[Ifeffit] Fitting Commercial Molybdenum Disulfide

Scott Calvin SCalvin at slc.edu
Fri Oct 29 17:53:58 CDT 2004


Hi Dan,

You can't exclude paths by pushing the R-range up to just short of 
the Reff for the path! This is for three reasons:

1) The EXAFS Fourier transform is not the radial distribution 
function, although it is "related" to the radial distribution 
function. One example of this is that the contribution from a path 
tends to be centered lower (maybe half an angstrom) in R-space than 
the Reff associated with the path.

2) Atoms vibrate, or have static disorder. That's what sigma2 is 
about, after all. This means that if the mean absorber-scatterer 
distance is x, there are many cases where it is somewhat less than x.

3) Technical effects having to do with taking a Fourier transform of 
a finite data range introduce additional broadening into the signal 
due to a given path, so that it extends well below and above its mean 
value.

So how do you know how high to go in R? There are many ways to 
decide. One is to include the paths that you don't want to worry 
about in the fit (e.g. the MS paths at 3.989). Then, when the fit is 
done, use Artemis to plot those paths. You can then visually see how 
far down in R they have a noticable effect, and set your Rmax 
accordingly. If you really want to be sure. run a fit with them 
included and one without. If the fit does not change significantly (R 
factor, parameters stay pretty stable), then you know you're OK. If 
the fit does change significantly, you've got to lower Rmax.

--Scott Calvin
Sarah lawrence College

>Dear List,
>
>         Thanks for all the good suggestions.  Here's how my fit stands at this
>point.  I have a few multiple scattering paths at 3.989, in order to excude
>these 2 I put the R-range from 1-3.975.  It seems that you think I should
>include these paths, if so, do I treat them a little differently.  They don't
>seem to help the fit using the same parameter strategy.  Additionally, I
>extended the k-range from 2-13.5 to 2-15.  This seemed to improve the reduced
>chi-square dramatically.
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