Self-Driving Laboratories for Advanced Sustainable Materials Synthesis

Self-driving laboratories (SDLs) combine the power of artificial intelligence (AI) and machine learning (ML), robotics, and automation to accelerate the process of scientific discovery. They have shown promise in chemical and materials science where they can explore vast multi-dimensional parameter spaces which would otherwise be impossible using manual approaches. Furthermore, when configured appropriately, automated experiments are more consistent and reproducible than manual experiments and can offer sustainability benefits through use of less raw materials and generating less waste. Recently, we have developed SDLs which can effectively screen a wide range of reaction conditions for generating polymer and nanoparticle-based products(see Chem Eng J 2025, 507, 160700;  Polym Chem. 2025 doi.org/10.1039/D5PY00123D; ACS Polym. Au 2025, 5, 1–9). This versatility was achieved by overcoming significant technical hurdles related to multi-phase (solid-liquid) reactions, often involving variable viscosity; and the need to monitor several different properties in near real-time. The latter involved several orthogonal online monitoring methods. Nevertheless, many challenges still exist which are often formulation dependent including a need for longer reaction times or the need to change reaction composition throughout the process (e.g. by injection of more reagents or solvents).

In this project, you will build upon our current SDL technologies with a view to enable rapid screening of polymers, nanoparticles and nanocomposites which require complex and changing reaction conditions. You will evaluate and adapt online monitoring tools to ensure rapid determination of critical quality attributes (CQAs) and subsequently evaluate experimental screening campaigns including programmed screening or Bayesian optimisation. You will characterise the resulting materials, in terms of their properties and performance for an intended application. Sustainability will be embedded throughout, with a quality by design approach facilitating targeting materials with an optimal trade-off between performance and sustainability.

The project suits an individual with a chemical engineering or chemistry background who is keen to be immersed in a research group with a vision of generating the next generation of sustainable, functional materials. Through learning how to build a reactor, configure online monitoring instrumentation, run machine-learning directed experiments, you will gain an extremely attractive multidisciplinary skill set encompassing materials synthesis, reactor design and computer programming. 

Entry requirements:

Upper second class degree in Chemical Engineering, Materials Science and Engineering or Chemistry.