Design and analysis of high-sensitivity hormone sensor with KNN behavior prediction for healthcare and biomedical applications

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement Pub Date : 2024-11-15 DOI:10.1016/j.measurement.2024.116172

Jacob Wekalao , Abdullah Baz , Shobhit K. Patel

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Abstract

This study presents an advanced sensor system integrating gold metasurfaces with graphene for the detection of reproductive hormones via refractive index variations. The proposed design operates under terahertz (THz) excitation to exploit the unique molecular signatures of hormones and minimize potential interferences. Comprehensive numerical analysis and finite element method (FEM) simulations were conducted to optimize the sensor’s design parameters and evaluate its efficacy. The optimized sensor exhibits exceptional performance metrics, including a peak sensitivity of 375 GHzRIU⁻1, a figure of merit (FOM) of 7.693 RIU⁻1, a quality factor (Q) of 2.381, and a limit of detection (LOD) of 0.556 RIU. Furthermore, the sensor demonstrates two-bit encoding capabilities through modulation of graphene’s chemical potential. Integration of a K-Nearest Neighbors (KNN) Regressor model enhances the sensor’s accuracy while reducing required resources and simulation time by approximately 85 %. These findings demonstrate the sensor’s potential for diverse applications, particularly in the biomedical field.

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设计和分析用于医疗保健和生物医学应用的 KNN 行为预测高灵敏度激素传感器

本研究提出了一种先进的传感器系统，它将金元面与石墨烯集成在一起，通过折射率变化检测生殖激素。拟议的设计在太赫兹（THz）激励下运行，以利用激素的独特分子特征，并最大限度地减少潜在干扰。为了优化传感器的设计参数并评估其功效，我们进行了全面的数值分析和有限元法（FEM）模拟。优化后的传感器表现出卓越的性能指标，包括 375 GHzRIU-1 的峰值灵敏度、7.693 RIU-1 的优点系数 (FOM)、2.381 的品质因数 (Q) 和 0.556 RIU 的检测限 (LOD)。此外，该传感器还通过调制石墨烯的化学势展示了双位编码能力。K-Nearest Neighbors (KNN) 回归模型的集成提高了传感器的准确性，同时将所需资源和模拟时间减少了约 85%。这些研究结果表明，该传感器具有多种应用潜力，尤其是在生物医学领域。

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来源期刊

Measurement 工程技术-工程：综合

CiteScore

10.20

自引率

12.50%

发文量

1589

审稿时长

12.1 months

期刊介绍： Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.