Global Least Squares Algorithm for 3D Elemental Surface Topography Reconstruction Using Quad-Segment Detector in...

Area of Science:

• Materials Science
• Analytical Chemistry
• Geophysics

Background:

• 2D elemental imaging techniques like micro-X-ray fluorescence (micro-XRF) and micro-particle-induced X-ray emission (micro-PIXE) are vital for elemental mapping.
• Surface irregularities in samples can cause shadow effects, compromising the accuracy of spectrometric analysis.
• Accurate surface topography information is crucial for correcting these distortions.

Purpose of the Study:

• To develop and demonstrate a method for reconstructing 3D surface topography using micro-PIXE data.
• To address the limitations of 2D elemental imaging caused by surface topography.
• To enhance the accuracy of elemental distribution analysis in various scientific fields.

Main Methods:

• Integration of a global least squares algorithm for 3D surface topography reconstruction.
• Utilizing two independent, noise-distorted gradient components from X-ray data.
• Acquisition of X-ray data using an annular quad-segment spectrometer.

Main Results:

• Successful reconstruction of 3D elemental surface topography on a homogeneous sample.
• Demonstration of a noniterative code for surface reconstruction.
• Validation of the algorithm's applicability to micro-PIXE and similar X-ray microscopy techniques.

Conclusions:

• The proposed method effectively reconstructs 3D surface topography from micro-PIXE data.
• This approach can enhance the correction of spatial elemental distributions, especially for heterogeneous samples.
• The technique offers a valuable tool for improving elemental mapping accuracy in diverse scientific applications.