Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.
Physics Department, Faculty of Science, Beni-Suef University, Beni-Suef 62512, Egypt.
Biosensors (Basel). 2023 Jun 27;13(7):683. doi: 10.3390/bios13070683.
This research presented a comprehensive study of a one-dimensional (1D) porous silicon phononic crystal design as a novel fluidic sensor. The proposed sensor is designed to detect sulfuric acid (HSO) within a narrow concentration range of 0-15%. Sulfuric acid is a mineral acid extensively utilized in various physical, chemical, and industrial applications. Undoubtedly, its concentration, particularly at lower levels, plays a pivotal role in these applications. Hence, there is an urgent demand for a highly accurate and sensitive tool to monitor even the slightest changes in its concentration, which is crucial for researchers. Herein, we presented a novel study on the optimization of the phononic crystal (PnC) sensor. The optimization process involves a comparative strategy between binary and ternary PnCs, utilizing a multilayer stack comprising 1D porous silicon (PSi) layers. Additionally, a second comparison is conducted between conventional Bragg and local resonant PnCs to demonstrate the design with the highest sensitivity. Moreover, we determine the optimum values for the materials' thickness and number of periods. The results revealed that the ternary local resonant PnC design with the configuration of {silicone rubber/[PSi1/PSi2/PSi3]/silicone rubber} is the optimal sensor design. The sensor provided a super sensitivity of 2.30 × 10 Hz for a concentration change of just 2%. This exceptional sensitivity is attributed to the presence of local resonant modes within the band gap of PnCs. The temperature effects on the local resonant modes and sensor performance have also been considered. Furthermore, additional sensor performance parameters such as quality factor, figure of merit, detection limit, and damping rate have been calculated to demonstrate the effectiveness of the proposed liquid sensor. The transfer matrix method was utilized to compute the transmission spectra of the PnC, and Hashin's expression was employed to manipulate the porous silicon media filled with sulfuric acid at various concentrations. Lastly, the proposed sensor can serve as an efficient tool for detecting acidic rain, contaminating freshwater, and assessing food and liquid quality, as well as monitoring other pharmaceutical products.
本研究提出了一种一维（1D）多孔硅声子晶体设计作为新型流体制感的综合研究。所提出的传感器旨在检测浓度范围在 0-15%的范围内的硫酸（HSO）。硫酸是一种在各种物理、化学和工业应用中广泛使用的矿物酸。毫无疑问，其浓度，特别是在较低水平时，在这些应用中起着关键作用。因此，迫切需要一种高度精确和敏感的工具来监测其浓度的哪怕最微小的变化，这对研究人员来说至关重要。在这里，我们提出了一种新型的声子晶体（PnC）传感器优化研究。优化过程涉及二进制和三元 PnC 之间的比较策略，利用包括一维多孔硅（PSi）层的多层堆叠。此外，还对常规布拉格和局域共振 PnC 进行了第二次比较，以展示具有最高灵敏度的设计。此外，我们确定了材料厚度和周期数的最佳值。结果表明，具有 {硅橡胶/[PSi1/PSi2/PSi3]/硅橡胶} 配置的三元局域共振 PnC 设计是最佳的传感器设计。传感器在浓度仅变化 2%的情况下提供了 2.30×10^-2 Hz 的超高灵敏度。这种出色的灵敏度归因于 PnCs 带隙内局域共振模式的存在。还考虑了温度对局域共振模式和传感器性能的影响。此外，还计算了传感器的附加性能参数，如品质因数、优值、检测限和阻尼率，以证明所提出的液体传感器的有效性。使用传递矩阵方法计算 PnC 的传输谱，并使用 Hashin 表达式来处理填充有不同浓度硫酸的多孔硅介质。最后，所提出的传感器可以作为检测酸雨、污染淡水、评估食品和液体质量以及监测其他药物产品的有效工具。
