Robust Automatic EXAFS First-Shell Fits

Area of Science:

• Materials Science
• Chemistry
• Physics

Background:

• Extended X-ray Absorption Fine Structure (EXAFS) is crucial for atomic structure determination.
• Accurate selection of the k-range for Fourier transformation is critical for reliable EXAFS analysis, particularly for first-shell fitting.
• Current methods for determining the k-range can be challenging and subjective.

Purpose of the Study:

• To develop an automated method for determining the optimal k-range for EXAFS data analysis.
• To improve the accuracy and reproducibility of first-shell EXAFS fitting, especially for noisy or diluted samples.
• To provide a robust approach for EXAFS data processing using the Larch package and Python.

Main Methods:

• An automated Python-based method using the Larch package was developed to determine the k-range.
• The method involves estimating spectral noise to find the optimal maximum k-value (k_max) based on an empirical noise threshold.
• The minimum k-value (k_min) is then determined by optimizing the background function and minimizing the R-factor for first-shell fitting.

Main Results:

• The automated method successfully identified suitable k-ranges for Fourier transformation across various datasets.
• Accurate first-shell EXAFS fits were achieved using the determined k-ranges.
• The approach demonstrated robustness, particularly for noisy data from diluted samples.

Conclusions:

• The developed automated method provides a reliable and reproducible way to determine k-ranges for EXAFS analysis.
• This technique enhances the accuracy of atomic structure determination and first-shell fitting.
• The method facilitates robust EXAFS data analysis, reducing reliance on subjective parameter selection.