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Development and testing of a novel approach to measuring biofouling on sensors and tidal energy materials.

Delgado Ollero, Adrian orcid logoORCID: 0000-0003-0791-0720 (2024) Development and testing of a novel approach to measuring biofouling on sensors and tidal energy materials. PhD thesis, Dublin City University.

When artificial surfaces are submerged in water, they can experience biofouling, which involves the accumulation of organic matter. The process starts when a clean surface is submerged, and microorganisms such as bacteria diatoms, barnacles and mussels colonize it, forming complex and dynamic microbial communities. These communities are surrounded by a matrix of extracellular polymeric substance (EPS). Currently, biocidal coatings are being utilized to prevent biofilm formation. However, many of these coatings are harmful to the aquatic environment, and their use is now regulated by legislation. Biofouling has long been considered a limiting factor and is recognised as one of the main obstacles to autonomous environmental monitoring in aquatic environments and tidal energy. As the demand for infrastructure to be in contact with water increases, the selection of the correct materials depends on the proper selection of the appropriate alloys and composites for the application and service environment. However, the analysis and quantification of biofouling is extremely complex as it is commonly based on biochemical methods like biofilm-extracted DNA analysis to understand the complexity and diversity of biofilms. Using molecular markers such 18S rRNA can help to identify different groups of organisms and thus to know the targets on which to focus prevention strategies on which manufacturers should manage their budgets in order to reduce maintenance cost due to this problem. However, although these molecular techniques are very useful, they are often not cheap and require highly specialised equipment and expertise in biology and bioinformatics. Therefore, in this work, the use of alternative techniques like image classification based on machine learning on different materials used for sensor manufacturing can help to make biofouling analysis more efficient and cost-effective. In addition to this, particular attention was paid to the examining and analyzing the effectiveness of novel surface topographical features based on Brill fish, (Scophthalmus rhombus), as inspiration to develop an antifouling texture for the first time, but also different materials and coatings on the adhesion of microfouling and macrofouling communities for sensor development and tidal energy applications from a laboratory scale using model organisms (Amphora coffeaeformis and Nitzschia ovalis) to the field scale with the construction of a floating platform with a turbine to perform environmental biofouling tests in real conditions.
Item Type:Thesis (PhD)
Date of Award:March 2024
Supervisor(s):Regan, Fiona
Subjects:Biological Sciences > Biosensors
Biological Sciences > Biotechnology
Biological Sciences > Microbiology
Physical Sciences > Analytical chemistry
Physical Sciences > Chemistry
Physical Sciences > Environmental chemistry
Mathematics > Statistics
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Science and Health > School of Chemical Sciences
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 License. View License
Funders:Enterprise Ireland, European Union
ID Code:29400
Deposited On:25 Mar 2024 15:04 by Fiona Regan . Last Modified 25 Mar 2024 15:04

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Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0


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