Just a couple of housekeeping notes: I’m giving talks in Europe in a month at the following locations: Unité Matériaux et Transformations (UMET), Lille on May 16, hosted by Grégory Stoclet, Birmingham University, Birmingham on the 20th of May, hosted by Zoe Schnepp, Nottingham University, Nottingham, on the 23rd of May, hosted by Philip Moriarty, Between these dates, I’ll also be joining Zoe and Martin for beamtime at the Diamond synchrotron (beamline I11) between May 21 and May 23. Please feel free to stop me at these locations and say hi! For today, I’ve got another bit of data correction to show. I thought it might be interesting to put them all together and show you what difference it makes to an integrated scattering pattern. Many of the data corrections implemented are quite straightforward shifts and scalings, but some are more involved and have a greater effect on the scattering pattern.
More about this next week, but Ingo has kindly provided a Windows executable version of one of the latest development builds of the Monte Carlo code (with GUI). While I have not yet had the chance to test it (being on a mac myself), please go ahead and get it from here! Some of you may have seen the Live Fourier Transform video that was made a while ago. I am happy to tell you that there is now a new, better version out of that program (still rather small and straightforward, though).
With the Bonse Hart instrument out of commission still (yet another failure in the X-ray generator target assembly), I decided it may be time to have another look at the data correction procedures. Some of you may remember that I wrote a comprehensive review of all conceivable corrections in a recent open-access review paper, and while I implemented some of the corrections mentioned therein, it might be time to implement most of them.
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.
Reading in the detector data, and programming methods for that, is one of the more tedious tasks of any data reduction program. Those of you who write their own data correction programs know this all too well. Many detector systems store their data a little bit differently than the others, despite the availability of decent standards for storing images and their metadata (fortunately, some manufacturers are now using standard data storage formats). For example, the NIKA manual shows a rather lengthy list of formats for which support had to be written.
Small-angle scattering analysis has never been easy for those working with oriented nanostructures (e.g. fibres, processed polymers, rolled metal alloys), whose structure may lead to anisotropic small-angle scattering. Upon the collection of such 2D scattering patterns, one can integrate thin pie-slices of the data to obtain 1D curves and analyse them in the same way as “normal”, isotropic scattering patterns. This way, however, important cross-correlation information is lost. Alternative full-pattern fitting methods have been developed (amongst others during my Ph.D. studies), but they are complicated to tune to the system at hand and can be quite unstable in least-squares optimisations.
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!
Dear scatterers, First of all, allow me to wish you a very happy 2013, wishing you much comfort, many good meetings and world peace. With that out of the way, this year might be different from others on this weblog, as I have to spend oodles of time on my “favourite” activity: trying to publish. Since there were hardly any publications last year, this year must be better (or I will “perish”, as the saying goes). There are seven publications in the pipeline as indicated by the title, though only three with me as first author. So please check the website’s “publications” section by the end of this year, and you will see how far I’ve managed to come with that by then. One of the publications that hopefully will come out first is on the 1D Monte-Carlo method, which will allow for the retrieval of form-free size distributions after assuming an elementary shape (spheres are the prime choice, but it also works with isotropic cylinders). On top of that, it will give you uncertainties on the size distributions the quality and reliability of which are directly related to the uncertainties on your measured intensities. Anyway, once that is published, rest assured that I will announce it here (it has been one hurdle for me, so I will be very happy to see it out there). The Python code used for this is freely available, currently the final touches are being put on a good, clean variant which should be available very soon. For the restless, please drop me a line and the code can be sent your way. Other publications will be about (amongst others) the 2D Monte-Carlo method for anisotropic scattering patterns, as presented at the SAS2012 conference, and a paper applying the 1D Monte-Carlo method to precipitate growth in magnesium alloys (the ArXiv link to an early draft was posted a few weeks ago here: arXiv:1210.5366). So all in all, this will be a busy year when it comes to paperwork. Anyway, I do not want to remain in the shadows for the entire year, so I decided to upload some more videos this year. I started the series off with a short explanation on the “classical” way of fitting scattering patterns, in a short demonstration that I used at SAS2012. This recording (shown below, or on youtube here) was simple and quick, and your host was suffering from an allergy attack, so please forgive the movie its faults. I hope it is fun nonetheless, and with this, I will sign off on this blog post. Another post will be ready in a few weeks!
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!