Enhanced plasmonic biosensors with machine learning for ultra-sensitive detection

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Dades bibliogràfiques
Publicat a:	Nanoscale Research Letters vol. 21, no. 1 (Dec 2026), p. 1
Autor principal:	Sheela, M. Sahaya
Altres autors:	Ponraj, A., Kumarganesh, S., Thiyaneswaran, B., Rishabavarthani, P., Rajesh, I., Pandey, Binay Kumar, Pandey, Digvijay, Lelisho, Mesfin Esayas
Publicat:	Springer Nature B.V.
Matèries:	Plasmonics Imidacloprid Environmental monitoring Accuracy Gold Comparative analysis Deep learning Optimization techniques Supervised learning Silver Biosensors Thiamethoxam Tetrahydrofolic acid Machine learning Surface plasmon resonance Raman spectroscopy Graphene Research & development > R&D Spectroscopy Learning algorithms Medical technology Biomolecules Medical diagnosis Malaria Insecticides Resonance Spectrum analysis Sensitivity Prediction models Genetic algorithms Sensors Zinc oxides Optimization Design Optical properties Thiacloprid Real time Environmental
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Descripció
Resum:	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.
ISSN:	1931-7573 1556-276X
DOI:	10.1186/s11671-025-04422-4
Font:	Materials Science Database