Data-driven fingerprint nanoelectromechanical mass spectrometry

Creators: Sader, John E.¹; Gomez, Alfredo^{1, 2}; Neumann, Adam P.¹; Nunn, Alex¹; Roukes, Michael L.¹

1. California Institute of Technology
2. Carnegie Mellon University

Abstract

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 that enables mass measurements of particles and molecules using complex, uncharacterized nanoelectromechanical devices of arbitrary specification. Nanoelectromechanical systems mass spectrometry is based on the frequency shifts of the nanoelectromechanical device vibrational modes that are 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 nanoelectromechanical-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 nanoelectromechanical devices 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 nanoelectromechanical devices with complex vibrational modes, which offer unprecedented prospects for attaining the ultimate detection limits of nanoelectromechanical mass spectrometry.

Copyright and License

This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material.

Acknowledgement

The authors thank J. F. Collis and S. R. Manalis for useful discussions. The authors gratefully acknowledge support from the Wellcome Leap Foundation through its Delta Tissue program.

Data Availability

The datasets generated during the current study are available via Zenodo using the identifier; https://doi.org/10.5281/zenodo.13352347.

Code Availability

The code used to generate the findings of this study are available via Zenodo using the identifiers; https://doi.org/10.5281/zenodo.10211934, https://doi.org/10.5281/zenodo.10211936.

Contributions

J.E.S. proposed the fingerprint method for NEMS mass spectrometry and A.G. introduced the most parallel-vector approach. A.P.N. developed the noise thresholding methodology and A.N. contributed to the variance calculation of the positional discrepancy. J.E.S. performed the mass uncertainty calculation, analyzed the SMR data and wrote the paper. M.L.R. supervised the project and provided its overall direction. All authors contributed to the data analysis and ensuing discussions regarding its interpretation, as well as editing of the manuscript.

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