[Ifeffit] fitting a specific k range

[Chris Patridge]

Matt,

Outside of the question of fitting the data, can you collect absorption data on the additional 3+ metal you are adding the material?  

Chris
 
*******************************************
Dr. Christopher Patridge
Assistant Professor
Department of Math and Natural Sciences 
D'youville College
Contact: 315-529-0501

On Dec 5, 2013, at 10:11 AM, Bruce Ravel <bravel at bnl.gov> wrote:

> 
> Matt,
> 
> At the risk of coming off sounding a bit mean, I don't think you are
> asking a very well-posed question.
> 
> Examining the history of this project, I see that you are fitting in q
> space.  Like Matt, this is not my favorite choice, but there is
> nothing horribly wrong about it, so long as you understand what you
> are doing.
> 
> What is problematic is your expectation that, in doing so, you should
> be better able to fit a particular feature in k space.  If you examine
> the data in k and q space, you will see that the act of Fourier
> filtering the data (i.e. plotting in q space) has the effect of
> suppressing the wiggle at 5 inv. Ang. that you are asking about.
> Given that you are fitting in q-space, it is completely unreasonable
> (from a numerical perspective) to expect that the fit could possibly
> reproduce a feature that you have (intentionally or otherwise)
> filtered out of the data.
> 
> To say that another way, given how you constructed the fit, you got a
> good fit.  You made the fit in a way that it cannot possibly reproduce
> the feature you are asking about, thus your question is ill-posed.
> 
> I think the deeper problem is that you don't have a deep grasp of what
> happens in Fourier analysis.  So let's talk about that a bit.
> 
> When you do the transform from k to R-space, you are representing the
> frequency spectrum contained in the original data.  Slow wiggling
> features in the original data give rise to the low-R
> (i.e. low-frequency) features in the chi(R) data.  Fast wiggling
> features in chi(k) give rise to high-R features in chi(R).  Your
> wiggle at 5 inv. Ang. looks to my eye like a pretty high frequency
> feature.
> 
> When you do the backwards transform from k to R with a restricted R
> range (in your case, from 1 to 3.5), you are filtering frequencies out
> of the data.  The chi(q) data only contains those frequencies from the
> original chi(k) spectrum that fall in your R range.
> 
> What I am suggesting is that the wiggle in question is due to Fourier
> components beyond 3.5 Ang in chi(R).
> 
> So, how would you reproduce that feature in chi(k)?  That's simple --
> fit features in the data beyond 3.5 in chi(R).  That is, do an actual
> good job of fitting the small signal from 3.5 to 5 Ang in R.
> 
> Of course, that's going to be difficult to do in a statistically
> robust manner because the signal is very small, there will be quite a
> large number of paths contributing to that region, and the
> parameterization of many paths for such a small signal is likely not
> to be very robust.  EXAFS is hard!
> 
> Hope that helps,
> B
> 
> 
> On 12/04/2013 02:17 PM, Matt Frith wrote:
>> Dear All,
>> 
>> I need some help in fitting an amorphous iron oxyhydoxide sample.  I am
>> having difficulty producing a good fit, particularly in the k=4-6 range.
>> Fitting this region well is very important for me, because if I add
>> another metal(+3 oxidation state) into my system, this is where I
>> observe the most quantifiable changes (The shoulder @ 5 A^-1 and the min
>> @ 5.6 A^-1). Thus far I have been unable to fit the shoulder well enough
>> to make meaningful comparisons.
>> 
>> I have been fitting in kq with kmin=2.566 And kmax=10.877, and Rmin=1
>> and Rmax=3.5, and using the goethite O1.1, Fe.1, and Fe.3 paths.
>> Attached is an Artemis file (P41_006_merge_norm_TRANS.fpj) for an
>> amorphous Fe oxyhydroxide sample (Fe only, no other metals). The data
>> was collected at the Fe K-edge.
>> 
>> *Is there a way to fit just this region (k~4-6 range) in k? If so what
>> is the best method for doing this? If not, does anyone have suggestions
>> as to how I can improve my fitting? Should I fit the data in k since the
>> shoulder is less evident in kq?*
>> 
>> Thank you for your time.
>> 
>> Sincerely,
>> 
>> Matt Frith
>> 
>> 
>> 
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> 
> 
> -- 
> Bruce Ravel  ------------------------------------ bravel at bnl.gov
> 
> National Institute of Standards and Technology
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> 
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