the $ R^{2} $ = 0.97 for the proposed artificial neural network (ANN) model. The ANN model of this structure also presented the values of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) for different epoch values. Results shown in this research can help to sense the wide range of biomolecule samples whose refractive index ranges from 1 to 2.5. 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| author |
Sorathiya, Vishal |
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Sorathiya, Vishal ddc 540 bkl 33.68 bkl 44.09 bkl 51.45 misc Surface plasmon resonance misc Biosensor misc Refractive index sensor misc Reflectance misc GST misc Borophene Borophene-$ Ge_{2} $$ Sb_{2} $$ Te_{5} $ (GST)-Based Refractive Index Sensor: Numerical Study and Behaviour Prediction Using Machine Learning |
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Sorathiya, Vishal |
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540 - Chemistry & allied sciences |
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1557-1963 |
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540 VZ 33.68 bkl 44.09 bkl 51.45 bkl Borophene-$ Ge_{2} $$ Sb_{2} $$ Te_{5} $ (GST)-Based Refractive Index Sensor: Numerical Study and Behaviour Prediction Using Machine Learning Surface plasmon resonance (dpeaa)DE-He213 Biosensor (dpeaa)DE-He213 Refractive index sensor (dpeaa)DE-He213 Reflectance (dpeaa)DE-He213 GST (dpeaa)DE-He213 Borophene (dpeaa)DE-He213 |
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ddc 540 bkl 33.68 bkl 44.09 bkl 51.45 misc Surface plasmon resonance misc Biosensor misc Refractive index sensor misc Reflectance misc GST misc Borophene |
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ddc 540 bkl 33.68 bkl 44.09 bkl 51.45 misc Surface plasmon resonance misc Biosensor misc Refractive index sensor misc Reflectance misc GST misc Borophene |
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ddc 540 bkl 33.68 bkl 44.09 bkl 51.45 misc Surface plasmon resonance misc Biosensor misc Refractive index sensor misc Reflectance misc GST misc Borophene |
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Elektronische Aufsätze Aufsätze Elektronische Ressource |
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Borophene-$ Ge_{2} $$ Sb_{2} $$ Te_{5} $ (GST)-Based Refractive Index Sensor: Numerical Study and Behaviour Prediction Using Machine Learning |
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Borophene-$ Ge_{2} $$ Sb_{2} $$ Te_{5} $ (GST)-Based Refractive Index Sensor: Numerical Study and Behaviour Prediction Using Machine Learning |
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Sorathiya, Vishal Soni, Umangbhai Vekariya, Vipul Golani, Jaysheel Almawgani, Abdulkarem H. M. Alhawari, Adam R. H. |
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borophene-$ ge_{2} $$ sb_{2} $$ te_{5} $ (gst)-based refractive index sensor: numerical study and behaviour prediction using machine learning |
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Borophene-$ Ge_{2} $$ Sb_{2} $$ Te_{5} $ (GST)-Based Refractive Index Sensor: Numerical Study and Behaviour Prediction Using Machine Learning |
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Abstract We proposed the numerical studies and machine learning prediction for the multilayered Borophene-GST-silica-Ag-based refractive index sensor for the 1.3–1.5 µm wavelength range. The top layers of the proposed sensor incorporate Ag gratings. The proposed structure utilizes a mode based on surface plasmonic resonance, which has been created using a finite element model through computational analysis. Resonance is observed across a range of refractive index values from 1 to 2.5, which primarily aligns with the refractive indices of key biomolecules such as haemoglobin, saliva, urine, and cancerous cells. The proposed structure has also been investigated for the different physical parameters such as material height, grating space, the incident wave’s wide incident angle, and the GST material’s phase. We demonstrate the modulation of reflectance values in response to varying phases of the GST material (amorphous and crystalline), which can be controlled through external temperature changes. The distribution of the electric and magnetic fields of the structure is also provided in order to examine the field distribution across the grating and other layers of material. This sensor offers a wide angle of stability (0 to 80°) for the respective resonating points. An artificial neural network is employed to analyze the simulated data and make predictions regarding the behaviour of the structure. We have found the $ R^{2} $ = 0.97 for the proposed artificial neural network (ANN) model. The ANN model of this structure also presented the values of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) for different epoch values. Results shown in this research can help to sense the wide range of biomolecule samples whose refractive index ranges from 1 to 2.5. The wide wavelength range makes this device applicable for developing wideband infrared biosensors. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract We proposed the numerical studies and machine learning prediction for the multilayered Borophene-GST-silica-Ag-based refractive index sensor for the 1.3–1.5 µm wavelength range. The top layers of the proposed sensor incorporate Ag gratings. The proposed structure utilizes a mode based on surface plasmonic resonance, which has been created using a finite element model through computational analysis. Resonance is observed across a range of refractive index values from 1 to 2.5, which primarily aligns with the refractive indices of key biomolecules such as haemoglobin, saliva, urine, and cancerous cells. The proposed structure has also been investigated for the different physical parameters such as material height, grating space, the incident wave’s wide incident angle, and the GST material’s phase. We demonstrate the modulation of reflectance values in response to varying phases of the GST material (amorphous and crystalline), which can be controlled through external temperature changes. The distribution of the electric and magnetic fields of the structure is also provided in order to examine the field distribution across the grating and other layers of material. This sensor offers a wide angle of stability (0 to 80°) for the respective resonating points. An artificial neural network is employed to analyze the simulated data and make predictions regarding the behaviour of the structure. We have found the $ R^{2} $ = 0.97 for the proposed artificial neural network (ANN) model. The ANN model of this structure also presented the values of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) for different epoch values. Results shown in this research can help to sense the wide range of biomolecule samples whose refractive index ranges from 1 to 2.5. The wide wavelength range makes this device applicable for developing wideband infrared biosensors. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract We proposed the numerical studies and machine learning prediction for the multilayered Borophene-GST-silica-Ag-based refractive index sensor for the 1.3–1.5 µm wavelength range. The top layers of the proposed sensor incorporate Ag gratings. The proposed structure utilizes a mode based on surface plasmonic resonance, which has been created using a finite element model through computational analysis. Resonance is observed across a range of refractive index values from 1 to 2.5, which primarily aligns with the refractive indices of key biomolecules such as haemoglobin, saliva, urine, and cancerous cells. The proposed structure has also been investigated for the different physical parameters such as material height, grating space, the incident wave’s wide incident angle, and the GST material’s phase. We demonstrate the modulation of reflectance values in response to varying phases of the GST material (amorphous and crystalline), which can be controlled through external temperature changes. The distribution of the electric and magnetic fields of the structure is also provided in order to examine the field distribution across the grating and other layers of material. This sensor offers a wide angle of stability (0 to 80°) for the respective resonating points. An artificial neural network is employed to analyze the simulated data and make predictions regarding the behaviour of the structure. We have found the $ R^{2} $ = 0.97 for the proposed artificial neural network (ANN) model. The ANN model of this structure also presented the values of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) for different epoch values. Results shown in this research can help to sense the wide range of biomolecule samples whose refractive index ranges from 1 to 2.5. The wide wavelength range makes this device applicable for developing wideband infrared biosensors. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Borophene-$ Ge_{2} $$ Sb_{2} $$ Te_{5} $ (GST)-Based Refractive Index Sensor: Numerical Study and Behaviour Prediction Using Machine Learning |
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| score |
7.3993464 |