Machine learning approach to surface plasmon resonance bio-chemical sensor based on nanocarbon allotropes for formalin detection in water
This article investigates the design of a surface plasmon resonance (SPR) sensor that utilizes carbon nanotubes (CNT) and graphene to detect formalin concentration in water. The proposed sensor's design optimization and performance evaluation are achieved by implementing Gradient Boosting Regre...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Sensing and Bio-Sensing Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214180423000570 |
| _version_ | 1797448076414156800 |
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| author | Gufranullah Ansari Amrindra Pal Alok K. Srivastava Gaurav Verma |
| author_facet | Gufranullah Ansari Amrindra Pal Alok K. Srivastava Gaurav Verma |
| author_sort | Gufranullah Ansari |
| collection | DOAJ |
| description | This article investigates the design of a surface plasmon resonance (SPR) sensor that utilizes carbon nanotubes (CNT) and graphene to detect formalin concentration in water. The proposed sensor's design optimization and performance evaluation are achieved by implementing Gradient Boosting Regression (GBR), a machine learning (ML) algorithm, and the artificial hummingbird algorithm. An iterative transfer matrix technique is employed to create training and test sets for machine learning analysis, and a dataset of 8505 × 8 is obtained. The optimized thickness of Ag, CNT, and graphene 51.71 nm, 0.489 nm, and 4.32 nm were obtained using the artificial hummingbird algorithm. The results demonstrate that the SPR sensor achieves excellent reflectance curves, leading to a significant increase in detection sensitivity of 340.44 deg./RIU. Other characteristic parameters such as detection accuracy (DA), full width at half maximum (FWHM), and figure of merit (FoM) have also been evaluated. |
| first_indexed | 2024-03-09T14:05:09Z |
| format | Article |
| id | doaj.art-c2fd4ea1bed841988584f5437c3f6183 |
| institution | Directory Open Access Journal |
| issn | 2214-1804 |
| language | English |
| last_indexed | 2024-03-09T14:05:09Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Sensing and Bio-Sensing Research |
| spelling | doaj.art-c2fd4ea1bed841988584f5437c3f61832023-11-30T05:07:31ZengElsevierSensing and Bio-Sensing Research2214-18042023-12-0142100605Machine learning approach to surface plasmon resonance bio-chemical sensor based on nanocarbon allotropes for formalin detection in waterGufranullah Ansari0Amrindra Pal1Alok K. Srivastava2Gaurav Verma3Dr. Shanti Swarup Bhatnagar University Institute of Chemical Engineering and Technology (formerly Department of Chemical Engineering & Technology), Panjab University, Chandigarh 160014, IndiaNational Research Council Nepal, Bijulibazar, Kathmandu 44600, Nepal; Corresponding author.Defence Materials and Stores R & D Establishment (DRDO), Kanpur 208013, IndiaDr. Shanti Swarup Bhatnagar University Institute of Chemical Engineering and Technology (formerly Department of Chemical Engineering & Technology), Panjab University, Chandigarh 160014, India; Energy Research Centre, Panjab University, Chandigarh 160014, India; Centre for Nanoscience & Nanotechnology, University Institute for Emerging Areas in Science and Technology, Panjab University, Chandigarh 160014, IndiaThis article investigates the design of a surface plasmon resonance (SPR) sensor that utilizes carbon nanotubes (CNT) and graphene to detect formalin concentration in water. The proposed sensor's design optimization and performance evaluation are achieved by implementing Gradient Boosting Regression (GBR), a machine learning (ML) algorithm, and the artificial hummingbird algorithm. An iterative transfer matrix technique is employed to create training and test sets for machine learning analysis, and a dataset of 8505 × 8 is obtained. The optimized thickness of Ag, CNT, and graphene 51.71 nm, 0.489 nm, and 4.32 nm were obtained using the artificial hummingbird algorithm. The results demonstrate that the SPR sensor achieves excellent reflectance curves, leading to a significant increase in detection sensitivity of 340.44 deg./RIU. Other characteristic parameters such as detection accuracy (DA), full width at half maximum (FWHM), and figure of merit (FoM) have also been evaluated.http://www.sciencedirect.com/science/article/pii/S2214180423000570Carbon nanotubeSurface plasmon resonanceFormalin detectionMachine learningGraphene |
| spellingShingle | Gufranullah Ansari Amrindra Pal Alok K. Srivastava Gaurav Verma Machine learning approach to surface plasmon resonance bio-chemical sensor based on nanocarbon allotropes for formalin detection in water Sensing and Bio-Sensing Research Carbon nanotube Surface plasmon resonance Formalin detection Machine learning Graphene |
| title | Machine learning approach to surface plasmon resonance bio-chemical sensor based on nanocarbon allotropes for formalin detection in water |
| title_full | Machine learning approach to surface plasmon resonance bio-chemical sensor based on nanocarbon allotropes for formalin detection in water |
| title_fullStr | Machine learning approach to surface plasmon resonance bio-chemical sensor based on nanocarbon allotropes for formalin detection in water |
| title_full_unstemmed | Machine learning approach to surface plasmon resonance bio-chemical sensor based on nanocarbon allotropes for formalin detection in water |
| title_short | Machine learning approach to surface plasmon resonance bio-chemical sensor based on nanocarbon allotropes for formalin detection in water |
| title_sort | machine learning approach to surface plasmon resonance bio chemical sensor based on nanocarbon allotropes for formalin detection in water |
| topic | Carbon nanotube Surface plasmon resonance Formalin detection Machine learning Graphene |
| url | http://www.sciencedirect.com/science/article/pii/S2214180423000570 |
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