There have been many developments on the Monte Carlo program that I have been rather silent about. For those that forgot: this is the method that allows the determination of a particle size distribution (or rather a scatterer size distribution) from a small-angle scattering pattern if you select a shape. It is described in detail here, but the supplementary information shows the real strength of the method: it can retrieve a wide variety of realistic size distributions.
Good news for those of you on the hunt for a way to get polydispersity (size distribution) information from your scattering patterns. Two pieces of good news, to be precise! Firstly, the paper that describes my implementation of the method that does exactly this has just been accepted earlier this month for publication in J. Appl. Cryst, though it will probably not make it into the February issue. With a bit of luck, I will be able to make it open access, though! I have talked about the method before (e.g. here) so I will not spend more words on it. The second news is that the Python code with the fitting procedure is now available in an online repository here, thanks to Pawel Kwasniew at ESRF for his efforts in setting up the repository. The code comes complete with a quickstart guide with several pictures and some test data. If you are reasonably familiar with Python, why not grab a copy and try the method on your data? Reports from early testers have been positive, and everyone is encouraged to comment or send me an e-mail so it can be improved. License-wise, the code is released under a creative-commons-attribution-sharealike license. Lastly, if you want to contribute to the code you are more than welcome to. Currently, the code is being recoded in object-oriented form to improve flexibility, with the first release of the OO version expected later this month. Afterwards, a smearing function will be implemented for directly fitting slit-smeared data, and more shape functions should be included. As it is intended to be integrated in existing SAS analysis GUI’s (of which there are quite a few), there is no graphical user interface, and as such the focus is on getting the base functionality implemented right. As usual, drop me a line or leave a comment!
We have now entered the final two weeks before the Scattering-Bonanza that is SAS2012… Are you feeling the pressure yet? With many good talks lined up for that conference, and many good people attending, I am honestly quite excited! I too will be giving my small contribution in the form of two talks, one at the Bonse-Hart satellite meeting, and another at the SAS2012 conference itself (Tuesday, 16:00 at the D4 session). While there are many good things being prepared (publications, presentation preparations and the likes), also for this website, none of them are quite ready yet. To give you a taste of the upcoming publications, there are two pre-publications of mine available on arXiv. The first considers the 1D Monte-Carlo model I have been talking about every now and then. Development on the analysis method has been ongoing for quite some time, and it is now in a very useable form. While not all of the latest modifications are in this pre-submission version of the manuscript, some details can be gleaned. It is available here: arXiv:1210.5304 The second paper applies this Monte-Carlo model to study the ageing-induced growth of rod-like precipitates in MgZn alloys, comparing the resulting radii distributions to the distributions found using TEM. This collaborative work between dr. Julian Rosalie (TEM expert) and me shows the symbiotic relation between the two technique very well. In other words, TEM provides morphological details so that the Monte-Carlo method can be applied to extract a size distribution. A very young version of that manuscript can be found here: arXiv:1210.5366 I hope you enjoy some of the results in there, and please talk to me at the SAS2012 conference!
I have worked on monte-carlo procedures for a while now. Initially (while working for Aarhus University), I managed to make these work to extract the particle size distribution from isotropic scattering patterns, and it works fine and quick. Now though, I managed to get this trial-and-error procedure to work for whole 2D pattens as well! The added dimension adds some coolness to the whole thing, and carries extra information to boot. I will be showing the details and results in my upcoming talks (hopefully at SAS2012 as well), but for now, please enjoy the short movie where the new method is applied to a measurement of PPTA fibre:
As promised, some more details which hopefully make the monte-carlo code (discussed here) more useful to you; the software documentation. softwaremanual_mcfit In the documentation are also some examples using the scattering pattern generators discussed before. Let me know if you canor cannot get it to work and post your findings here (or father, on the software page)!
Hi all, Sorry for the big wait. It is not over yet, but I am working on it. More posts hopefully soon although I cannot promise a topic. Might be videos, but maybe also the monte-carlo code. The Monte-Carlo thing is almost ready to go, just some documentation left to write. It has become a slow bugger by now, but if you have a fast computer, you have nothing to worry about ;). Limitations: You need good data. and I mean thousands of counts. Millions, if you have them. Also, it only works for dilute, spherical systems. God, I feel like a physicist now… Anywho, for that, it works and it retrieves all information present in your scattering pattern. Check again soon and I may have it up here. And I hope there is a publication to follow as well, as we have to keep the numbers up in this climate. Oh, and if you wonder about my current writing style… I must admit, I have been overdosing on Transmetropolitan