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Related Experiment Videos

Efficient sampling for three-dimensional atom probe microscopy data.

Olof C Hellman1, John Blatz du Rivage, David N Seidman

  • 1Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208-3108, USA.

Ultramicroscopy
|January 22, 2003
PubMed
Summary
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Accurate analysis of atom-probe microscopy data requires balancing spatial positioning and statistical errors. A novel double-splat algorithm efficiently optimizes this balance for concentration profile calculations.

Area of Science:

  • Materials Science
  • Data Analysis
  • Microscopy

Background:

  • Three-dimensional atom-probe microscopy (3D APM) data analysis involves calculating concentration profiles, isoconcentration surfaces, and Gibbsian interfacial excesses.
  • Optimizing these calculations necessitates a trade-off between spatial positioning error and statistical sampling error.
  • The complexity increases with the 3D nature of data and spatially varying statistical errors.

Purpose of the Study:

  • To present strategies and efficient algorithms for optimizing spatial sampling in 3D APM data analysis.
  • To address the challenge of balancing spatial positioning and statistical errors in concentration profile calculations.
  • To introduce a novel double-splat algorithm for flexible spatial sampling.

Main Methods:

Related Experiment Videos

  • Development of a unique double-splat algorithm for data processing.
  • Initial fine-grained sampling to convert raw data to a regular grid.
  • A second, variable-width splat applied to the regular grid for adjustable spatial sampling, mimicking Gaussian spreading without direct coefficient calculation.

    • Main Results:

    • The double-splat algorithm allows for efficient generation of various spatial samplings from 3D APM data.
    • This method provides flexibility in adjusting the sampling distance to achieve the optimal balance between statistical and positioning errors.
    • Demonstration of isoconcentration surfaces calculated under different sampling conditions using the same dataset.

    Conclusions:

    • The proposed double-splat algorithm offers an efficient solution for optimizing spatial sampling in 3D APM data analysis.
    • This approach facilitates improved accuracy in determining concentration profiles and interfacial properties.
    • The algorithm provides a practical method for researchers to navigate the complexities of 3D APM data interpretation.