数据驱动的指纹纳米机械质谱法
Data-driven fingerprint nanomechanical mass spectrometry
AI总结：
该研究提出一种数据驱动的指纹纳米机械质谱法，无需预先掌握NEMS器件振型及相关模型，利用吸附引发的振动频移序列作为指纹进行模式匹配，实现任意规格器件的颗粒与分子质量测量，为突破纳机电质谱检测限提供新路径。
摘要：
指纹分析是模式识别中普遍使用的工具，应用范围涵盖地理定位、DNA分析、人脸识别以及法医鉴定。其实用性的核心在于无需预先建立模式的数学模型即可实现准确识别。我们提出了一种面向纳机电系统质谱（NEMS-MS）的数据驱动指纹方法，可利用任意规格、复杂且未表征的纳机电器件实现颗粒与分子的质量测量。NEMS-MS基于分析物吸附引发的NEMS振动模式频移，频移序列构成了该吸附过程的指纹，可直接用于模式匹配。当前基于NEMS的质谱法有两个要求：（1）预先掌握或测量器件的振型；（2）建立基于振型的模型，将引发的模态频移与质量吸附关联起来。对于具有三维振型和纳米级特征的先进NEMS而言，这可能无法实现。本文报道的进展消除了这一障碍，从而允许采用任意规格和尺寸的器件设计。这使得具备复杂振动模式的先进NEMS器件得以应用，为实现纳机电质谱的终极检测限提供了前所未有的前景。
英文摘要：
Fingerprint analysis is a ubiquitous tool for pattern recognition with applications spanning from geolocation and DNA analysis to facial recognition and forensic identification. Central to its utility is the ability to provide accurate identification without an a priori mathematical model for the pattern. We report a data-driven fingerprint approach for nanoelectromechanical systems mass spectrometry (NEMS-MS) that enables mass measurements of particles and molecules using complex, uncharacterized nanoelectromechanical devices of arbitrary specification. NEMS-MS is based on the frequency shifts of the NEMS vibrational modes induced by analyte adsorption. The sequence of frequency shifts constitutes a fingerprint of this adsorption, which is directly amenable to pattern matching. Two current requirements of NEMS-based mass spectrometry are: (1) a priori knowledge or measurement of the device mode-shapes, and (2) a mode-shape-based model that connects the induced modal frequency shifts to mass adsorption. This may not be possible for advanced NEMS with three-dimensional mode-shapes and nanometer-sized features. The advance reported here eliminates this impediment, thereby allowing device designs of arbitrary specification and size to be employed. This enables the use of advanced NEMS devices with complex vibrational modes, which offer unprecedented prospects for attaining the ultimate detection limits of nanoelectromechanical mass spectrometry.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
