Self-funded PhD students, Research Fellows and Visitors
If you have your own funding, an externally sponsored PhD studentship, want to apply for a Research Fellowship and are interested in our research topics, please do not hesitate to contact Prof. Pamme directly.
Sensing greenhouse gas emissions from weathering plastics using lab-on-the-sea technology
NERC Doctoral Training Partnership (DTP) – Panorama
Closing date: Mon 7 Jan 2019, Studentships will start 16 Sep 2019
(fully funded 3.5 year studentships (stipend + fees) to both UK and EU applicants)
Summary of project
Once plastics are disposed of they become part of the Earth System and so they begin to weather. As the polymers are gradually oxidised, they lose mass producing methane, ethane, ethene and associated gasses. These reactions are well-known, but the fluxes produced by plastics in the environment and the changes in gas production rates at the different stages of weathering for the different polymers is not well-known. These emissions are greenhouse gases and thus can be major contributors to climate change, further exacerbating the enormous problems associated with the inappropriate disposal of plastic litter. The capture of gaseous fluxes is presently limited by the analytical approaches used to measure them, which require manual handling, storage, transport and off-site mass spectrometry. This is expensive, time consuming and cannot be implemented as a monitoring array at useful spatial or temporal scales.
The aim of this project is to develop new technologies that can be implemented as autonomous monitoring arrays within the natural environment. The student will build on the leading expertise of Prof. Pamme in developing analytical measurement systems based on microfluidic lab-on-a-chip technology, to invent gas sensing systems that can operate in an automated fashion on a deployable buoy and transmit data at regular intervals, a lab-on-the-sea. Prof. Rogerson will assist with geochemistry and experimental design elements, and Dr. Caswell will assist with implementation, and support investigation into the implications for marine pollution.
Sensing and Safeguarding the Water Environment
University of Hull PhD cluster with 6 fully funded PhD Scholarships
Closing date: Wed 23 Jan 2019, Studentships will start 16 Sep 2019
Summary of Cluster
Water pollution from industry, agriculture and urban settings is a major global pressure on human and ecological health. Effective monitoring of water quality is vital to safeguard our water supply and managing the health of our aquatic ecosystems. However, traditional monitoring has relied on relatively expensive physical sensors operating at low spatial resolution that require skilled personnel for servicing and maintenance. Such sensing systems are often not affordable; in particular in those jurisdictions with the greatest challenges concerning water quality. A fuller understanding of water quality and pollution dynamics, including sources and behaviour of pollutants are often lacking, hindering cost-effective and targeted environmental management.
There is an urgent need for gathering data at low cost, that goes beyond simple physical sensors operating at low frequency (monthly, annually) and low spatial resolution (<1 sample per 40 km2 even in some OECD nations) towards chemical and biological sensing of pollutants and key environmental parameters at higher frequencies (< daily) and higher spatial densities. This will facilitate better quantification of trends and pressures, underpin predictive modelling and provide the foundation for robust and cost-effective management of the aquatic environment.
High frequency sensing requires either (1) simple and low-cost devices operated by citizens or lower skilled agents locally and uploaded to a cloud to build a picture of a larger area that is not obtainable by sending expert scientists into the field in isolated locations at isolated time points, or (2) automated systems mounted on platforms, buoys or robotic vehicles and sending data to a central server. Computer scientists are then needed to pull the information together, exploring change patterns and deriving computer models of the dynamics of environmental change.
With recent breakthroughs in pump- and label-free fluid processing as well as label-free high sensitivity sensing, complex sample processing workflows can now be greatly simplified, offering a pathway to create high density networks of automated sensors or enrolling members of the general public in gathering data with simple sensors.
In this cluster we bring together experts inenvironmental sciences for marine, coastal and river waters with experts in lab-on-a-chip, nanophysics sensing technologies and lifecycle engineering as well as big data analyticsto address research questions in this area.
The students will join an exciting research and training environment. They will have access to our Lab-on-a-Chip Fabrication Facility, our Fab Lab for 3D printing and additive manufacturing and our Nanofabrication & Nanopatterning Facilities. Our University has invested heavily in High Performance Computing with VIPER as the highest-rated academic HPC in the North of England. Through our Environmental Scientists we have access to a wide range of sample sites including streams and rivers, aquaculture farms and estuarine/oceanic sites. Our NERC/UoH-funded Transportable Environmental Analytical Laboratory (TEAL) will provide a key means for facilitating real-world testing. Our cluster supervisors have excellent links to industry and stakeholders which we will involve as advisors in the projects. This will provide a unique opportunity for our cluster PhD students to engage across sectors and provide access to additional training that will greatly enhance PhD employability and also create KTPs and spin-out opportunities. The PhD students immersed in this environment will develop responsible citizenship and be empowered to take on the next step in their career, be it as PostDoc, employee or running their own spin-out company.
PhD Project 1 – Mapping heavy metal pollution in river water with paper-based devices through citizen science with Nicole Pamme (paper microfluidics), Will Mayes (heavy metals in the environment) and Mark Lorch (science communication). Water contamination by toxic metals occurs across the globe; established methods require well-equipped laboratories and only give a sporadic picture of what are very dynamic environments. Frequent measurements (daily) in many locations are needed. To tackle this challenge, the PhD student will develop paper-based dip-stick devices to be operated by members of the local community. This requires suitable chemistry for fast readout and reagent storage. To enable pre-concentration as required for low-level pollutants, the student will develop simple filter systems linking with the pads. The devices will be trialled with existing partners in Kenya, Malaysia and/or Vietnam, using a custom-built app. Ultimately, this will enable a deeper understanding of the source, transport, and persistence of environmental contaminants. Candidates suited to this project would have a degree in Chemical or Environmental Sciences or a related discipline.
