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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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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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Atom Probe Tomography Studies on the CuIn,GaSe2 Grain Boundaries
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Guided mass spectrum labelling in atom probe tomography.

D Haley1, P Choi2, D Raabe2

  • 1Max-Planck-Institut für Eisenforschung, Max-Plack Straße 1, Düsseldorf, Germany; Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, United Kingdom.

Ultramicroscopy
|March 21, 2015
PubMed
Summary
This summary is machine-generated.

Computer-guided ranging aids atom probe tomography (APT) by automating mass spectra analysis, reducing errors and operator variability. This technique enhances the accuracy and standardization of identifying peaks in complex APT datasets.

Keywords:
Atom ProbeData analysisStandardisation

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Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Nanotechnology

Background:

  • Atom probe tomography (APT) is a near-atomic resolution imaging technique producing mass spectrographic data.
  • Manual peak identification in APT mass spectra is subjective, prone to errors, and hinders standardization.
  • Operator variability in manual analysis contributes to scatter in reported composition data.

Purpose of the Study:

  • To explore computer-guided ranging for automated identification and analysis of APT mass spectra.
  • To develop a robust algorithm for enumerating and ranking potential peak identities.
  • To minimize identification errors and inter-operator variance in APT experiments.

Main Methods:

  • Development of a robust algorithm for enumerating possible identities of detected peak positions.
  • Implementation of a ranking scheme to evaluate the likelihood of each potential identity.
  • Creation of a complete work-chain for converting mass spectra to identified APT spectra.
  • Comparative experimental trials with different APT operators to assess computer-assisted vs. manual procedures.

Main Results:

  • Computer assistance showed little to no loss in precision, with occasional gains, compared to manual methods.
  • Inter-operator precision for ranging varied between 0 and 2 significant figures in composition reporting.
  • Intra-operator precision ranged from 1 to 3 significant figures, depending on species composition.

Conclusions:

  • A computer-guided work-chain effectively converts mass spectra to identified APT spectra, reducing errors.
  • Automated analysis minimizes inter-operator variance, improving the standardization and reliability of APT data.
  • Inconsistencies in manual peak labeling are identified as a major source of scatter in APT composition data.