Abstract
The application of spectrum imaging multivariate statistical analysis methods, specifically principal component analysis (PCA), to atom probe tomography (APT) data has been investigated. The mathematical method of analysis is described and the results for two example datasets are analyzed and presented. The first dataset is from the analysis of a PM 2000 Fe-Cr-Al-Ti steel containing two different ultrafine precipitate populations. PCA properly describes the matrix and precipitate phases in a simple and intuitive manner. A second APT example is from the analysis of an irradiated reactor pressure vessel steel. Fine, nm-scale Cu-enriched precipitates having a core-shell structure were identified and qualitatively described by PCA. Advantages, disadvantages, and future prospects for implementing these data analysis methodologies for APT datasets, particularly with regard to quantitative analysis, are also discussed.
| Original language | English |
|---|---|
| Pages (from-to) | 1362-1373 |
| Number of pages | 12 |
| Journal | Ultramicroscopy |
| Volume | 110 |
| Issue number | 11 |
| DOIs | |
| State | Published - Oct 2010 |
Funding
This research was conducted as part of the Shared Research Equipment (SHaRE) User Program, which is sponsored at Oak Ridge National Laboratory (ORNL) by the Division of Scientific User Facilities, U.S. Department of Energy. CMP sponsored by Laboratory Directed Research and Development Weinberg Fellows Program at ORNL, which is managed by UT-Battelle, LLC, for the U.S. Department of Energy. Thank to Dr. C. Capdevila, Centro Nacional de Investigaciones Metalúrgicas, Spain, for the PM2000™ material. PM 2000 is a trademark of Plansee.
| Funders | Funder number |
|---|---|
| Division of Scientific User Facilities | |
| U.S. Department of Energy | |
| Oak Ridge National Laboratory | |
| Laboratory Directed Research and Development |
Keywords
- Atom probe
- Multivariate statistical analysis
- Principal component analysis
- Steel