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在SERS热点的动态电化学重定位使得DNA寡核酸的组成,长度和序列读取成为可能

Cam Tu Tran¹, Lam Bang Thanh Nguyen¹, Emily Xi Tan^1,2

¹School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 21 Nanyang Link, Singapore, Singapore 637371.

Journal of the American Chemical Society

|March 14, 2026

概括

这项研究引入了一种电化学方法,可以在表面上动态重定位DNA,克服了表面增强拉曼光谱 (SERS) 对全面分子分析的局限性. 这种技术能够准确预测DNA的组成,长度和序列.

科学领域:

分析化学分析化学
频谱学是一种光谱学.
纳米技术纳米技术

背景情况:

表面增强拉曼光谱 (SERS) 在分析像DNA这样的大分子方面面临挑战,原因是"表面选择规则",其中只检测到接近表面的分子.
这导致不完整或偏差的光谱数据,妨碍提取关键的序列级信息,如长度和基础组成.
目前的SERS方法在DNA表征方面存在困难,限制了需要详细分子结构信息的应用.

研究的目的:

开发一种新的电化学调制策略,用于在等离子热点附近的DNA寡核酸的动态重定位.
为了克服SERS中"表面选择规则"的局限性,用于DNA的综合分子分析.
通过使用SERS超级配置文件和机器学习,实现DNA组成,长度和序列的准确预测.

主要方法:

开发了一种电化学调制策略,通过调整基质表面潜力来动态重定位吸收的DNA寡核酸.
通过组合通过DNA基的顺序重定位获得的特定配置光谱来生成SERS"超级配置".
集成的SERS超级配置文件,具有逐步机器学习 (ML) 框架,用于预测寡核酸特性.

主要成果:

通过动态重定位实现了对寡核酸组成,长度和序列的全面解码.
通过使用SERS超级配置文件,证明了44种不同的寡核酸的高分类准确性 (98.4%).
通过ML集成,能够准确地预测未见的寡核酸,基组成 (3.4%),长度 (0.9个基) 的误差率低,主要序列的准确性为100%.

结论:

电化学重定位策略有效地克服了静态SERS固有的方向和表面选择偏差.
这种动态的SERS方法使未知的DNA寡核酸的可靠结构预测成为可能.
该方法通过扩大分析剂-表面配置,在各种领域的分子表征中具有广泛应用的潜力.

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