Enhanced plasmonic biosensors with machine learning for ultra-sensitive detection
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| Publicado en: | Nanoscale Research Letters vol. 21, no. 1 (Dec 2026), p. 1 |
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| Autor principal: | |
| Otros Autores: | , , , , , , , |
| Publicado: |
Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 1931-7573 | ||
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| 024 | 7 | |a 10.1186/s11671-025-04422-4 |2 doi | |
| 035 | |a 3289985012 | ||
| 045 | 2 | |b d20261201 |b d20261231 | |
| 084 | |a 243536 |2 nlm | ||
| 100 | 1 | |a Sheela, M. Sahaya |u Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Department of ECE, Chennai, India (GRID:grid.464713.3) (ISNI:0000 0004 1777 5670) | |
| 245 | 1 | |a Enhanced plasmonic biosensors with machine learning for ultra-sensitive detection | |
| 260 | |b Springer Nature B.V. |c Dec 2026 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Plasmonic biosensors, particularly Surface Plasmon Resonance and Surface-Enhanced Raman Spectroscopy, have gained significant attention for real-time, label-free biochemical detection. However, optimizing these sensors for maximum sensitivity and selectivity remains a challenge due to their complex plasmonic interactions with different biomolecules. This work proposes SERA, an AI driven framework that integrates machine learning algorithms with experimental Surface-Enhanced Raman Spectroscopy (SERS) data for the predictive modeling and optimization of plasmonic sensing performance. Using supervised learning techniques, the ML models are trained on a spectral dataset - SERS-DB obtained from various plasmonic nanostructures. The model predicts key parameters such as resonance shift, intensity variations, and molecular binding efficiency, allowing for rapid optimization of biosensor designs without extensive trial-and-error experimentation. This approach accelerates plasmonic biosensor development and enables real-time adaptive sensing based on live data. The results through evaluation on the SERS-DB database with 420 samples for training and 180 for the testing phase, 6 classes like Thiacloprid, Imidacloprid, Thiamethoxam, Nitenpyram, Tetrahydrofolate, and Dihydrofolate, an accuracy of 92%, precision & recall of 90%, and F1-score of 92% were attained. The SERA model excelled with an overall score of around 0.90 in all 6 classes, proving additional superiority in biosensing applications. Further comparative analysis of the proposed approach with conventional methods underscores the best performance in accuracy with 92%, sensitivity, 1000 nm/RIU, and 95% in optimization efficiency. Overall, this research highlights a scalable and cost-effective strategy for advancing biosensor technology in medical diagnostics, environmental monitoring, and bio photonics. | |
| 653 | |a Plasmonics | ||
| 653 | |a Imidacloprid | ||
| 653 | |a Environmental monitoring | ||
| 653 | |a Accuracy | ||
| 653 | |a Gold | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Deep learning | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Supervised learning | ||
| 653 | |a Silver | ||
| 653 | |a Biosensors | ||
| 653 | |a Thiamethoxam | ||
| 653 | |a Tetrahydrofolic acid | ||
| 653 | |a Machine learning | ||
| 653 | |a Surface plasmon resonance | ||
| 653 | |a Raman spectroscopy | ||
| 653 | |a Graphene | ||
| 653 | |a Research & development--R&D | ||
| 653 | |a Spectroscopy | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Medical technology | ||
| 653 | |a Biomolecules | ||
| 653 | |a Medical diagnosis | ||
| 653 | |a Malaria | ||
| 653 | |a Insecticides | ||
| 653 | |a Resonance | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Sensitivity | ||
| 653 | |a Prediction models | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Sensors | ||
| 653 | |a Zinc oxides | ||
| 653 | |a Optimization | ||
| 653 | |a Design | ||
| 653 | |a Optical properties | ||
| 653 | |a Thiacloprid | ||
| 653 | |a Real time | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Ponraj, A. |u Easwari Engineering College, Department of ECE, Chennai,, India (GRID:grid.464713.3) (ISNI:0000 0004 1774 1876) | |
| 700 | 1 | |a Kumarganesh, S. |u Knowledge Institute of Technology, Department of ECE, Salem, India (GRID:grid.464713.3) | |
| 700 | 1 | |a Thiyaneswaran, B. |u Sona College of Technology, Department of ECE, Salem, India (GRID:grid.464713.3) (ISNI:0000 0004 1764 6625) | |
| 700 | 1 | |a Rishabavarthani, P. |u Sri Ramakrishna Engineering College, Department of ECE, Coimbatore, India (GRID:grid.464713.3) (ISNI:0000 0004 1767 7042) | |
| 700 | 1 | |a Rajesh, I. |u Knowledge Institute of Technology, Department of CSE, Salem, India (GRID:grid.464713.3) | |
| 700 | 1 | |a Pandey, Binay Kumar |u Govind Ballabh Pant University of Agriculture and Technology Pantnagar, Department of Information Technology, College of Technology, Uttarakhand, India (GRID:grid.440691.e) (ISNI:0000 0001 0708 4444) | |
| 700 | 1 | |a Pandey, Digvijay |u (Government of U.P.), Department of Technical Education Uttar Pradesh, Lucknow, India (GRID:grid.440691.e) | |
| 700 | 1 | |a Lelisho, Mesfin Esayas |u Mizan-Tepi University, Department of Statistics, College of Natural and Computational Science, Tepi, Ethiopia (GRID:grid.449142.e) (ISNI:0000 0004 0403 6115) | |
| 773 | 0 | |t Nanoscale Research Letters |g vol. 21, no. 1 (Dec 2026), p. 1 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3289985012/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3289985012/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3289985012/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |