We are looking for a highly motivated candidate for a PhD project funded by the Hydro Nation Scholar Programme. This project aims to develop a novel, quick and reliable analytical method to detect and identify the chemical composition of plastic particles (in the nanometers to micrometers range) in water samples using Surface-enhanced Raman spectroscopy (SERS), and machine learning tools to improve drinking water quality management.
Globally, around 350 million tons of plastics are manufactured annually and this amount is still increasing. A significant proportion of these plastics end up as plastic waste, largely due to widespread usage of single-use disposable plastics and improper recycling of plastic waste. As plastics do not biodegrade, they are expected to get fragmented over time into micro-plastics and nano-plastics (MNPs) due to physical, chemical and biological processes. MNPs pollution is fast becoming an environmental crisis that needs to be urgently addressed. To-date there are no suitable techniques available for the analysis of smaller size MNPs (<50 µm - <1 µm) which are expected to be more prevalent in freshwaters and are expected to cause significant impact to living organisms. Therefore, there is an urgent need for development of an analytical method that allows for easy and rapid measurement of especially smaller-size MNPs. This studentship will address this knowledge gap by developing an analytical method for quantitative analysis and chemical identification of plastic particles present in water samples using a combination of SERS and machine learning approaches. The objectives of the project are four-fold:
- Developing an analytical method for quantitative analysis and chemical identification of plastic particles present in water sample using synthesized spherical gold/silver/other metallic nanoparticles as SERS substrate and Raman spectroscopy.
- Optimization of SERS nanoparticles: find a highly reproducible, reliable and low-cost substrate (colloid) that can be used to detect micro and nanoparticles, particularly when the polymer of interest is present at very low concentrations in aqueous samples.
- Detection and identification of MNPs in field samples: To identify and overcome the major challenges of using SERS technique for analysing field samples. The developed SERS method will be integrated with both benchtop and Portable Raman spectrometers that will be used to obtain data from field collected samples.
- Examine the performance of different machine learning and statistical approaches for quantification and classification analysis of plastic particles.
The student will be hosted at the James Hutton Institute with periods of training spent at the Robert Gordon University (RGU), Aberdeen, UK. Experimental analyses will be carried out at both Hutton and RGU. The student will handle a wide range of spectroscopy analysis techniques, carry out analytical method development and statistical analyses. The scholar will be encouraged to attend relevant lectures and seminars hosted at the University and the James Hutton Institute and will be part of a larger cohort of Hydro Nation scholars across Scotland as well as other PhD students at both the James Hutton Institute and the RGU.
The student will have the intellectual freedom to steer the direction of the research and actively find solutions to research challenges. This project will suit a student with background in statistical and data mining techniques using programming language like R and python. It will provide wide-ranging training in spectroscopy, analytical laboratory techniques, wet chemistry and chemometrics and will be an excellent foundation for a future career in both academic and water industry innovation sectors.
Applicants are strongly advised to make an informal enquiry about the PhD to the primary supervisor well before the final submission deadline. Applicants must send a completed Hydro Nation Scholarship application form (available here https://www.hydronationscholars.scot/apply) with a Curriculum Vitae and covering letter to Dr. Reza K Haghi (Reza.KhodaparastHaghi@hutton.ac.uk) by the final submission deadline of 7th January 2022.