Chemical separations have long been essential to human society. However, purifying component from mixtures is often complex, costly, and energy-intensive. In the group, we look for innovative ways to solve chemical separation challenges with the help of artificial intelligence, robotics experimentation and modelling tools, to accelerate the development of separation processes. We primarily focus on liquid-phase separation (e.g. solvent extraction), with various applications from the start (e.g. feedstock transition), to the end of the chemical value chain (e.g. end-of-life recycling). This ranges from understanding fundamental transport phenomena, designing novel extraction systems, to developing process models for in silico prediction of separation performances at large scale.

With the help of digital tools, we aim at:

1. Understanding the complexity of separation science
2. Accelerating separation process design and development
3. Reducing cost and environmental impact from in silico testing and optimisation

1. Liquid-liquid fundamentals

Understanding liquid-liquid fundamental phenomena is key to the success of solvent extraction processes. With the advanced imaging techniques and computer vision, we look at the interactions of the droplets and the molecules transport at the liquid-liquid interface, and extract useful information to better understand the underlying mechanisms of the separation processes.

2. Autonomous experimental platform for separations

Identifying the key physicochemical properties is essential to screen extraction systems and design new separation process. However, getting data is not straightforward and this can be very labour-intensive with repeated experiments. By leveraging robotics and multiple types of sensors, we design experimental workflows to automate liquid handling and measurements of liquid-liquid systems. Meanwhile, we also design “closed-loop” platforms for autonomous optimisation of separation processes, to identify the optimal experimental conditions for later scale-up.

3. AI-assisted separation process development

Moving from lab to manufacturing is never easy. We develop mechanistic models based on first principles to describe separation process. This allows us to understand the complexity of separation systems, as well as to create a “digital twin” of the separation process to support model-based process optimisation or environmental and techno-economic assessments. We design AI agents to automate the workflows. This includes model knowledge representation, automated model assembly as well as process design.

4. Novel separation systems

New separations technologies are on the horizon, this usually requires novel design of advanced materials (e.g. ligands, green solvents), use of new driving forces (e.g. electric, microwave, light), as well as designing new separation devices. With AI-guided algorithms (such as Bayesian optimisation), we are able to navigate our search in a large design space to identify green solvents, or to find the best experimental conditions to meet multiple objectives in a short time.

• 01/2025 - 09/2025, Senior Research Associate (now Research Assistant Professor), Department of Chemical Engineering, University of Cambridge, UK
• 08/2022 - 01/2025, Research Associate, Department of Chemical Engineering, University of Cambridge, UK
• 08/2017 - 06/2022, PhD, Department of Chemical Engineering, Tsinghua University, China
• 06/2019 - 06/2022, Joint PhD, Department of Chemical Engineering, University of Melbourne, Australia

We are actively recruting! We always welcome motivated talents with diverse backgrounds in such as Chemical Engineering, Chemistry, Computer Science or other related fields to join our team!