Department of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran.
Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, United Kingdom.
Sci Rep. 2020 Feb 25;10(1):3385. doi: 10.1038/s41598-020-60296-9.
Recently, detecting biological particles by analyzing their mechanical properties has attracted increasing attention. To detect and identify different bioparticles and estimate their dimensions, a mechanical nanosensor is introduced in this paper. To attract particles, numerous parts of the substrate are coated with different chemicals as probe detectors or receptors. The principal of cell recognition in this sensor is based on applying an electrical excitation and measuring the maximum deflection of the actuated cantilever electrode. Investigating the critical voltage that causes pull-in instability is also important in such highly-sensitive detectors. The governing equation of motion is derived from Hamilton's principle. A Galerkin approximation is applied to discretize the nonlinear equation, which is solved numerically. Accuracy of the proposed model is validated by comparison studies with available experimental and theoretical data. The coupled effects of geometrical and mechanical properties are included in this model and studied in detail. Moreover, system identification is carried out to distinguish bioparticles by a stability analysis. Due to the absence of a similar concept and device, this research is expected to advance the state-of-the-art biosystems in identifying particles.
最近，通过分析生物粒子的机械特性来检测生物粒子引起了越来越多的关注。为了检测和识别不同的生物粒子并估计它们的尺寸，本文引入了一种机械纳米传感器。为了吸引粒子，基底的许多部分都涂有不同的化学物质作为探针探测器或受体。传感器中细胞识别的原理基于施加电激励并测量致动悬臂电极的最大挠度。研究导致拉入不稳定性的临界电压在这种高灵敏度探测器中也很重要。运动的控制方程是从哈密顿原理推导出来的。应用 Galerkin 逼近将非线性方程离散化，并对其进行数值求解。通过与现有实验和理论数据的比较研究验证了所提出模型的准确性。该模型中包含了几何和机械特性的耦合效应，并进行了详细研究。此外，通过稳定性分析进行系统识别，以区分生物粒子。由于缺乏类似的概念和设备，这项研究有望推动生物系统在识别粒子方面的发展。
