About the project
This project will develop an integrated, automated platform for rapid synthesis of isotope-labelled compounds for defence-relevant analytical and toxicological applications. Combining synthetic chemistry, automation, analytical measurement and digital optimisation, it will investigate closed-loop isotope labelling for producing analytical reference standards and compounds supporting metabolism, toxicokinetic and chemical-threat studies.
Isotope-labelled compounds are essential tools for chemical analysis, enabling the accurate identification and quantification of chemicals and their transformation products. However, their synthesis can be time-consuming and often requires considerable manual optimisation. This project will address this challenge by developing an integrated, automated platform for the rapid synthesis of isotope-labelled compounds for defence-relevant analytical and toxicological applications.
The project will combine synthetic chemistry, laboratory automation, analytical measurement and digital optimisation to investigate closed-loop approaches to isotope labelling.
Automated experimentation will enable reaction conditions to be rapidly explored and optimised, while simultaneously generating large, structured datasets of isotope-labelling reactions. These data will be used to develop predictive approaches that relate reaction conditions and molecular structure to labelling outcomes, ultimately enabling the planning of synthetic pathways to isotope-labelled compounds. Target compounds will include analytical reference standards and molecules supporting studies of metabolism, toxicokinetics and chemical threats.
A distinctive feature of the project is its interdisciplinary nature. You will receive training spanning organic synthesis, isotope chemistry, automation, analytical chemistry, large-scale chemical datasets, predictive modelling and 3D printing, developing an unusually broad combination of practical laboratory and digital skills.
The datasets generated will also contribute to the EPSRC-funded Physical Sciences Data Infrastructure (PSDI) data collection, allowing high-quality experimental data to be reused and integrated into other researchers' workflows.
By connecting automated experimentation, predictive modelling and data sharing, the project aims to establish a more efficient and data-driven approach to designing and producing isotope-labelled molecules.
