**Name: **Brian Richard Pauw

**Age: **34

**Birthday: **January 5th

**Institute: **BAM Federal Institute for Materials Research and Testing, Germany

My GPG key is here.

My resume is here (last updated Nov. 2014).

My online publication list is here.

Summarius Curriculum Vitae:

I am a SAXS expert, developing advanced applied metrological methodologies for small-angle scattering. Currently, I am employed as a permanent researcher at BAM in Germany.

Thank you for your important information on SAXS.

Dear Brian, here Maria, thanks for setting up this page. I’m also Postdoc, I’m started a project that requires SAXS measurements. I’m new in this field and I’m hight motivated, I wonder if you know about tutorials specific that could help me, I wanted to attend a course but unfortunately I got the information with more than 3 weeks delay. Any way, I have an Anton Paar machine and the sorftware related to this company, they don’t offer tutorias. My needs are regarding nanoparticles in liquid, this nanaoparticles are zeolite precursors, I’m working with two types of organic molecules, one simple TPAOH and the other is a surfactant which behave not sure, first I need to find out the form, ly supervisor thinks that it is between lamilar and cilinder, that is tufff, any idea about tutorias that would help me?

Dear Maria,

Welcome to the field! I will write a detailed reply to your e-mail address, give it an hour or so..

Cheers,

Brian.

Dear Brian

I’m really impressed by you work especially the live FFT !I have a huge respect for your work ! It’s something amaizing ! Actually i’m have a degree on embaded sytems and signal processing .Iwas working on discret fourier transform i want realy to talk about it ….and you give inspiration so think you !

Honor,

J.Jebari

Dear Brian,

I’m a regular visitor of this weblog and I’ve got to say that it’s really good. I’m a novice in this SAXS business and I’ve found many of your posts interesting and informative.

I’m doing research now on inorganic materials in solution and in the solid state. We have a SAXS instrument here and my PI has been working with SAXS and those materials for a number of years. However, I’d like to do my own reading and learn the basics (and beyond) on the subject. What are the best books/papers/other resoruces available for me to start? Maybe I could have a list similar to the one you prepared for Maria.

Thanks in advance. I’ve just bought a Mac so I’ll be installing the live FFT in it soon to play around with the software!

Cheers

Pedro

Dear Pedro,

Welcome to the field! I hope you will find it interesting, and please ask any scatterer any questions.

Unfortunately, there is no good, thorough introduction to SAXS. I tried to introduce it a little bit in my “Everything SAXS”-review paper (open access), but I do not know how well that worked (let me know if it was useful to you). I’m trying to flesh that out in the new book, but unfortunately there is not yet all that much in it yet.

I think there is a lot of useful introductory information in the videos I made, so that would be my first advice. Other than that, if you have the chance, just play around with your instrument to measure all kinds of stuff (paper, leaves and such, but also water and nothing and capillary to see how the instrument behaves).

Also, I recommend getting a calibrated glassy carbon sample from Jan Ilavsky, so you can measure a strong scatterer and compare it to the calibrated curve!

Cheers,

Brian.

Hi Brian,

I just re-read the paper you coauthor with Abecassis and that got me thinking on one of my experiments. I have a small molecular metal oxide (spherical, Diam 1 nm) which, I believe, oligomerises in solution upon the addition of acid. It should form chains of different length and hence become a polydisperse sample. I don’t know how the size distribution would go.

I am learning to use the Irena macros developed by J. Ilavsky. However, he’s telling me that they might not be adequate for my experiment as they work best for wide size distributions and mine would be, presumably, narrow.

I was wondering if your McSAS program could work better for this. What are your thoughts on this?

I can send you files if you’d like to have a look at the data.

Cheers

Pedro

Hi Pedro,

There are two polymer models in McSAS you might be interested in using in that case: the Gaussian Chain model and the Kholodenko Worm (a wormlike micelle, like the gaussian chain but with thickness).

These functions are a bit slower, in particular it precalculates a lot of things before setting off.

