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Published February 2024 | Version v1
Journal Article Open

Nanomechanical mass measurements through feature-based time series clustering

Creators

  • Neumann, Adam P. ORCID icon
  • Gomez, Alfredo ORCID icon
  • Nunn, Alexander R.
  • Sader, John E.1 ORCID icon
  • Roukes, Michael L.1 ORCID icon
  • 1. ROR icon California Institute of Technology

Abstract

Recent years have seen explosive growth in miniaturized sensors that can continuously monitor a wide variety of processes, with applications in healthcare, manufacturing, and environmental sensing. The time series generated by these sensors often involves abrupt jumps in the detected signal. One such application uses nanoelectromechanical systems (NEMS) for mass spectrometry, where analyte adsorption produces a quick but finite-time jump in the resonance frequencies of the sensor eigenmodes. This finite-time response can lead to ambiguity in the detection of adsorption events, particularly in high event-rate mass adsorption. Here, we develop a computational algorithm that robustly eliminates this often-encountered ambiguity. A moving-window statistical test together with a feature-based clustering algorithm is proposed to automate the identification of single-event jumps. We validate the method using numerical simulations and demonstrate its application in practice using time-series data that are experimentally generated by molecules adsorbing onto NEMS sensors at a high event rate. This computational algorithm enables new applications, including high-throughput, single-molecule proteomics.

Copyright and License

© 2024 Author(s). Published under an exclusive license by AIP Publishing.

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Additional details

Identifiers

ISSN
1089-7623

Funding

Wellcome Leap Foundation
Thermo Fisher Scientific (United States)
National Science Foundation
2016555
National Science Foundation
1828787

Dates

Accepted
2024-02-01
published print
Accepted
2024-02-06
published online

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Keywords and subjects

Keywords

  • Instrumentation

Details

DOI
10.1063/5.0176303
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DOI

10.1063/5.0176303

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Resource type
Journal Article
Publisher
AIP Publishing
Published in
Review of Scientific Instruments, 95(2), ISSN: 0034-6748, 2024.
Languages
English

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  • No commercial reproduction, distribution, display or performance rights in this work are provided.
    No further description.

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Created
February 21, 2024
Modified
February 1, 2025
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