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Physical model for recognition tunneling

机译:识别隧道的物理模型

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Recognition tunneling (RT) identifies target molecules trapped between tunneling electrodes functionalized with recognition molecules that serve as specific chemical linkages between the metal electrodes and the trapped target molecule. Possible applications include single molecule DNA and protein sequencing. This paper addresses several fundamental aspects of RT by multiscale theory, applying both all-atom and coarse-grained DNA models: (1) we show that the magnitude of the observed currents are consistent with the results of non-equilibrium Green's function calculations carried out on a solvated all-atom model. (2) Brownian fluctuations in hydrogen bond-lengths lead to current spikes that are similar to what is observed experimentally. (3) The frequency characteristics of these fluctuations can be used to identify the trapped molecules with a machine-learning algorithm, giving a theoretical underpinning to this new method of identifying single molecule signals.
机译:识别隧穿(RT)可以识别被识别分子功能化的隧穿电极之间捕获的靶分子,这些识别分子充当金属电极与被捕获靶分子之间的特定化学键。可能的应用包括单分子DNA和蛋白质测序。本文通过多尺度理论探讨了RT的几个基本方面,同时应用了全原子模型和粗粒DNA模型:(1)我们证明观察到的电流大小与进行的非平衡格林函数计算结果一致在溶剂化的全原子模型上(2)氢键长度的布朗波动导致电流尖峰类似于实验观察到的峰值。 (3)这些波动的频率特性可用于通过机器学习算法来识别被困分子,从而为这种识别单分子信号的新方法提供了理论基础。

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