I think the best approach is by Rietveld Method. With right software and a math background, its possible to take into account different sources of background noise and distinguish then between the amorphous content contribuition (http://scripts.iucr.org/cgi-bin/paper?ce5094).

Other way is to use a known standard (internal or external) with your sample (http://www.xrd.us/technote/amorphous%20content%20determination.htm).

Both ways uses Rietveld Method.

Where have you found this definition? Origin is not the best sw for data analysis. It's better doing that with Matlab. If that is your definition of 'relative crystallinity' you need to isolate your peak (fitting with an appropriate model), calculate the area of it, and then compare that to the total area of the other peaks. For the numerical integral see http://www.mathworks.com/help/matlab/ref/trapz.html for example.

... it seems the definition is based on the ratio between the total scattering of the crystalline phases versus the total scattering of the specimen. This means the sum of the areas of all peaks (not just one peak) versus the total area. It could be good as a starting point as it is prone to several problems! It's not easy to identify the crystalline features and because there are also other contributions to the scattering that would be considered in a wrong way (e.g. fluorescence, thermal diffuse scattering, instrumental background).

You can use Origin and you can use Matlab. None of them is however optimized for this type of task. I don't agree on the fact that Matlab is better than Origin. We don't need to reinvent every time from zero!!

For most graphing tasks Origin is orders of magnitude better than Matlab (you don't need to program anything to prepare a professional publication-ready plot). For some calculation is also quite powerful and you can use Origin C if the basic stuff is not good enough. For sure Matlab is far more flexible and it allows you to do things far beyond the limits of Origin (especially for what concerns the analysis part. For the task you need, I would rely on software for the analysis of diffraction data.

There are many available and the main source of info is still the CCP14 website:

http://www.ccp14.ac.uk/

Hello Ahmed,

I agree with Matteo. However, although origin pro may be used for create baseline, fitting peaks and integrate areas, it is sometimes hard to handle as it was created for other issues. The nicest program I know to do that is Profile Fit. You need to run it with Win XP, but it is worth it.

Ara! Please post a link to the above referenced Win XP software, Profile Fit, when convenient.

Hi Ravi,

I cannot find it in the web. I downloaded it from my institute. I can share a Dropbox folder with you, though, if you need it. Just send me a private with your Dropbox name account in that case.

I think the best approach is by Rietveld Method. With right software and a math background, its possible to take into account different sources of background noise and distinguish then between the amorphous content contribuition (http://scripts.iucr.org/cgi-bin/paper?ce5094).

Other way is to use a known standard (internal or external) with your sample (http://www.xrd.us/technote/amorphous%20content%20determination.htm).

Both ways uses Rietveld Method.

Ara! [email protected]

Agree with Guilherme Calligaris

There is a equation and with the help of XRD Graph peaks. You can get the % of crystallinity.

Only after calibration with "known standard samples" or the complete deconvolution of any and all instrumental, Nano structural and other extraneous effects on the 2D diffractogram. Best to use Matteo Leoni's suggestions. Much work exists since the early 1900's. (1950-1960's) Worth reviewing! http://www.flickr.com/photos/85210325@N04/10515372183/

By the way, in the "old days" along with "photo densitometers" we would plot the graph (with 'French Curves') on paper, cut out the specific peaks or profile segments and then weigh the paper. Even that would work to some extent with limited precision. This would usually depend on paper consistency and humidity. So Origin or MatLab depends on the degree of decadence desired. The bottom line as Matteo points out is to properly identify the various constituent components in the diffractogram correctly before you apply any formula. Get to understand the "formula" and the assumptions used correctly.

http://www.flickr.com/photos/85210325@N04/11228321453/

Here is just an example of a conventional 1D diffractogram, exhibiting both "crystalline" and "amorphous" components, using a 0D scintillation type point counter system. This data is "blind" to any effects of "preferred orientation" that invariably are present to some extent in such scenarios.

 Good reference Wissam! Thanks!

All they need to do now is to extend the analyses from the conventional 1D equatorial diffractogram to a 2D diffractogram invariably involving some extent of "preferred orientation" convoluted in the XRD signal. Calibration with "known standards" would help "relative % crystallinity" detection. Consistent sample prep would be key in any such endeavor along with WPPM concepts to deconvolute individual constituent XRD signal :-)

See example of Kevlar & Aluminum below. I'd be interested in such publications of similar analyses with 2D diffractograms if and when you have any.