I can have a look at your data if you are interested, or you can have a play yourself.

Cheers,

Brian.

Brian,

I took a while to reply as one guy from Anton Paar was having a look at my data using GIFT to extract so size distribution values. Anyhow, it’d be great if you could have look at the same data yourself to see how they compare. By the way, I showed him this weblog and he quite liked it. I was quite surprised that he didn’t know about it before.

Let me know what’s the best way of sending you data files.

Thanks!

Pedro

Hi Pedro,

I can take a quick look. There is an e-mail address on this page that you can use to contact me :).

Cheers,

Brian.

Hi Brian,I recently started to use McSAS software to analyze a series of core-shell samples with fixed cores and variable shell thickness. I obtained the size distribution for the cores (before the coating processes), and I would like to know if there is a way to use it as an input for the coated samples.

Thanks in advance for your help, and thank you for this nice blog!

Best regards,

Patricia

You have an interesting problem. It might theoretically be possible to use a random number generator for the cores of the coated sample, with a probability distribution as you got for the uncoated sample. However, you might run into trouble later on trying to make sense of the result: the shell thickness may be coupled to the core thickness, in a non-obvious way. There is also a very high risk of skewing the result of the core radius picker that way.

What I would suggest instead is to try and see if you can use the distribution limits from the uncoated samples, to set up a fit with a variable core *and* radius (i.e. both active). This will require a very narrow range of allowed sizes for both the core and the shell, and they cannot overlap. Double-check afterwards that the core size distribution of the coated samples still matches that of the uncoated sample.

Another option is to use the distribution you got using McSAS, and try to describe it using a parametric distribution. Then you can use those distribution parameters in a classical fitting procedure to describe the core sizes while fitting a parametric distribution for the shell sizes. You may have to set up your own data fitting procedure for this, as I don’t think SASfit or any of the others can handle more than one distribution at a time.

Please let me know if any of this works, I’m curious to know!

Hi Brian !

You make the Fourier transform beautiful and funny with that live demonstration, really great work. I think I will be around here more often.

Cheers,

Bibiana

There is at least I’ve python package for drawing nomograms -can’t remember any of their names now

Also, the caption has a typo – draw line between two _knowns_ to get the unknown.

Hi Brian,

I hope this is not an inappropriate place for these questions. I saw your dynamic fourier transform images on You Tube, which I thought were very helpful conceptually. If you shifted the vertical sine wave bars one half period horizontally, would the FT look the same? I am assuming the phase would change by Pi, but that would not show up on the magnitude display.

Considering the obligatory intermittency of image representations of movement (continuous images are always blurred), would the alternation of two out of phase vertical bar patterns introduce a time-based spatial frequency in your system?

Hi brian could you please elaborate that whether PDDF gives infromation about the average size or the maximum particle size of the system. However, if one has a bell shaped curve then what can we say about the average size of the particles. Moreover, what I have known is that it can be defined as the histogram of the pair distances of all the particles in the system. Then, if its the case then it means that if a particle is in a centre then it can connect with only another particle in one direction only in order to have a pair. It will be really helpful if you can clear my doubt.

Dear Sweta,

It is not completely clear to me what you mean.

However, I can say that the PDDF does not inherently give you information on particle sizes! The PDDF gives information on the frequency of lengths within your sample. It is the only thing which can be uniquely defined for your sample, independent of whether your sample is a glass, alloy or particle dispersion.

However, the frequency of lengths is often not so useful for your analysis. That is why, when you need to determine physical parameters such as particle size, you need to add information about what your sample *is*. That is, if you have a sample containing a dispersion of spheres, you put that information in your analysis, and only then you get information on the sphere dimension and possibly their packing (in dense systems).

Let me finish by saying that if you do try to get a size distribution from a PDDF by applying these assumptions, then your size distribution information obtained from your particles is volume-weighted. You can convert this volume-weighted size distribution information into a number-weighted distribution, but not without enormous uncertainties on the smaller sizes.

