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An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a soft-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.To...
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Related Experiment Video

Updated: Dec 21, 2025

Atom Probe Tomography Studies on the CuIn,GaSe2 Grain Boundaries
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Bayesian approach to automatic mass-spectrum peak identification in atom probe tomography.

A Mikhalychev1, S Vlasenko1, T R Payne2

  • 1Atomicus OOO, Mogilyovskaya Str. 39a-215, 220007 Minsk, Belarus.

Ultramicroscopy
|May 18, 2020
PubMed
Summary

We developed a Bayesian method to automatically identify mass-spectrum peaks for atom-probe tomography. This approach improves accuracy and efficiency in reconstructing sample models from complex mass spectra.

Keywords:
Artificial intelligenceAtom probe tomographyAutomatic peak identificationBayesian approachFisher informationPeak labeling

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Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Computational Methods

Background:

  • Manual identification of mass-spectrum peaks in atom-probe tomography is time-consuming and error-prone.
  • Accurate peak identification is crucial for reliable reconstruction of atomic-scale sample models.

Purpose of the Study:

  • To develop an automated and robust Bayesian approach for mass-spectrum peak identification.
  • To enhance the efficiency and accuracy of atom-probe tomography reconstruction.

Main Methods:

  • A Bayesian framework for ranking candidate ions based on posterior probabilities.
  • Iterative reconstruction of sample models incorporating prior information and error models.
  • Development of a 'sliding window' method using Fisher information for peak decomposition.

Main Results:

  • The Bayesian approach reliably identifies mass-spectrum peaks in inorganic samples.
  • Reconstructed sample models are consistent with manual analysis results.
  • The 'sliding window' method provides accurate and efficient peak decomposition.

Conclusions:

  • The proposed Bayesian method offers a significant improvement over manual peak identification in atom-probe tomography.
  • This automated approach enhances the reliability and efficiency of materials characterization.
  • The developed techniques are applicable to time-of-flight mass spectrometry data analysis.