Study of 3D composition in a nanoscale sample using data-constrained modelling and multi-energy x-ray CT

A. Trinchi, Y. S. Yang, J. Z. Huang, P. Falcaro, D. Buso, L. Q. Cao

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Submicrometre x-ray computed tomography (CT), referred to as x-ray nanotomography, is a research area attracting much attention nowadays. The major limiting factors in observing compositional structures and features below the micrometre scale are signal-to-noise ratio and loss of information below the x-ray detector pixel size. Conventional image segmentation techniques, such as image thresholding, are not usually sufficient for accurately resolving such microscopic compositional distributions. In this work we carried out multi-energy x-ray CT simulations on a computer generated sample of nanoporous alumina with gold nanoparticles incorporated inside some of the pores. The multi-energy CT data sets served as inputs to our in-house developed data-constrained microstructure (DCM) modelling software, which can accurately predict the 3D chemical composition of a sample from CT data. Different levels of x-ray detector noise were also added to the CT simulations and the DCM chemical phase predictions were analysed under these conditions. The pixel resolution was 15 nm, and the x-ray projection images were re-sampled to lower resolutions to simulate the effect of features smaller than the CT pixel resolution. We found that despite the simulated sample having constituents that possess vastly different x-ray absorption properties, with one constituent having a total linear absorption coefficient up to two orders of magnitude greater than the other, the DCM showed a clear advantage over image thresholding, particularly in the presence of noise.

Original languageEnglish
Article number015013
JournalModelling and simulation in materials science and engineering
Volume20
Issue number1
DOIs
Publication statusPublished - Jan 2012
Externally publishedYes

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ASJC Scopus subject areas

  • Modelling and Simulation
  • Condensed Matter Physics
  • Materials Science(all)
  • Mechanics of Materials
  • Computer Science Applications

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