Interpreting Scattering Patterns from Fast Moving Particles

Identifying particles that are suspended and advected by air motions in the atmosphere is one of the most difficult problems faced by atmospheric scientists. Knowledge of these particles, their size, shape and composition is needed for a huge range of climate, atmospheric chemistry, radiative transfer and pollution applications. However no single instrument can provide all the parameters required to measure these parameters, which are needed to identify their origin and their potential impact. Different instruments are needed for chemical composition analysis (slow and complex), size, shape and scattering properties (fast but difficult to interpret). In this project you will work with a new instrument developed to record the scattering patterns of airborne particles in real time. This instrument is unique and was originally designed in collaboration with our partners at the University of Hertfordshire for operation on the NASA Global Hawk unmanned research drone on which it was tested at very high altitudes (Figure 1). The instrument was originally designed to detect ice crystals but also highlighted the presence of dust particles. For this project we will focus on expanding the possible applications of the instrument to sample airborne dust particles and develop software tools to identify and discriminate different particles types according to their shape and surface morphology based on 2D polarization scattering patterns which are recorded on a particle by particle basis. These particles are transient, often passing through the instrument at several hundred kph (when mounted on aircraft platforms) and so may have random orientations when exposed to the optical detection system which captures their scattering patterns in real-time. The challenge therefore is to retrieve information about the particles original structure, their size and shape, and surface morphology. The project will develop software tools to achieve this. If successful this will open up a plethora of potential applications for similar technology used in many similar instruments.

The project will start by developing software to configure the basic data structures from the instrument to provide a library of ambient particle data from different environments. In parallel models to simulate particle diffraction patterns for prescribed particle shapes with random orientations will be used to develop virtual particle libraries to compare with “real data”. And finally samples of real particles, which will also be characterised in the laboratory using a new state of the art nano-scanning electron microscope and mass spectrometer, will be passed through the instrument and the various scattering patterns recorded. The challenge will be to use the combination of real and virtual particle data to discriminate between different particle types.

This project will be mainly computer based with some laboratory work so would suit a candidate with strong mathematical and programming backgrounds interested in applying their knowledge to real-world applications. Continual additions to the real particle data sets will be possible as the instrument is expected to be used in future international airborne and surface field experiments at different locations around the world, as well as in European cloud and aerosol chamber experiments. Other more exotic particle types may also be examined with very different detection applications in mind and the project is designed to adapt to the candidates strengths and interests in applying these new software tools to different areas of aerosol science.

The candidate will be expected to work closely with the leading UK cloud and aerosol teams in Manchester and through collaboration with many UK and international partners via regular meetings, and in particular with Dr. Topping’s team who are developing machine-learning algorithms. The candidate would also work with our collaborators at the University of Hertfordshire who developed this technology on interpretation of the real particle data sets. All computing and laboratory facilities will be provided and the small amount of laboratory work including nano-scanning electron microscopy and mass spectrometer analysis of single particles will be supported by state of the art facilities within the School with full training and support staff provided.