Total variation denoising-based method of identifying the states of single molecules in break junction data.

0 MATERIALS SCIENCE, MULTIDISCIPLINARY Discover nano Pub Date : 2024-01-29 DOI:10.1186/s11671-024-03963-4
Yuki Komoto, Jiho Ryu, Masateru Taniguchi
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Abstract

Break junction (BJ) measurements provide insights into the electrical properties of diverse molecules, enabling the direct assessment of single-molecule conductances. The BJ method displays potential for use in determining the dynamics of individual molecules, single-molecule chemical reactions, and biomolecules, such as deoxyribonucleic acid and ribonucleic acid. However, conductance data obtained via single-molecule measurements may be susceptible to fluctuations due to minute structural changes within the junctions. Consequently, clearly identifying the conduction states of these molecules is challenging. This study aims to develop a method of precisely identifying conduction state traces. We propose a novel single-molecule analysis approach that employs total variation denoising (TVD) in signal processing, focusing on the integration of information technology with measured single-molecule data. We successfully applied this method to simulated conductance traces, effectively denoise the data, and elucidate multiple conduction states. The proposed method facilitates the identification of well-defined plateau lengths and supervised machine learning with enhanced accuracies. The introduced TVD-based analytical method is effective in elucidating the states within the measured single-molecule data. This approach exhibits the potential to offer novel perspectives regarding the formation of molecular junctions, conformational changes, and cleavage.

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基于总变异去噪的断裂结数据单分子状态识别方法。
断裂交界(BJ)测量可深入了解各种分子的电特性,从而对单分子电导进行直接评估。BJ 方法在确定单个分子、单分子化学反应和生物大分子(如脱氧核糖核酸和核糖核酸)的动态方面具有潜力。不过,通过单分子测量获得的电导数据可能容易受到连接点内部微小结构变化引起的波动的影响。因此,要清楚地识别这些分子的传导状态具有挑战性。本研究旨在开发一种精确识别传导状态轨迹的方法。我们提出了一种新颖的单分子分析方法,在信号处理中采用了总变异去噪(TVD)技术,重点是将信息技术与测得的单分子数据相结合。我们成功地将这种方法应用于模拟传导迹线,有效地对数据进行了去噪处理,并阐明了多种传导状态。所提出的方法有助于识别定义明确的高原长度和监督机器学习,并提高了准确性。引入的基于 TVD 的分析方法能有效地阐明单分子测量数据中的状态。这种方法有望为分子连接的形成、构象变化和裂解提供新的视角。
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