PhD Project 2 – Analysis-on-a-roll platforms for automated and high frequency remote sensing of natural geochemical fluxes with Ian Bell (engineering design), Gillian Greenway (analytical chemistry), Dmitriy Kuvshinov (chemical engineering) and Mike Rogerson (geoscience). The PhD student will investigate a novel approach to high-frequency measurement of chemicals in natural water systems using automated/robotic sensing that does not require any user input. This will allow in-situwater analysis in less accessible locations, e.g.cave systems, remote waterways or ocean platforms. Such systems can not only be used for pollution monitoring, but also provide data which can advance our fundamental understanding of geoscience. The ‘analysis-on-a-paper-roll’ approach will reduce operational difficulties associated with glass or plastic devices under repeated use in remote automated systems. The work will build on our experience in developing in-situlab-on-a-chip systems and will consider aspects of design for reliability (automatic fault detection, data integrity) and the possible impacts of in-situsensors on the environment. The project would be suited for a candidate with a degree in Engineering or a related discipline.
PhD Project 3 – Unravelling micro-plankton populations through lab-on-a-chip-based sorting and analysis platformswith Alex Iles (on-chip focussing/sorting), Ali Adawi (optical readout) and Rodney Forster (micro-plankton in water). Phytoplankton form the base of the marine food chain; knowledge of the plankton community structure is fundamental for assessing marine biodiversity. Plankton size is important: large cells can be consumed directly by herbivorous zooplankton and small fish, whereas small-celled picoplankton (<2 µm) are less important as food source and export less carbon to the deep ocean. Current methods are complex and inaccurate, relying on a combination of observations and models and the coastal ocean is greatly under-sampled for plankton data. The PhD student will develop inertial focussing and sorting approaches to be linked with highly sensitive nano-plasmonic spectroscopy readout (from project 4) towards an accurate estimation of plankton size with an ability to rapidly sort cells for biochemical, optical and genetic analysis. A candidate with a background in Chemistry, Physical Sciences, Engineering or a related discipline would be suited for this project.
PhD Project 4 – Cutting edge optical spectroscopy for sensing particulate pollutants in aquatic systems with Jean-Sebastien Bouillard (SERS sensors), Nicole Pamme (filtration), Dmitriy Kuvshinov (modelling) andDan Parsons (microplastics). Particulate pollution in aquatic systems, in the form of turbidity and microplastics, are a key and growing concern, and is at the forefront of political and policy attention. To understand the impact and spread of particulates, such as microplastics, there is a need for sensing not only of the number of particles, but also their chemical composition. The PhD student will approach this challenge by building on Hull expertise in nano-plasmonics and Surface enhanced Raman Scattering (SERS) sensors to obtain sensitive readout for chemical fingerprinting. Ultimately, this knowledge will be coupled with flow filtration and pre-concentration systems (from project 3), supported by fluid modelling (COMSOL). The project would be suited to a candidate with a degree in Physics or a related discipline and an interest nano-optical sensing approaches.
PhD Project 5 – Development of ultra-high sensitivity and selectivity nanogap electrode apta-sensors for mapping the prevalence of hormones in aquatic systems with Neil Kemp (nanogap electrode sensors) and Jeanette Rotchell (hormones in the environment). Pharmaceuticals and other anthropogenic compounds are found in the aquatic environment often at extremely low levels, yet are still capable of toxic impacts that require monitoring. A new advancement in nanoscale sensor technology at Hull, culminating in the development of a label-free, capacitive nanogap sensor with ultra-high sensitivity and selectivity, has the potential to bring a new dimension to environmental sensing. It avoids labour intensive collections and lab-based analytical assessment using large and expensive equipment. The PhD project will improve the fabrication methodology of the device and better understand the underlying physics of how the device functions. The ultimate aim will be to build a hand-held or remote prototype sensor that targets a number of EU Watch List chemicals, such as estrogens. The PhD student will develop advanced nanotechnology skills involving chip design, cleanroom fabrication methods, aptamer electrode functionalization methods and AC impedance spectroscopy measurement techniques. Working with our partners we will also aim to trial remotely stationed prototypes that are wirelessly linked to the cloud. The candidate should have a degree in Physical or Engineering Sciences or a related discipline.
PhD Project 6 – Modelling Environmental Data Systems with Deep Learning with Nina Dethlefs (Computer Science), Rodney Forster (Institute for Institute of Estuarine and Coastal Studies) and Dan Parsons (Energy and Environment Institute). The student will apply deep learning and HPC to induce a joint and multi-dimensional data model from a range of available environmental data. The goal is to identify hidden patterns and generate inferences and predictions on water quality, nutrients and pollutants that can emerge only from a multi-dimensional perspective on the data rather than taking a restrictive approach. We will derive models about movement of chemicals and processes in the environment by integrating and processing data sets from our deployed sensors with openly available datasets including those from remote sensors (e.g. EU-Sentinel satellite images) and apply transfer learning approaches, i.e. transfer knowledge from data in one environmental domain to another to accelerate learning and seamlessly integrate the disparate origins of the data to develop an interconnected inference model. The candidate should have excellent programming skills and a degree in Computer Science or a related discipline. Knowledge or experience in one or more of the following areas is desirable: machine learning / deep learning, embedded systems, physical sciences, engineering or environmental sciences.