The interaction of light with aerosol particles is important in atmospheric optics, playing an important role in regulating the earth’s climate through interactions of solar and terrestrial radiation with aerosols and clouds. Light-particle interactions are also invaluable in the detection and characterisation of aerosol. CDT projects are exploring the measurement of respirable fibres from light scattering patterns, developing new approaches using deep learning to categorise particles from light scattering patterns, and employing novel single particle tools to trap and characterise particles by cavity ringdown and photoacosutic spectroscopies.
Photoinitiated Chemistry in Single Levitated Aerosol Droplets using Cavity Ring-Down Spectroscopy
Photochemistry in atmospheric aerosols represents one of the largest uncertainties in climate models, while understanding the enhanced rates of in-aerosol reactions could transform green approaches to chemical synthesis. This project will utilise recently developed state-of-the-art spectroscopy instrumentation to improve understanding of photoinitiated processes in aerosols.
Novel measurements of aerosol thermodynamic and optical properties using phase shift photoacoustic spectroscopy
This studentship develops a state-of-the-art spectroscopic approach to enable measurements of light absorption and volatility distributions for aerosols containing volatile species. The outcomes of this project will transform UK and international research capability in observations of aerosol properties that remain among the largest uncertainties in climate science.
Classification of microparticles using two-dimensional scattering data and machine learning techniques
Two-dimensional light scattering patterns contain information regarding the size, shape, and orientation of micro-scale aerosol particulates. However, these have proven difficult to classify using traditional algorithms. You will develop a machine-learning classifier to classify such particles as cirrus ice, bioaerosol, pollution, and other respirable hazards thereby providing hitherto unavailable real-time data analysis.
This project is an industry funded studentship supported by Alphasense.
Deep learning based classification of aerosol particles from holographic imagery
The impacts aerosol particles have are linked to their origin. Very few experiments are able to record the information to make that distinction in real time [e.g. volcanic ash detection]. However, digital holography combined with deep learning algorithms offer an exciting new potential to make that distinction.
Respirable Fibre Measurement from Light Scattering Patterns
Fibrous particle inhalation can cause a range of respiratory diseases. Current detection methods require filtration and manual counting under a microscope. You will work with state-of-the-art optical instrumentation to develop a technique for the real-time detection and measurement of airborne fibres.
Extinction Cross Section Measurements for Single Aerosol Particles Confined to a Linear Electrodynamic Quadrupole Trap
The contribution of organic aerosol to the warming of the Earth’s atmosphere remains uncertain because particle composition and morphology affect the absorption of sunlight. Using a recently developed spectroscopic apparatus, this project will measure precise optical properties of single, trapped aerosol particles.
EPSRC CDT in Aerosol Science
University of Bristol
School of Chemistry
Bristol, BS8 1TS
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