生物传感器
材料科学
灵敏度(控制系统)
超参数
深度学习
分析物
电介质
计算机科学
纳米技术
生物系统
光电子学
人工智能
电子工程
色谱法
化学
工程类
生物
作者
Sadia Afreen Chowdhury,Lway Faisal Abdulrazak,Sumaiya Akhtar Mitu,Kawsar Ahmed,Francis M. Bui,Lassaad K. Smirani,Li Chen
标识
DOI:10.1016/j.aej.2023.06.093
摘要
A very sensitive multi-channel refractive index-based biosensor with a detection range of 1.26 to 1.36 has been proposed. Titanium oxide (TiO2) was utilized as the dielectric, and gold as the plasmonic material in the sensor. The suggested sensor achieved a maximum sensitivity value of 48,000 nm/RIU and 7220 RIU-1 using the wavelength and amplitude interrogation techniques with a pitch layer variation of 5.55–5.95 μm and a metal layer variation of 30–50 nm. The suggested sensor can detect many compounds with amazing sensitivity, such as glucose, sucrose, ethanol, methanol, human intestinal mucosa, sevoflurane, and bio-chemicals. Additionally, a deep learning model that forecasts six optical properties has been proposed. The hyperparameters of the deep neural network model are tuned extensively to maximize accuracy. Our model had a Mean Squared Error of 0.006 and was significantly faster than conventional techniques. The results show that deep learning can predict optical features in photonic sensors, which can identify specific analytes or biomolecules in a sample without costly and time-consuming simulations. Photonic sensor researchers and engineers may utilize the suggested model on vast datasets and adjust it to new settings. This study may open the door to the use of deep learning-based optimization techniques to enhance biosensors.
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