College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China.
Sensors (Basel). 2024 Mar 20;24(6):1989. doi: 10.3390/s24061989.
Lateral flow immunoassay (LFIA) is extensively utilized for point-of-care testing due to its ease of operation, cost-effectiveness, and swift results. This study investigates the flow dynamics and reaction mechanisms in LFIA by developing a three-dimensional model using the Richards equation and porous media transport, and employing numerical simulations through the finite element method. The study delves into the transport and diffusion behaviors of each reaction component in both sandwich LFIA and competitive LFIA under non-uniform flow conditions. Additionally, the impact of various parameters (such as reporter particle concentration, initial capture probe concentrations for the T-line and C-line, and reaction rate constants) on LFIA performance is analyzed. The findings reveal that, in sandwich LFIA, optimizing parameters like increasing reporter particle concentration and initial capture probe concentration for the T-line, as well as adjusting reaction rate constants, can effectively enhance detection sensitivity and broaden the working range. Conversely, in competitive LFIA, the effects are inverse. This model offers valuable insights for the design and enhancement of LFIA assays.
侧向流动免疫分析（LFIA）因其操作简便、经济高效和快速结果而被广泛应用于即时检测。本研究通过使用 Richards 方程和多孔介质传输开发了一个三维模型，并通过有限元方法进行数值模拟，研究了 LFIA 中的流动动力学和反应机制。该研究深入探讨了在非均匀流动条件下夹心 LFIA 和竞争 LFIA 中每个反应组分的传输和扩散行为。此外，还分析了各种参数（如报告粒子浓度、T 线和 C 线的初始捕获探针浓度以及反应速率常数）对 LFIA 性能的影响。研究结果表明，在夹心 LFIA 中，通过优化报告粒子浓度和 T 线初始捕获探针浓度等参数，以及调整反应速率常数，可以有效提高检测灵敏度并拓宽工作范围。相反，在竞争 LFIA 中，效果则相反。该模型为 LFIA 分析的设计和增强提供了有价值的见解。
