Optimized Flow Pathways for Monodisperse Nanoparticles of Porous Materials

Description

Metal–organic frameworks (MOFs) are ordered nanoporous materials that can be manipulated for a wide range of applications, including gas storage, industrial separations, and catalysis. MOF research has primarily focused on bulk phases composed of polydisperse mixtures of crystallites that span multiple orders of magnitude in size. Recently, significant effort has been dedicated toward the realization of uniform MOF nanoparticles with enhanced properties, including improved kinetic performance in gas sorption, and improved packing in the formation of high-density materials for energy applications. Despite these advances, research into the control of particle size and morphology with scaling has thus far been understudied. This project will develop automated and sustainable synthesis pathways for highly porous MOF nanoparticles. This research will merge continuous synthesis pathways (Flow chemistry), process analytical technologies (PAT), data analytics, and machine learning to automate the optimisation process to ensure the continuous production of high-quality MOF nanoparticles which maintain important characteristics (i.e. particle size, morphology, and porosity) for use in energy applications. This project will lead to significant opportunities in terms of new materials, processes, and technologies for multiple industries, in particular the industry partner supporting this project.

This project will use automation and closed-loop optimisation to address a key problem in translating novel porous materials from a lab to industry: precise control over the properties of porous materials with scale. Prospective students will be comprehensively trained in three aspects: continuous flow chemistry, sensors and inline particle detection, and the production and characterisation of porous materials.

This project will develop automated processes for synthesising highly uniform MOF nanoparticles. The use of flow brings inherent scalability to optimised processes developed in this project, reducing the need for downstream re-optimisation. The incorporation of process analytical technologies (PAT) and process control tools will be inherently amenable to automation. Algorithms have been used to autonomously optimise chemical processes, e.g., well-understood two-step reactions in flow; recently, automated optimisation of nanoparticles has been realised for CdSe or noble metal NPs, but it is a significant challenge to achieve this for particles without an obvious spectroscopic handle.

This project will be supported by Baker Hughes and supervised by Prof Anna Slater (Chemistry), Prof Matthew Rosseinsky (Chemistry) and Dr Kai Hoettges (Electrical Engineering). Any informal enquiries about the project can be directed to Anna.Slater@liverpool.ac.uk.

The global need for researchers with capabilities in materials chemistry, digital intelligence and automation is intensifying because of the growing challenge posed by Net Zero and the need for high-performance materials across multiple sectors. The disruptive nature of recent advances in artificial intelligence (AI), robotics, and emerging quantum computing offers timely and exciting opportunities for PhD graduates with these skills to make a transformative impact on both R&D and society more broadly.

The University of Liverpool EPSRC Centre for Doctoral Training in Digital and Automated Materials Chemistry is therefore offering multiple studentships for students from backgrounds spanning the physical and computer sciences to start in October 2025. These students will develop core expertise in robotic, digital, chemical and physical thinking, which they will apply in their domain-specific research in materials design, discovery and processing. By working with each other and benefiting from a tailored training programme they will become both leaders and fully participating team players, aware of the best practices in inclusive and diverse R&D environments.

This training is based on our decade-long development of shared language and student supervision between the physical, engineering and computer sciences, and takes place in the Materials Innovation Factory (MIF), the largest industry-academia colocation in UK physical science. The training content has been co-developed with 35 industrial partners and is designed to generate flexible, employable, enterprising researchers who can communicate across domains.

Applicant Eligibility

Candidates will have, or be due to obtain, a Master’s Degree or equivalent related to Physical Science, Engineering or Computational Science. Exceptional candidates with a First Class Bachelor’s Degree in an appropriate field will also be considered.

Application Process

Applicants are advised to apply as soon as possible no later than 17^th February 2025. The CDT will hold two rounds of applications assessment:

• Assessment Round 1: for all applications received between 11^th December 2024 – 15^th January 2025.
• Assessment Round 2: for all applications received between 16^th January 2025 – 17^th February 2025

Applicants who wish to be considered in Assessment Round 1 must apply by 15^th January 2025. Projects will be closed when suitable candidate has been identified (this could be before the 17th February 2025 deadline).

Please review our guide on “How to Apply” carefully and complete the online postgraduate research application form to apply for this PhD project in Chemistry.

We strongly encourage applicants to get in touch with the supervisory team to get a better idea of the project.

We want all our Staff and Students to feel that Liverpool is an inclusive and welcoming environment that actively celebrates and encourages diversity. We are committed to working with students to make all reasonable project adaptations including supporting those with caring responsibilities, disabilities or other personal circumstances. For example, if you have a disability you may be entitled to a Disabled Students Allowance on top of your studentship to help cover the costs of any additional support that a person studying for a doctorate might need as a result.

Please ensure you include the project title and reference number CCPR143 when applying.