Measurements and Models
Aerosol science can only advance as quickly as new measurement tools and model methodologies are developed. CDT projects are developing new optical based approaches to detect cloud droplets and biological aerosols using machine learning. New tools are also under development for identifying airborne microplastics and characterising exhaled aerosol, and for inferring particle shape and charge. Modelling approaches are being developed to treat aerosol in turbulent flows and improved aerosol filtration.
Development of a Novel Single Droplet Mass Spectrometry Approach to Investigate Interfacial Photochemistry in Aerosol Droplets
Chemistry at the aerosol-air interface may be fundamentally different to that occurring in the bulk. However, vanishingly few approaches can measure chemical composition at the aerosol-air interface. This project will involve the development of a completely novel approach to study directly the chemical composition at the aerosol-air interface. The student will gain experience with single droplet levitation approaches, mass spectrometry, and the effects of electric fields on liquid droplets.
HYDRA – Hydrogels for aerosol capture
Viruses and bacteria have evolved to become adept at invading, and remaining viable inside, natural hydrogels like mucus; suggesting hydrogels possess an innate ability to sustain the viability of a bioaerosol sample. This project will combine aerosol science, engineering and polymer chemistry to develop aerosol capture devices with soft hydrogel components. Engineering new synthetic hydrogel materials with tuneable physicochemical properties could transform our capability to protect a collected bioaerosol sample and optimise recovery of viable pathogens for identification of airborne pathogens. Experimental testing will utilise simulants and explore capture and recovery of foot-and-mouth virus from aerosols produced by infected livestock.
Michael Cook will be based at the UCL School of Pharmacy and it is anticipated that the student will have opportunity to work between UCL and UH.
Combining state of the art real-time multi-technique optoelectronic bioaerosol spectrometry with neural network algorithms to discriminate, monitor and model different biological aerosol emissions from agriculture
This studentship will combine state of the art real-time single particle integrated optoelectronic bio-aerosol and dust-aerosol spectrometry techniques with neural network data analysis and micrometeorological flux measurement techniques, augmented by laboratory wind tunnel studies, and UK Met Office dispersion models to transform our understanding and quantification of bioaerosol emission fluxes from different agricultural landscapes under different atmospheric conditions and agricultural applications. Using the same flux techniques, spray pesticide dispersion cloud properties and deposition efficiencies may also be monitored over and within agricultural canopies using high speed liquid droplet spectrometers and turbulence sensors. 3D plume mapping techniques using drone based aerosol measurements will also be investigated for inverse emission flux model studies to improve emission quantification.
Radioactive Aerosols in Wall-bounded Turbulent Flow
Nuclear energy production is set to expand as one of the means for reliable energy output, as we curb CO2 emissions. This project employs advanced mathematical and computational models to develop an understanding of the complex interaction of radioactive aerosols, as these are transported and deposit in ventilation systems.
This project is an industry funded studentship supported by National Nuclear Laboratory.
Digital Microfluidic Lab-on-a-chip for multiplex detection of biomarkers in exhaled breath
Exhaled aerosols contain precious information on lung health, which could inform diagnosis and therapies and help saving lives. This project will combine emerging microfluidic and lab-on-a-chip technologies to create a portable and fully automated Lab-on-a-chip for detection of multiple disease biomarkers in exhaled aerosols.
Airborne microplastic detection and quantification – developing, evaluating, and applying novel laboratory and field-based approaches
Microplastic particles are emitted from a range of sources but remain a poorly understood fraction of airborne particulate matter, with potential health impacts. This project will use cutting-edge laboratory and online analytical techniques to identify chemical and optical markers in different environments and better understand microplastic emissions.
This project is an industry funded studentship supported by LECO Corporation.
Developing and deploying new sensors for in-situ monitoring of clouds
Clouds form a crucial component of the Earth system, reflecting large amounts of incoming sunlight back into space. Low-cost sensors are needed to allow long-term monitoring of climatically relevant cloud properties, but to-date no such sensor exists. This project will develop and test new sensors for cloud monitoring.
Airborne particle collection into single droplets to analyse and identify harmful aerosol constituents
Aerosols are a primary mechanism for spreading harmful particles and diseases. It is crucial to improve the speed and accuracy of detection by concentrating the material during collection. This project aims to achieve this by investigating techniques for collecting aerosols directly into droplets using prototyping, experimental and modelling approaches.
Improving Evaporative Light Scattering detector performance using experiments and modelling
Evaporative Light Scattering detectors are used with high performance liquid chromatography by collecting light scattered by droplets formed from separate analytes. The project will combine experiments with modelling and simulations for the nebulisation and evaporation process to allow the sensitivity of the detector to be improved.
This project is an industry funded studentship supported by Agilent Technologies.
Low-cost sensing of ultrafine aerosols: Sensor development and integration for first and second moment measurements
Sub-micron particulates are important pollutants, but difficult to measure with inexpensive methods. This project will use and further develop two low-cost sensors developed by the group to measure total particle area (nd2) or total particle length (nd) and thus diameter (d) in the atmosphere.
This project is an industry funded studentship supported by Cambustion.
Modelling the impact of soot fractal aggregate structures on the aerodynamic and mobility diameters of particles in the transition regime
The ageing process, i.e. soot maturing in the atmosphere, usually involves partial or total coating by water or organic compounds. This added material drastically changes the radiative transfer to/from the in-flight particles and their overall morphology and dynamics.
Smart filtration of aerosols in ventilation systems
Aerosols in ventilation systems of energy efficient buildings affect indoor air quality. The experimental and computational project examines the influence of flow speed, level of turbulence and aerosol size on aerosol tendency to concentrate and deposit in typical ventilation ducts. Findings will guide active and passive control for efficient filtering.
Dynamic Surface Properties of Atmospheric Aerosol and Resulting Climate Impacts
The surface tension of atmospheric aerosols impacts their ability to serve as cloud droplet seeds and affect climate. This project will develop approaches to measure droplet surface tensions and better resolve dynamics at the particle surface, working closely with modellers.
Investigating the charge states of ambient and indoor aerosols
Conveying and generation of powders can lead to very high levels of charge on particles, affecting their transport agglomeration and ultimate removal from the environment. Through modelling and experiments this project seeks to optimize collection of particles in filtration processes accounting for and manipulating electrostatic charge.
This project is an industry funded studentship supported by Dyson.
Building flexible biological particle detection algorithms for traditional and emerging real-time instrumentation
Whilst the importance of biological particles in the environment, human health and as a potential security threat is known, development of robust detection technologies remains a challenge. This project will apply and evaluate a range of machine learning techniques to to convert multidimensional signatures from new and emerging detection techniques into distinct PBA types.
This project is an industry funded studentship supported by Droplet Measurement Technologies.
EPSRC CDT in Aerosol Science
University of Bristol
School of Chemistry
Bristol, BS8 1TS
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