Hi Brain,

Thanks a lot that means PDDF does not have any direct relationship with the average particle size but the longest estimate of the corelation length between the particles gives the maximum dimension or the particle size.

Brian ,

Thanks for the reply.

Regards

Hi Brian, I am a new appointee on SAXSpoint. I performed few measurements with carbon nanodots. But i am encountering few problems.

1. when the concentration of nanodots was 5mg/20 ml in water as buffer and exposure was 30 minutes, the sample intensity after buffer subtraction decreased in the form of a dip after primary beam .

2. Then, I increased exposure time to 1 hour, then the intensity of sample was found to be lower than the buffer.

3. then, I increased the concentration of sample to 5 mg/10ml and sample showed aggregation.

Please guide me how should i proceed with the experiment.It will be a great help.

Thanks

Hi Shweta,

a sharp dip at low Q can indicate that you’re oversubtracting your background. This can happen when the transmission factors or primary beam flux between the sample and the background is different and not corrected for. With this sample, I don’t expect a big diff in transmission factor, but you can calculate. Are you using a flow-through cell for both background and sample?

According to my sheet, it might be hard to have enough scattering contrast with those carbon nano dots at 0.25 mg/ml in water to see anything. You may want to start with a simpler sample such as silica nanoparticles, or our silver nanoparticle dispersion (see our round robin experiment paper), to make sure your system is working.

After this, take a careful look at your data correction procedure (we have a paper on that too). This is more involved than many assume if you want to do it right and correctly determine small signals. Unfortunately, I’m not sure if your instrument can accurately measure flux and transmission: I’ve not worked with the SAXSpoint, so I simply don’t know…

Start with simple samples, look critically at your data, and work your way slowly towards more complex samples. Good luck!

Hi Brian,

Thanks so much. I am using a quartz 1 mm capillary for the experiments. I worked it out with carbon nanodots, it is giving a good scattering with changed buffer.

I will start with silver nanoparticles dispersion also.

I don’t know how to calculate the flux of primary beam , can you suggest any standard procedure or any paper related to that.

Thanks, I t was a great help.

Hi Shweta,

We use this method in the SAXSess for measuring the primary beam intensity and the transmission of the sample:

https://lookingatnothing.com/index.php/archives/2043

For the MAUS, we move out the beam stop to measure both directly on the Dectris Eiger detector. It might work on the Pilots detectors as well, but I don’t think you can do this with a Mythen due to you exceeding the max. count rate on this thing…

B.

Hi Brian,

Thanks so much. I will go through the article.

I was wondering if there’z any direct formula for calculating surface area and pore volume for the mesoporous samples from SAXS, just like as we have for Guinier analysis, coz in publications, itz very complicated.

Thanks always for the effort u put in to reply.

Hi Brian,

Thanks so much. I will go through the article.

I was wondering if there’z any direct formula for calculating surface area and pore volume for the mesoporous samples from SAXS, just like as we have for Guinier analysis, coz in publications, itz very complicated.

Thanks always for the effort u put in to reply.

You might look at the derivation of the Porod method, this could be useful for that purpose.. Or you can try using a full analytical model for your pore structure and fit the whole curve (as you should). This does require you to measure in absolute units, but this is not so difficult if you follow the data correction methodology.

Thanks Brian,

I will be working on it.

Hi Brian,

Can we compare the average particle size obtained from TEM with the size or DMax obtained from the SAXS results ?

Yes, as long as you realize that the size distribution represented in the SAXS patterns are fundamentally volume/mass weighted distributions, then you can translate this to a TEM mean diameter if you convert either the TEM distribution to a mass-weighted distribution or the SAXS distribution to a number-weighted distribution. Be careful with and convert your uncertainties as well, and you’ll see that for broad distributions, the uncertainty on the *number* of smaller sizes gets super large once converted. For not too large distributions, comparisons between SAXS and TEM have been done many times in the past.

As for DMax, this is a more abitrary/handwavy metric, and I don’t expect to see much of a comparability there.