Most of my time is spent tirelessly chipping away at the scientific rock face, probably bogged down fixing a bug in my code or staring at some noisy looking data. Every now and then it all comes together and I want to tell people about it. So I write up my results as best I can, spend hours tinkering with figures, another few hours getting the fonts right on the axes, and after drafts and re-drafts, eventually I’ll send it away to a journal to be published. This is where I become caught up in the process of peer review.

All being good it looks like I’ve secured employment for a tiny while longer. Hooray!
The place I’m moving to is a big place for synthetic colloids, so it seems like a good time to go through what I know about colloids. If nothing else it’ll be interesting to compare this to what I’ll know in a year’s time! So, here is a theorists perspective on colloid science.
I’ll spare the usual introduction about how colloids are ubiquitous in nature, you can go to Wikipedia for that. The type of colloids I’m interested in here are synthetic colloids made in the lab.

I’m finding that I’m becoming increasingly fascinated by shape. It seems such a simple thing yet scratch the surface only a little and the complexity comes pouring out. Take simple tiling problems; I can tile my floor with squares or regular hexagons, but not regular octagons - they’ll always leave annoying gaps. From a statistical mechanics point of view those gaps are very important, little sources of entropy that you can’t get rid of. In three dimensions understanding the packing of tetrahedra has proved no simple task. But that’s a story for another day.
So it came as no surprise that I was very taken with Lev Gelb’s talk on polyominoes at the Brno conference.

Just a quick one. I saw this post, When intuition and math probably look wrong, via Ben Goldacre’s mini blog. The problem is set as follows:
I have two children, one of whom is a son born on a Tuesday. What is the probability that I have two boys?
Intuition tells you the answer is 1/2, mathematicians tell you it’s something else. I’ll leave the answer until the end of the post in case you want to run off and solve it first. It’s essentially a fancier version of the Monty Hall problem.
The Science News article deals with this just fine so I don’t really want to expand on it.

I spent ages writing a post about some tricks I use to do quick analysis of data but it got incredibly bloated and started waffling about work flows and so on. Anyway, I woke up from that nightmare so I thought I’d just bash out a couple of my top tips.
This is a pretty nerdy post, you may want to back away slowly.
Pipes
Pipes are, in my opinion, why the command line will reign for many years to come. Using the pipe I can quickly process my data by passing it between different programmes gradually refining it as it goes.

The trouble with science is that you need to do things properly. I’m working on a paper at the moment where we measured some phase diagrams. We’ve known what the results are for ages now, but because we have to do it properly we have to quantify how certain we are. Yes, that’s right. ERRORS!
I’ve come on a long way with statistics, I’ve learned to love them, I defy anyone to truly love errors. However, I took a step closer this month after discovering bootstrapping. It’s a name that has long confused me, I seem to see it everywhere. It comes from the phrase “to pull yourself up by your boot straps”.

I came across this new feature in the NYT via Science Blogs by Steven Strogatz. You may remember him from his paper with Duncan Watts on small-worlds that arguably kick started modern network theory. It looks like it’s going to be a regular series so I highly recommend adding the feed to your rss reader.
The article that first caught my eye was called Rock Groups. It starts by differentiating between the serious side of arithmetic and the playful side. This is something I’ve long gone on about but never quite had the nice way of putting it like these guys do.

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The New York Times is running a piece about tap water and the regulation thereof called “That Tap Water Is Legal but May Be Unhealthy”. One particular contaminant becomes dangerous on exposure to sunlight so, at a lake in Los Angeles, they’ve tipped 400,000 plastic balls into the lake to block out the sunlight.
Perhaps this shows I’ve been in stat-mech too long. All I could think about upon seeing this picture was - “cool, a massive 2D elastic disc simulation!”.
It’s quite interesting where the crystal structure is interrupted - each one of those interfaces costs a lot of free energy.

Probability can do strange things to your mind. This week I had a probability problem where every time I tried to use intuition to solve it I ended up going completely wrong. I thought I’d share it as I think it’s interesting.
Consider a one dimensional random walk. At each time step my walker will go left with probability \(p_l\), and right with probability \(p_r\). It stays where it is with probability \(1 - p_l - p_r\). Furthermore these probabilities are dependent on the walker’s position in space, so it’s really \(p_l(x)\) and \(p_r(x)\). I’m imagining I’m on a finite line of length, L, although it doesn’t matter too much.

It’s been ages since my last post. This is because I’ve been busy doing lots of interesting physics, met a bunch of interesting physicists, maybe I’ll write something about it. For now, something I’ve been meaning to write about for a while, and for once it’s something that’s timely.
The journal Soft Matter has an issue out with a membrane biophysics theme. You can read the editorial for yourself if you have access, otherwise make do with my ropey understanding of it. Soft Matter is a relatively new journal that I think is looking really good. Their website needs work but I’ll leave that for my science 2.