Aerosol optical properties are central to the scattering and absorption of sunlight by atmospheric aerosols. Indeed, the representation of aerosols in climate models is one of the largest uncertainties in predicting future climate. The interaction of light with an aerosol particle is governed by its scattering and absorption cross sections, which depend on the particle’s microphysical properties, including shape, internal structure (including heterogeneity in chemical composition), and the refractive index of the material comprising the particle. The poor representation of aerosol optical properties in models arises in part from poor measurements to constrain their descriptions and test models.
Prof Cotterell's research concerns studies of the physicochemical properties and behaviours of aerosol particles through the development of new spectroscopy tools. He is leading several projects as Principal Investigator that are related to the research problem described above, including: a NERC Independent Research Fellowship project focussing on the optical properties of light absorbing organic aerosol particles commonly found in Earth's atmosphere; a NERC Standard Grant project working with the Met Office to develop next-generation technologies for in situ observations of aerosol optical properties from research aircraft; and an EPSRC Standard Grant project developing new measurement technologies to levitate single aerosol particles for subsequent measurements of aerosol optical properties with unrivalled accuracy.
A DPhil project in Prof Cotterell's group could involve any number of several potential research ideas, with the exact nature of the project to be agreed in consultation with the applicant. One area is to develop a new integrated measurement framework to better understand the connection of aerosol microphysical properties (i.e. particle size, composition, shape, and internal structure) to their optical properties. This project would be supported by equipment already funded through Departmental and Royal Society funding, and would involve performing laboratory measurements on a range of aerosol types during varied aerosol generation procedures, spectroscopically interrogating the generated particles to measure accurately their sizes, effective densities, and optical properties, and collecting the particles for electron microscopy imaging. A second area is to develop a new electrodynamic trap for levitating single aerosol particles, and using broadband elastic light scattering to provide information on the evolving chemical composition of the levitated particles as they undergo chemical reactions (e.g. driven by light - photochemistry - or oxidative chemistry at the particle surface). Another research area is to combine laboratory studies of optical properties for bulk samples of light absorbing chemical species (chromophores) relevant to atmospheric aerosols, and to develop machine learning approaches to predicting the optical properties of chromophores based on their chemical composition.
Research methods would involve using laser-based optical spectroscopy approaches (including cavity ring-down spectroscopy, cavity attenuated phase shift spectroscopy, nephelometry, broadband light scattering, and UV-Vis optical spectroscopy) for laboratory studies of aerosol particles and chemical species relevant to atmospheric aerosols. Data analysis approaches, including Python and C++, will be used to perform light scattering calculations for interpreting measurement data sets. All the suggested research areas above have several opportunities to collaborate with academics in the Department of Physics Oxford, academics at the University of Bristol, project partners at the Met Office, and international partners in the USA.
Qualifications and Experience
PhD in Chemistry; I have supervised 4 PhD students and 2 PDRA researchers; I am the tutorial Fellow for Chemistry at Corpus Christi College and teach undergraduate physical chemistry there. I also teach in a CDT at the University of Bristol, delivering a 2-day training course using team-based learning.
