About the Project Context Reverse osmosis (RO) holds significant promise in alleviating global water scarcity through seawater desalination.
But shortages of increasing frequency and intensity now demand treatment of unconventional waters beyond seawater, such as wastewater, to expand supply. RO is also increasingly deployed to treat industrial streams in microelectronics, pharmaceutical, and food & drink manufacturing, which must meet stringent water quality standards. In all cases, RO polyamide (PA) membranes – originally designed to reject charged (ionic) species in seawater – are increasingly challenged by small charge-neutral contaminants (SNC) that pass through largely unrejected. SNC include toxic disinfection by-products (e.g., nitrosamines) and organic pollutants (trihalomethanes, phenols, dioxanes, alcohols, ketones); their removal requires additional energy-intensive treatment stages. Answering the following two long-unresolved research questions (RQ) is key to shifting from a trial-and-error approach to rational membrane design, and achieving transformative improvements in SNC rejection: RQ1. What are the molecular-level mechanisms governing sorption and transport in RO membranes? RQ2. How can insights into sorption and transport be leveraged to efficiently search a vast library of possible membrane materials – the chemical design space – and discover highly selective, water-permeable membranes? Objectives We will tackle RQ1-2 through simulation and experiment to achieve our Overall Objective: to computationally discover new membrane materials for SNC removal by harnessing molecular dynamics (MD) simulation and machine learning (ML). The project is structured along the following two Works Streams (WS) addressing RQ1-2. In WS1, MD simulation will elucidate molecular-level sorption and transport mechanisms, providing insights into the optimal membrane properties – pore sizes, interfacial chemistry – that boost selectivity. In WS2, a vast chemical design space formulated with insights from WS1 will be explored using a ML technique enabling computationally-efficient materials discovery. Research and Training The successful applicant will conduct research in the School of Engineering at the University of Edinburgh, under the supervision of Dr Santiago Romero-Vargas Castrillón. The student will have access to a wide range of computational facilities. Educational and research opportunities afforded by this project include: training in state-of-the-art molecular simulation techniques close mentoring through regular meetings, as well as interactions with other investigators at the Institute of Multiscale Thermofluids (IMT) and the Institute for Infrastructure and Environment (IIE) at Edinburgh the opportunity to attend national and international scientific conferences to disseminate your results strong emphasis and support to publish research results in leading scientific journals, which will kickstart your career in academia or industry. Eligibility This is a challenging and scientifically ambitious project, requiring a student who is dedicated and enthusiastic about asking, and tackling, fundamental questions. The successful applicant will have been awarded an undergraduate degree at the time of appointment (2:1 or above, preferably supported by an MSc) in chemical engineering, mechanical engineering, chemistry, physics, materials science, or a cognate field. A strong background in mathematics and physics is required, as well as interest in molecular simulation. Prior research experience in modelling and simulation is highly desirable. Funding Notes We applications from qualified self-funded students. Qualified UK applicants (or those with EU settled status) may be supported to apply for highly competitive School of Engineering studentships.
