There is a scathing review coming out on the topic of academia (particularly the US one). While the general tone of the dissertation is negative, they do indicate a strong need, or rather duty, of scientists to communicate our findings as clear and understandibly as possible. Personally, I completely agree with this. Clear communication sometimes hinges on explaining everything in cool graphics, be it in posters or in presentations. So how do we learn how to create cool graphics? Well, one avenue is comics! They have supplied us with concise stories, be it comical or educational, applying graphics to provide a visual narrative.

One of my favourite online comics is Dresden Codak. Witty, beautifully drawn and often requiring quite some thought to understand the story, it is everything I seek in a comic. The author/artist also provides us with excellent tools to learn about visualisations, as he keeps a superb weblog analysing various forms of visual narration. This post is my favourite, as it indicates very clearly how we can use space intelligently in our graphics to tell a story, and what pitfalls to avoid. I hope it will provide us all with good inspiration for our next talks! Good luck!

Hi all,

I found some interesting papers for you, and a talk. Let me start with the talk. It is a TED talk (naturally) concerning TED talks. This nice introspective talk is actually of interest for all of us as it gives a few pointers to the set-up of excellent (and terrible) talks, with a fascinating slide on the colours used to evoke certain responses from the audience. Funny and applicable to us to make our talks better (and we know we need it, right?). The talk is here.

Then there are some papers, two of which I found to be closely related to what I did. One paper discusses the stretching of voids in tensile experiments, simulating the 2D patterns with cylinders (but unfortunately not using a 2D fit, but 1D slices to arrive at a solution). That paper is here (yes, you have probably already read it since it is in j.appl.cryst., but just in case you have been too busy like me to read the table of contents…). Another one is similar, but I must admit I have not managed to completely read it yet. Looks interesting, though.

Also, it is not everyday you see a new geometry diffractometer being suggested. I wish these guys good luck in the further development of their diffractometer and I hope they publish some fantastic results when they get to it.

At the moment, I am trying to do a literature research (something I should have done much, much earlier) on in-situ particle growth studies using SAXS. I have come up with quite some references by now, but if you know an excellent study, do drop me a line at brian at stack dot nl, and I will be eternally grateful. If you are, on the other hand, interested in co-authoring a small review paper on the topic, I am always open to collaborate!

The determination of the particle size distribution from small-angle scattering curves is usually achieved by assuming a certain statistical size distribution model (f.ex. a Schultz distribution, a Gaussian distribution or a log-normal distribution), and fitting this to the data using a non-linear least-squares optimisation method.

Fitting multimodal distributions then implies the addition of multiple contributions, each with their own set of parameters. This increase in the number of parameters may make the fitting function unstable and the results unreliable.

Retrieval of distribution model-independent size information therefore would be of great benefit to the experimentalist. One problem with this is that the scattering intensity of particles scales with the volume of the particle squared (i.e. for spherical particles with the radius to the sixth power). This then causes information on the small particle sizes to be drowned out by the signal of the larger particles.

A method to retrieve this information is presented in the 1996 paper entitled “Small-Angle X-ray and Neutron Scattering of Polydisperse Systems: Determination of the Scattering-Particle-Size Distribution” (M. Mulato and I. Chambouleyron, J. Appl. Cryst. 1996, 29, 29-36). This paper presents an iterative method for retrieval of this information, and compares it to existing methods such as implemented in the GNOM package. A particularly challenging bimodal size distribution with one mode at 0.5 nm and another at 5 nm reveals that the newly presented model is capable of retrieving this distribution to good agreement.

This then is a very interesting approach to the problem of the determination of polydispersity information from systems of hard spheres. Personally, I will certainly implement this approach. In addition, the paper provides good insight in the challenges associated with scattering problems of a polydisperse nature. Lastly, its clear writing makes it recommended reading material.

All in all, an interesting paper worth reading. I will let you know how it works for me if I can get it implemented.

Hello everyone,

I’d like to draw your attention to a course I would recommend to any ph.d. student using neutrons or X-rays. I’ve had good fun during this course, and learned a lot, so I’d recommend anyone to apply for this:

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Binning of data is often done to SAXS data in order to reduce the size of the data, “improve statistics” or otherwise retransform the data to better suit the subsequent plotting or fitting of the data. Common 2D and 1D binning methods are “linear” binning, where the q-range is divided into bins of equal size, and “logarithmic” binning, where the q range is divided just so, that the bins are equidistant on a logarithmic q-scale.

Depending on your binning method, you may be weighting your data differently in subsequent fits of no weighting is done by uncertainty or error of the intensity. If you would like to weigh your data points in the fitting by their errors, you need to compute the error for each pixel, which is often given as the square root of the number of counts on your detector. This does imply that you either need a single photon counting detector or that you need to scale your detector output to photon counts.

An alternative to this is to rebin your data just so, that all points have the same intensity, meaning that you have wider bins at higher q than at low q. Since each bin then contains the same relative amount of intensity, the error in each bin should be the same (similar solutions can be achieved with non-position sensitive detectors such as the cyberstar, by counting in each position until a certain threshold value has been reached).

I have written a small Matlab program which achieves just this, and is fast enough to do this for large datasets (i.e. 2D images consisting of 2048×2048 pixels) within seconds. I invite you all to take a look at it, and, as always, suggestions or alternative methods are more than welcome. The input is self-explanatory, but the output isn’t necessarily. The output consists of four vectors:

• qbin: the mean centres of the bins
• dqbin: the width of the bins
• Ibin: the intensity in each bin
• Ibdq: the binned intensity divided by the width of the bins. This is given for plotting purposes (since plotting qbin vs. Ibin would give you more or less a straight line).

Good luck!

equal intensity binning routine:

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After too much time, I have finally upgraded to a newer version of wordpress. Previously I had some issues during writing, so that it did not easily understand paragraph breaks, I hope that’s finished now.

In other news, I am only 9 days away from handing in my thesis. The name of the website may change after that, but since I’ll still be working on SAXS, it’ll certainly continue. If you have any suggestions for content or just to say hi; drop a comment below!

B.

Step one in creating a good presentation is to spice up your talk. I will present here several methods you can use to make a spicy talk. Read the rest of this entry »

I think there are a ton of wonderful topics out there, that are currently being researched by scientists. Now more than ever, they need to be able to present them in front of not only their peers, but also to those who control money. The presentation, therefore, needs to cater to a wider audience.  Read the rest of this entry »

Hi, I’m starting the “Clear Presentation Initiative”, as a movement to improve presentation skills in science. I will be posting more here about this soon.  Cheers! 

There is a new TED presentation out there. And whilst this one is not necessarily superb (The presenter is a little out of touch with the audience, and apparently rather nervous: his jokes do not hit the mark), what is very impressive is the way he presents his topic.

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