检测空气传播病原体:一种利用表面声波传感器进行微生物检测的计算方法

Sharon P. Varughese, S. Merlin Gilbert Raj, T. Jesse Joel, Sneha Gautam
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引用次数: 0

摘要

传染性病原体构成的持续威胁仍然是人类面临的巨大挑战。由空气中微生物引起的传染病迅速蔓延,对全球造成深远影响,给社会造成巨大代价。虽然出现了各种检测技术,包括生化、免疫学和分子方法,但这些方法仍然存在明显的局限性,如耗时、不稳定性和对专业操作人员的需求。这项研究提出了一种创新的解决方案,利用表面声波(SAW)传感器的潜力来检测空气中的微生物。该研究涉及在COMSOL Multiphysics框架内建立传感器模型,利用预定义的压电多物理场接口并采用二维建模方法。壳聚糖被选为模型的传感膜,它与铌酸锂(LiNbO3)界面,这是一种被选的压电材料,负责检测空气中的病原体。微生物存在的分析集中在固体位移和电位频率上,在850-900 MHz范围内工作。值得注意的是,第一和第二谐振频率分别被识别为856和859 MHz。为了加深理解,本研究提出了一个基于斯托克斯定律和质量平衡方程的新的数学模型。该模型用于分析微生物浓度,为量化空气传播病原体的存在提供了新的视角。通过这些努力,本研究有助于推动空气微生物检测领域的发展,为解决传染病带来的挑战提供了一条有希望的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting Airborne Pathogens: A Computational Approach Utilizing Surface Acoustic Wave Sensors for Microorganism Detection
The persistent threat posed by infectious pathogens remains a formidable challenge for humanity. Rapidly spreading infectious diseases caused by airborne microorganisms have far-reaching global consequences, imposing substantial costs on society. While various detection technologies have emerged, including biochemical, immunological, and molecular approaches, these methods still exhibit significant limitations such as time-intensive procedures, instability, and the need for specialized operators. This study presents an innovative solution that harnesses the potential of surface acoustic wave (SAW) sensors for the detection of airborne microorganisms. The research involves the establishment of a sensor model within the framework of COMSOL Multiphysics, utilizing a predefined piezoelectric multi-physics interface and employing a 2D modeling approach. Chitosan, selected as the sensing film for the model, interfaces with lithium niobate (LiNbO3), the chosen piezoelectric material responsible for detecting airborne pathogens. The analysis of microbe presence centers on solid displacement and electric potential frequencies, operating within the 850–900 MHz range. Notably, the first and second resonant frequencies are identified at 856 and 859 MHz, respectively. To enhance understanding, this study proposes a novel mathematical model grounded in Stokes’ Law and mass balance equations. This model serves to analyze microbe concentration, offering a fresh perspective on quantifying the presence of airborne pathogens. Through these endeavors, this research contributes to advancing the field of airborne microorganism detection, offering a promising avenue for addressing the challenges posed by infectious diseases.
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