Krstić Predrag, Ashcroft Brian, Lindsay Stuart
Institute for Advanced Computational Science, Stony Brook University, Stony Brook, NY 11794-5250, USA.
Nanotechnology. 2015 Feb 27;26(8):084001. doi: 10.1088/0957-4484/26/8/084001. Epub 2015 Feb 3.
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.
识别隧穿（RT）可识别被困在由识别分子功能化的隧穿电极之间的目标分子，这些识别分子充当金属电极与被困目标分子之间的特定化学连接。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，应用了全原子和粗粒度DNA模型：（1）我们表明观察到的电流大小与在溶剂化全原子模型上进行的非平衡格林函数计算结果一致。（2）氢键长度的布朗波动会导致电流尖峰，这与实验观察到的情况相似。（3）这些波动的频率特性可用于通过机器学习算法识别被困分子，为这种识别单分子信号的新方法提供理论基础。
