NMR analysis of illicit drugs
Illicit drug use is a major cause of harm, particularly for young New Zealanders. In many cases, harm is caused because users inadvertently consume a different substance to that intended, or the dosage is different to that expected. Existing drug checking techniques can provide identification of the substances present, but cannot simultaneously provide quantification of the amount of substances present. This information is key intelligence required to mitigate the risks of illicit substances. This project seeks to develop automated analysis of illicit substances using Nuclear Magnetic Resonance (NMR) spectroscopy.
NMR is a powerful analytical technique that provides both identification and quantification in a single measurement. The project is a collaboration between the University of Canterbury (UC), Victoria University of Wellington (VUW), and ESR. UC has developed a new analytical tool that uses quantum mechanical (QM) modelling of the NMR signature to enable accurate quantification of mixtures of substances using affordable benchtop NMR instruments. VUW provides expertise in machine learning for NMR applications. While ESR provides primary analytical chemistry and forensic services for the analysis of illicit drugs in New Zealand. This project will combine the expertise from all three of these research partners in order to develop machine learning techniques to enable automated analysis of compounds in the spectral database.
Supervisors
Supervisor: Daniel Holland
Key qualifications and skills
A first class or high second class Masters/Honours degree in chemistry, data science, deep learning, or chemical engineering. Knowledge of analytical chemistry, nuclear magnetic resonance (NMR), and Python are advantageous. However, we recognise it is unlikely any single applicant will have knowledge of all of the above, and we will provide training as required.
Does the project come with funding
Yes – the project includes a stipend of $35000 per year + fees.
Final date for receiving applications
31 March 2024 – though applications will be assessed on submission.
Keywords
analytical chemistry; NMR spectroscopy; chemical analysis; data science; deep learning; machine learning
