Bayesian inversion for nanowire field-effect sensors
Bayesian inversion for nanowire field-effect sensors
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DOI：
10.1007/s10825-019-01417-0
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发表时间：
2019-11-13
影响因子：
2.1
通讯作者：
Heitzinger, Clemens
中科院分区：
文献类型：
--
作者：
Khodadadian, Amirreza;Stadlbauer, Benjamin;Heitzinger, Clemens
纳米线场效应传感器最近被开发出来用于生物分子的无标记检测。在这项工作中，我们引入了一种基于贝叶斯估计的计算技术来确定传感器的物理参数，更重要的是，确定了分析物分子的性质。为此，我们首先提出了一个基于偏微分方程的模型来模拟器件的电荷传输和电化学行为。然后，采用带延迟拒绝的自适应Metropolis算法估计未知参数的后验分布，即分子电荷密度、分子密度、掺杂浓度、电子和空穴迁移率。我们同时确定了器件和分子的性质，在确定了器件参数后，我们还计算了分子密度作为唯一的参数。这种方法不仅使确定未知参数成为可能，而且还表明通过产生概率密度函数(Pdf)可以很好地确定每个参数。
Nanowire field-effect sensors have recently been developed for label-free detection of biomolecules. In this work, we introduce a computational technique based on Bayesian estimation to determine the physical parameters of the sensor and, more importantly, the properties of the analyte molecules. To that end, we first propose a PDE-based model to simulate the device charge transport and electrochemical behavior. Then, the adaptive Metropolis algorithm with delayed rejection is applied to estimate the posterior distribution of unknown parameters, namely molecule charge density, molecule density, doping concentration, and electron and hole mobilities. We determine the device and molecules properties simultaneously, and we also calculate the molecule density as the only parameter after having determined the device parameters. This approach makes it possible not only to determine unknown parameters, but it also shows how well each parameter can be determined by yielding the probability density function (pdf).