Modeling of High-Performance Fiber Optic SPR Sensor for Colorectal Cancer Detection Designed Using Amorphous Silicon and TiO2 Layers Under Optimum Radiation Damping

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Adarsh Chandra Mishra;D. K. Dwivedi;Anuj K. Sharma;Pooja Lohia;Baljinder Kaur
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引用次数: 0

Abstract

The performance of fused silica core and perfluorinated (PF) polymer clad-based fiber optic sensor is simulated and investigated in near-infrared (NIR) region under optimum radiation damping (ORD) condition of plasmonic excitation. The sensor structure consists of an amorphous silicon (a-Si) layer over the polymer clad as a high-refractive index (RI) substrate for Ag-titanium dioxide (TiO2) heterojunction. Normal and pathological (cancerous) colorectal tissues have been considered as analytes. By keeping the thickness of the a-Si layer (100 nm) constant, the figure of merit (FOM) of the sensor is optimized by judiciously coordinating the thicknesses of Ag layer ( ${d}_{\text {Ag}}$ ) in the range of 30–60 nm and TiO2 layer ( ${d}_{\text {TiO2}}$ ) in the range of 0–10 nm at 1000-nm NIR wavelength using 2-D simulation under principal ORD condition. An optimized FOM as high as 12 810 RIU $^{-{1}}$ is achieved for ${d}_{\text {Ag}} =40.4$ nm and ${d}_{\text {TiO2}} =8.6$ nm. Moreover, there appear more such combinations (called secondary ORD) leading to slightly smaller FOM. Furthermore, the effect of coordinated variation of wavelength and ${d}_{\text {TiO2}}$ on the sensor’s FOM is also analyzed for further optimization. The power loss (PL) ratio and field enhancement factor are also calculated for optimized thicknesses of Ag and TiO2 layers. Finally, the combined FOM (CFOM) for the proposed sensor is $128073.09~\mu $ m4/RIU, which is substantially higher than the existing FOSPR sensors to the best of the authors’ knowledge. The introduction of the bovine serum albumin (BSA) layer improves the selectivity and prevents cross-sensitivity, however, with a slight decrease in FOM and sensitivity. The findings are crucial for the development of high-performance plasmonic sensors in NIR.
在最佳辐射阻尼条件下使用非晶硅和 TiO2 层设计的用于大肠癌检测的高性能光纤 SPR 传感器建模
在等离子体激发的最佳辐射阻尼(ORD)条件下,模拟并研究了基于熔融石英芯和全氟(PF)聚合物包层的光纤传感器在近红外(NIR)区域的性能。传感器结构包括聚合物包层上的非晶硅(a-Si)层,作为二氧化钛(TiO2)异质结的高折射率(RI)基底。正常和病理(癌症)结直肠组织被视为分析对象。在保持非晶硅层厚度(100 nm)不变的情况下,利用主ORD条件下的二维模拟,在1000 nm近红外波长下,通过合理协调30-60 nm范围内的银层厚度(${d}_{text {Ag}}$)和0-10 nm范围内的二氧化钛层厚度(${d}_{text\{TiO2}}$),优化了传感器的优点系数(FOM)。当 ${d}_{text {Ag}} =40.4$ nm 和 ${d}_{text {TiO2}} =8.6$ nm 时,优化的 FOM 高达 12 810 RIU $^{-{1}}$。此外,还出现了更多这样的组合(称为次级 ORD),导致 FOM 稍微变小。此外,还分析了波长和 ${d}_{\text {TiO2}}$ 的协调变化对传感器 FOM 的影响,以便进一步优化。此外,还计算了优化 Ag 层和 TiO2 层厚度后的功率损耗(PL)比和场增强因子。最后,据作者所知,拟议传感器的综合 FOM(CFOM)为 128073.09~\mu $ m4/RIU,大大高于现有的 FOSPR 传感器。牛血清白蛋白(BSA)层的引入提高了选择性并防止了交叉敏感,但 FOM 和灵敏度略有下降。这些发现对于开发近红外高性能等离子体传感器至关重要。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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