4.6 Article

Terahertz Women Reproductive Hormones Sensor Using Photonic Crystal Fiber With Behavior Prediction Using Machine Learning

期刊

IEEE ACCESS
卷 11, 期 -, 页码 75424-75433

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2023.3277955

关键词

Hollow core PCF; biosensor; progesterone; estradiol; relative sensitivity; locally weighted linear regression

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In this article, a rectangular hollow core Photonic Crystal Fiber (PCF) sensor is proposed for highly sensitive detection of women's reproductive hormones (progesterone and estradiol) in blood samples at the THz regime. The numerical analysis shows improved relative sensitivity and minimal loss by optimizing the sensor's structural parameters. The sensor exhibits a maximum relative sensitivity of 99.87% for 10 n mol/L (progesterone) and 99.88% for 3 n mol/L (estradiol) at 1.35 THz. It also demonstrates low effective material loss and a large effective mode area. The research highlights the importance of monitoring estradiol and progesterone levels for women's reproductive health and offers a feasible fabrication method.
In this article, we propose a rectangular hollow core Photonic Crystal Fiber (PCF) sensor for high detection of women's reproductive hormones (progesterone and estradiol) in the blood sample at THz regime (0.8 THz to 1.7 THz). The numerical sensing performances are evaluated by the full vector finite element method (FVFEM). We have achieved improved relative sensitivity with minimal loss for sensing progesterone concentrations and estradiol concentrations by optimizing structural parameters. The obtained maximum relative sensitivity is 99.87% for 10 n mol/L (progesterone) and 99.88% for 3 n mol/L (Estradiol) at 1.35 THz. Further, we have obtained low effective material loss (EML) and a large effective mode area. The primary takeaway from this research is that it's critical to monitor estradiol and progesterone levels in order to ensure that a woman's reproductive system is functioning in a balanced manner and in general health systems. Also, this biosensor can be fabricated by current fabrication technologies. Moreover, the prediction carried out with the help of Locally Weighted Linear Regression, and hyperparameter tuning, we can conclude that for weighting hyperparameter value of 0.02. We have achieved the maximum prediction accuracy with the unity R2 score and this model can be employed for the prediction of relative sensitivity for various parameters.

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