About the Project
Project Supervisors: Professor Chris-Kriton Skylaris and Professor Jonathan W. Essex
Biosensors could enable low-burden detection of energetic compounds, but challenges arise because many explosives exhibit poor solubility or complex behaviour in water. This project will develop a validated computational pipeline to identify optimal ionic liquid or deep-eutectic solvents for target compounds, enabling improved biosensing and rational solvent design for future detection technologies.
Explosives detection is critical for defence and security, yet current physicochemical sensors are often bulky and power-hungry. Biological systems, by contrast, offer exceptional sensitivity and adaptability with far lower size, weight, and power demands. Unlocking this potential requires overcoming a major challenge: enabling biological sensing elements to interact with energetic compounds that are poorly soluble in water and chemically complex.This project will tackle that challenge by developing a computational pipeline to identify optimal ionic liquids (ILs) and deep eutectic solvents (DESs) for dissolving and stabilising explosives.
Using advanced molecular modelling, machine learning, and large-scale quantum chemistry, the project will predict solubility and stability, validate models against experimental data, and ultimately enable rational design of novel solvents tailored for biosensing applications. The research is highly interdisciplinary, combining chemistry, data science, and defence technology. It includes close collaboration with an industrial partner, offering unique access to defence expertise and experimental validation, as well as opportunities for placements and networking. In addition to the main supervisors from the School of Chemistry and Chemical Engineering, the project will also be co-supervised by an industrial collaborator.
You'll receive comprehensive interdisciplinary training at the interface of computational chemistry, materials science, and biosensing. You'll gain expertise in molecular simulation methods, including atomistic modelling, free-energy calculations, and machine-learning approaches for predicting solubility and stability in complex solvent environments. Your training will also cover high-performance computing, data analysis, and the development of computational workflows suitable for deployment as predictive design tools.
In addition, you'll work closely with experimental researchers developing biosensing technologies, providing valuable exposure to the practical challenges of energetic compound detection and validation of computational predictions. This combination of advanced computational training, cross-disciplinary collaboration, and transferable skills in programming, scientific communication, and project management will equip you for careers in both academia and industry.
Entry requirements
You must have a UK 2:1 honours degree, or its international equivalent, in one of the following:
- chemistry
- physics
- materials science
- a related field
Experience with DFT calculations or molecular dynamics simulations is desirable.
This project is open to UK nationals only.
Fees and funding
For UK students, tuition fees will be paid and you'll receive a tax-free living stipend.
This project is open to UK nationals only.
How to apply
You need to:
- choose programme type (Research), 2026/27, Faculty of Engineering and Physical Sciences
- select Full time or Part time
- search for programme PhD Chemistry (7189)
- add name of the supervisor in section 2 of the application
Applications should include:
- your CV (resumé)
- 2 academic references
- degree transcripts and certificates to date
- English language qualification (if applicable)
Contact us
Faculty of Engineering and Physical Sciences: feps-pgr-apply@soton.ac.uk
Project leader: C.Skylaris@soton.ac.uk
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