Aerogels are a class of highly porous nanomaterials with interesting properties for many applications. The technical relevance of aerogels as insulation materials has come to life with their second wave of industrialization around 2004.
The focus of current research lies on life science and environmental applications, which are still further away from commercialization.
The key properties of aerogels such as thermal conductivity, mechanical stability, sorption critically depend on their microstructure.
Thus, optimization of aerogels towards a specific application generally involves optimization of their microstructure. However, mostly due to their nanoscale characteristic length scale, there are currently no methods to directly determine the true 3D microstructure of aerogels.
State-of-the-art techniques such as electron microscopy, physisorption or small angle scattering (SAS), only provide partial structural information.
However, the combination of the different available techniques should provide sufficient data to allow the determination of their 3D microstructure.
Today the optimization of aerogels towards a specific application is generally done via experimental studies. However the amount of input data (i.
e. experiments or trial runs) required for full optimization will in many cases exceed the experimentally feasible. This is a known problem in materials science leading to an increased popularity of modelling as a method to expand the input data, allowing the successful optimization of materials and microstructures for specific target applications.
A reliable 3D microstructural aerogel model, developed by fitting simultaneously to data from different experimental techniques, will allow such an approach.
The aim of this project is thus to develop such a 3D microstructural model and to show that the model will allow faster and more reliable optimization of aerogels towards a specific application.
The goals of this project are :
Study of computer generated 3D models to interpolate and extrapolate the experimental data set and determination of the optimal microstructure for minimal thermal conductivity.
For this project, we are looking for a PhD student with a degree in materials science, chemistry, physics or computational science and engineering, capable of working autonomously but as a part of the team.
The candidate should have a sound basis in numerical modelling and programming.
The Building Energy Materials and Components laboratory at Empa spans subjects from fundamental research of wet chemical / sol-gel methods to the development of building components with the overarching goal to improve energy efficiency and reduce fossil fuel consumption.
In this multidisciplinary environment, communication and interaction to create synergies and develop new ideas is highly valued.