The physics of biofilms and ice cream

Cait Macphee

MSc Science Communication and Public Engagement students Anna Purdue, Hanyue Sun and Jiazhuo Lin  interviewed School of Physics and Astronomy researcher Prof Cait MacPhee.


Tell us about your research. How does it link to ice cream?

We are studying a biofilm formed by a very common microbe called Bacillus subtilis. We’re interested in the fact that it’s basically waterproof. We discovered the protein that makes this biofilm water repellent, and it does this by going to an interface between liquid and air and forming a film. Ice cream contains air bubbles which make it lighter and easier to scoop.  It also contains oil (fat) and ice crystals. The protein we found goes to the surface of the ice crystals, the surface of the air bubbles and the surface of the oil droplets and stabilises all of them. By doing this you can slow the melting down of the ice cream! Continue reading “The physics of biofilms and ice cream”

Industrial collaborations and problem solving

Reseacher Susana Direito

MSc Science Communication and Public Engagement students Anna Purdue, Hanyue Sun and Jiazhuo Lin visited us recently and interviewed School of Physics and Astronomy Dr Susana Direito about her research and industrial collaborations.


Tell us about your research

I have been working with Edinburgh Complex Fluids Partnership (ECFP) for three years, and have also been a researcher for the National Biofilms Innovation Center (NBIC) for two years. I am a bit different from the other researchers because my main focus is based on industrial projects. Companies will come to us with a problem and we will try to solve it, so it works a bit like a consultancy. Continue reading “Industrial collaborations and problem solving”

Machine Learning for particle physics

Student Brendan Martin

Brendan Martin is currently in year 4 of the MPhys Mathematical Physics degree.  He completed a Career Development Summer Project in machine learning.


I worked in the area of machine learning for particle physics. Machine Learning can be an extremely useful tool for analysing data from experiments – in classifying particles or identifying interesting event topologies, for example. Designing accurate, computationally cheap algorithms is therefore hugely important. Under the supervision of Prof Luigi Del Debbio, I investigated the relationship between the bias, variance and noise of a given data set using a deep neural network as an estimator. I gained insight into the fascinating, quickly developing field of machine learning whilst simultaneously improving my programming skills.

Continue reading “Machine Learning for particle physics”