PhD Studentship - AI-Assisted Characterisation of Triboelectrification of Powders

Faculty of Engineering and Physical Sciences EPSRC Project Proposals 2025/26 (jobs.ac.uk)

Project Link: AI-Assisted Characterisation of Triboelectrification of Powders | Project Opportunities | PhD | University of Leeds

Funding: School of Chemical & Process Engineering Studentship, in support of the EPSRC Research Grant: Modelling, Validation and Application of Triboelectrification (Grant Number: EP/X023389/1), providing the award of full academic fees, together with a tax-free maintenance grant at the standard UKRI rate of £20,780 per year for 3.5 years.

Lead Supervisor’s full name & email address

Dr. Xiaodong Jia: X.Jia@leeds.ac.uk

Co-supervisor’s full name & email address

Dr. Arash Rabbani: A.Rabbani@leeds.ac.uk

Professor Motjaba Ghadiri: M.Ghadiri@leeds.ac.uk

Dr. Wei Pin Goh: W.P.Goh@leeds.ac.uk

Project summary

Triboelectrification, the process by which particles acquire an electric charge through contact and separation, is crucial in various industries, including pharmaceuticals, agriculture, and materials science. This phenomenon significantly impacts powder flow, adhesion, and segregation, affecting manufacturing efficiency and product quality. Understanding and controlling triboelectrification is essential for optimising processes and ensuring the consistency of powder-based products.

This research project aims to utilise the advanced particle characterisation capabilities at the University of Leeds to map a comprehensive array of physical, chemical and electrical properties of powders, including particle size, shape, density, surface roughness, surface resistivity, and dielectric constant. These characterisation data will serve as the foundation for training an artificial intelligence (AI) model designed to predict the tribocharging tendencies and behaviours of different powders. The AI model will analyse the influence of these properties on triboelectrification, identifying the critical factors that affect charging behaviour. By establishing a clear relationship between particle properties and triboelectrification, this research will provide valuable insights for tailoring particles with desired tribocharging characteristics. This capability has wide ranging implications to any industry that handles powders, such as pharmaceutical industry, where the triboelectrification of active pharmaceutical ingredients (APIs), food industry and additive manufacturing, as the phenomenon can adversely affect the processability, leading to challenges in manufacturing and product consistency.

The outcomes of this project will not only advance the fundamental understanding of triboelectrification but also offer practical solutions for industries reliant on powder processing. By harnessing AI to predict and control triboelectrification, we aim to enhance the efficiency and reliability of powder-based manufacturing processes, ultimately contributing to improved product quality and performance. Additionally, there is a parallel ongoing project focusing on characterising the flowability of powders, and the work from both projects is complementary. The combined insights from these studies will enhance our understanding of powder behaviour, leading to more efficient and reliable manufacturing processes.

Please state your entry requirements plus any necessary or desired background

A first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline.

Subject Area: Chemical engineering, manufacturing, computer science & IT

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