The safe operation of nuclear storage ponds is crucial to the continued provision of low carbon nuclear power to the UK, while decommissioning of legacy pond systems from historic operations remains a significant £multi-billion challenge to the industry. Optimising the management and treatment of the legacy ponds on the Sellafield site is crucial to reduce the hazards, reduce the cost to the tax-payer and protect the environment. However, the impact of microorganisms on plant operations is becoming evident, as the formation of microbial blooms in legacy ponds lead to reduced visibility within these facilities and fouling of downstream water treatment systems. As a result, controlling microbial growth is of growing importance, to keep decommissioning schedules on time and to budget.
Recent work from the Manchester Geomicrobiology group, via collaborations with Sellafield Ltd. and the National Nuclear Laboratory, have identified discrete microbiomes in a network of hydraulically-linked legacy ponds (see references above). For example, cutting edge DNA-sequencing and metabolomic profiling has identified photosynthetic algae (MeGraw et al. 2018) and cyanobacteria (Foster et al. 2020a) in outdoor ponds, adapted to high radiation levels and other extremes (e.g. high pH) associated with the ponds, and potentially playing a role in controlling the fate of priority radionuclides (MeGraw et al. 2018 and Foster et al. 2020b). In contrast the low light intensities associated with indoor ponds, has not supported the widespread growth of photosynthetic communities, resulting in unique microbiomes sustained by hydrogen (generated through radiolysis reactions; Ruiz-Lopez et al, 2020). These studies have extended our knowledge of extremophile microbiology in engineered environments, and have also helped underpin control strategies, for example through carefully controlled purging cycles (Foster et al, 2020a).
This new study will focus on a closed-pond system, that has proved susceptible to microbial blooms, but cannot be controlled by purge systems. Recent work has resulted in the identification of a discrete microbiome in this pond, and helped fine-tune approaches for biomass control. We now wish to build on these positive initial results, via a new EPSRC CASE project which will:
1. Use DNA-based high-throughput 16/18S rRNA gene sequencing to monitor long-term operation of the pond and help fine-tune targeted treatments.
2. Apply complementary multi-omics approaches to study adaptation strategies within the pond.
3. Develop culture-based approaches to (i) confirm adaptation strategies in carefully constrained laboratory systems, (ii) quantify the impacts of microbial colonisation on radionuclide fate, and finally (iii) test additional control strategies for future use.
A cross-disciplinary approach will be adopted with training in techniques including culture-based microbiology, DNA extraction and sequencing (16S/18S rRNA and genome sequencing), bioinformatics, transcriptomic and proteomic analyses, geochemical and radiochemical profiling, and cutting-edge imaging and spectroscopy as appropriate. The student will benefit from access to our newly refurbished £4M NNUF RADER laboratories (https://www.nnuf.ac.uk/rader) and other facilities available through extant collaborations with Sellafield and the National Nuclear Laboratory. The student will join the largest grouping of academic researchers in the UK nuclear environmental sector, working closely with vibrant group of 40+ experimental officers, postdoctoral scientists and other PhD students. PhD students leaving the group are in high demand in both the nuclear/environmental sectors, and also by academia.
Based on your current searches we recommend the following search filters.
Check out our other PhDs in Manchester, United Kingdom
Check out our other PhDs in Biotechnology
Start a new search with our database of over 4,000 PhDs
Based on your current search criteria we thought you might be interested in these.
Multi-rate and/or non square systems and/or decentralised control
University of Sheffield
Application of predictive control to fast systems
University of Sheffield
Multi-omics data fusion for better understanding of host-microbe interactions in health and disease
University of Reading