Hi All, I have some questions regarding different aspects of the peak-fitting process in Larix. I wish to repeat the process reported in similar studies, but I'm facing challenges in adapting it effectively to my dataset. Farges 2005: 'One
ZjQcmQRYFpfptBannerStart
This Message Is From an External Sender
This message came from outside your organization.
 
ZjQcmQRYFpfptBannerEnd

Hi All,

I have some questions regarding different aspects of the peak-fitting process in Larix. I wish to repeat the process reported in similar studies, but I'm facing challenges in adapting it effectively to my dataset.

 

Using Mn2+ as an example, after normalisation, the baseline is fitted within the pre-peak region, followed by fitting an arctangent background function to the data. The parameters for the arctangent function are initially estimated using the 'pick values from plot' feature. However, modifying these parameters doesn't result in corresponding changes in the graph, making it difficult to ascertain if it aligns with the data points. Considering this, would it be acceptable to set the arctangent's amplitude to 1 (normalised edge jump) and position its centre a couple of eV below E0?

Following this, two pseudo-Voigt functions are introduced, with their parameters initially estimated. Then, to replicate the conditions of '1.3 eV 2ó width and 45% Gaussian,' do I set the pseud_fraction to 0.45 and pseud_sigma to 2? I'm uncertain about where to input the 1.3 eV width and whether this choice is optimal, especially considering that the natural width of the atomic K level at the Mn edge is 1.16 eV (Krause, 1979).

Finally, I couldn't find the specific paper, but the authors stated that due to the significant processing times required for Voigt functions, they opted for pseudo-Voigt functions to model instrumental and core-hole broadening factors. With improvements in processing times, are Voigt functions now the preferred choice, or does the pseudo-Voigt function still hold advantages over both?

Any insights and suggestions would be immensely valuable and greatly appreciated.

Ryan